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Development of rapeseed cleaning loss monitoring system and experiments in a combine harvester
b i o s y s t e m s e n g i n e e r i n g 1 7 8 ( 2 0 1 9 ) 1 1 8 e1 3 0
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Research Paper
Development of rapeseed cleaning loss monitoring system and experiments in a combine harvester Lizhang Xu, Chuncai Wei, Zhenwei Liang*, Xiaoyu Chai, Yaoming Li, Qi Liu Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University, Zhenjiang Jiangsu, 212013, China
article info
To monitor rapeseed loss in real-time during the harvesting process, a rapeseed loss
Article history:
monitoring system based on the signal analysis of impacts, was developed. Firstly, to
Received 12 August 2018
understand grain and MOG (material other than grain) collision signal characteristic,
Received in revised form
models for different threshed outputs components were established in the EDEM software,
1 November 2018
and the corresponding collision signal characteristic for different components were ob-
Accepted 1 November 2018
tained, thereby laying the foundation for the design of a signal processing circuit. Secondly, using a P5-3B type piezoelectric ceramic as a sensitive element, a multi-block cleaning loss sensor and a signal processing circuit with amplification, filtering, rectification, compari-
Keywords:
son functions and early warning functions were developed. Finally, performance calibra-
Rapeseed
tion tests with different grain flow rates, different rapeseed varieties and mixture were
Sieve loss
carried out in the laboratory. Calibration and field testing showed that the detection ac-
Monitoring
curacy of the developed rapeseed loss monitoring sensor was high and the overall per-
Sensor
formance was acceptable.
Field experiment
1.
Introduction
Cleaning losses are one of the main types loss that occur in rapeseed machine harvesting, and the process of cleaning is also one of the main bottlenecks restricting the development of mechanised rapeseed harvesting in China. At present, rapeseed cleaning is mainly adjusted manually (Xu, 2008), with the main disadvantage being that the measurement lags and has a large error. The method cannot provide accurate current grain loss levels for the operator to adjust relevant working parameters in time and this causes a direct loss of income for the famers. Much research has been carried out to
* Corresponding author. E-mail address: [email protected] (Z. Liang). https://doi.org/10.1016/j.biosystemseng.2018.11.001 1537-5110/© 2018 IAgrE. Published by Elsevier Ltd. All rights reserved.
© 2018 IAgrE. Published by Elsevier Ltd. All rights reserved.
monitor grain sieve losses in combines (Ing et al., 1976; Maertens.,2004; Zhao, Li, Liang, & Chen, 2012). As early as 1977 Liu et al. (1993), proposed using electroacoustic principle to monitor grain loss in real-time. A high sensitive microphone was used to pick up the collision sound signals generated as wheat grain and stem collided with the sensitive plates; the full grain collision signal was filtered out by the signal processing circuit and then the grain loss was calculated. However, it was found that the accuracy of the sensor was poor due to the interference of mechanical vibration and internal noise in practise. Strubbe et al. (1991), selected plates with a resonant frequency of 8e25 kHz as the sensitive plate, and experiment results indicated that the
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Nomenclature a q e E* E1 E2 QT u Fn Fn max Ft Fdn Fdt g G* G1 G2 amax tr ti R1 k I m m* n QL q sm U v vn vt vrel n vt rel dn dt a n1 n2 R* R2
Contact radii, m Grain impact angle, (0) Coefficient of restitution Effective Young's modulus, MPa Young's modulus of grain, MPa Young's modulus of sensor, MPa Theoretical monitoring quality Rotational velocity, rad s1 Normal contact force, N Maximum normal impact force, N Tangential contact force, N Normal damping force, N Tangential damping force, N Gravitational acceleration, m s2 Effective shear modulus, MPa Shear modulus of seed, MPa Shear modulus of sensor, MPa Maximum normal overlap, m Rise time, s Collision time, s Radius of the grain Proportional coefficient Moment of inertia, kg.m2 Mass of grain, g Equivalent mass, g Contact normal vector Measured cleaning loss One grain mass, g Number of monitored grains Unit vector of rotating shaft Vector of particle velocity m s1 Normal velocity, m s1 Tangential velocity, m s1 Normal relative velocity, m s1 Tangential relative velocity, m s1 Normal overlaps, m Tangential overlaps, m Normal overlap, m Poisson's ratio of material Poisson's ratio of plate Effective radius Radius of the sensitive plate
grain collision frequency is 15 kHz, which improved grain signal response and the monitoring accuracy improved significantly. Craessaerts. et al., 2010 used pressure sensors to measure the pressure difference in different parts of the vibrating sieve. By using the fuzzy control theory, a nonlinear model for predicting grain sieve was established, and the best position for the cleaning loss monitoring sensor in combine harvester was indicated. Deniz and Huseyin Caner (2015), measured cereal losses (chickpea) with PW4C3-300 pressure sensors. By analysing material collision response, grain loss was identified and calculated. The effectiveness of the sensor was verified by field experiments. Agricultural machinery companies have also conducted in-depth research on
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cleaning loss monitoring technology. Some commercial grain loss sensors, such as the MK4 type grain loss monitor developed by RDS Technology Ltd. Minchinhampton, Stroud, Gloucestershire, UK, the LH756 type loss monitor by Teejet company, Wheaton Facility, IL,USA), and the grain loss sensor designed by the Australian KEE group (Langar Way, Landsdale, Perth WA, Australia) has become one of an indispensable accessory for farmers. The Ferguson 860 combine harvester made by British Ferguson company, the Case 2366IH type combine harvester by Keyes Co, the JD9660STS type harvester by the United States John Deere Company and the Lexion 760 harvester by the Class company (Eldredge, 1985) also installed grain loss sensors. In Europe and the USA, research on cleaning loss monitoring sensors has been carried out for around 30 years and the relevant technology is mature. However, in recent years, some scientists from China have carried out some research on grain loss monitoring by using PVDF, multi contact induction film and piezoceramic as sensitive elements. Li (2006) and Li and Jie (2007), proposed using PVDF with its high piezoelectric constant and wide frequency response to construct a sensitive plate to monitor grain loss. Zhou, Zhu, Zhou, and Tang (2010) used a multi contact induction film to monitor the grain signal. When the grain impacted the film, the induction film formed a closed loop and a pulse signal was obtained. However, the design cost is high and the mechanism has not been applied in a harvest. Liang, Li, Zhao and Chen (2013) used modal analysis with different thicknesses and different boundaries by using ANSYS software with to predict the performance of sensitive plates of 304 stainless steel, T6 aluminium, and brass. Grain collision tests were carried out to study the relationship between the sensitive plate vibration characteristics and detecting performance by using YT-5L piezoelectric ceramic as sensitive element. The material and structure of the sensitive plate was selected. Li et al., (2013) designed a grain loss monitoring system, in which the symmetry sensors was used to eliminate machine vibration interference. The stimulating signals were processed by charge amplifier band-pass and anti-aliasing filter, and the signals of cleaning losses were then extracted effectively. The single-chip computer unit was used for monitoring and counting in real-time. The results of laboratory test and field test showed that the designed monitoring system can effectively monitor combined harvesters cleaning losses, and the maximum measurement error was 2.81%, which met the practical monitoring requirements. From the above it can be seen that researchers in China are focussing on the monitoring of rice, wheat and soybeans loss during harvest. However, rapeseed is an important crops in China for the production of edible oil. Currently, there is no literature on monitoring rapeseed sieve loss in a combine harvester. Field results have indicated that it is not effective to monitor rapeseed sieve losses using sensors developed for rice and wheat. Therefore, it is necessary to design a sensor to specifically monitor rapeseed loss according to the properties of rapeseed. Firstly, on the basis of studying the characteristics of rapeseed cleaning and ejecting materials, discrete element software was used to simulate the collision mechanics process of rapeseeds against monitoring plates with different compositions and different moisture content, and
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the parameters of signal processing circuits were determined by experiments. Then, a multi-block cleaning loss monitoring sensor was developed. Finally, performance calibration tests with different grain flow rates, different rapeseed varieties and mixture were carried out in the laboratory, and a field experiment was carried out.
2.
rapeseed loss monitoring sensor with a monitoring accuracy of 300e1000 is required. From the above analysis, it can be concluded that the requirements for a rapeseed loss sensor are as follows: (1) Due to the large number of loss grains per unit time, the developed sensor needs to meet the overall detection frequency of 1000 grains s1. (2) The sensor should have a good monitoring performance for rapeseed with multiple varieties and different maturity (moisture content 8%e30%).
Experimental section
2.1. Difficulties and technical requirements for rapeseed cleaning loss monitoring There are a number of rapeseed varieties in China, the typical varieties are designated mustard, cabbage and Chinesecabbage types. The differences among the varieties are significant: (1) Mustard-type rapeseed has a thousand-grain mass of 1.5e2.5 g where as the thousand-grain mass for cabbage-type is 3e4 g, and thousand-grain mass for Chinese-cabbage-type can be up to 6e7 g. The mass of a single rapeseed is only 1/7 of rice grain, and only 1/5 in volume. Small rapeseeds are mixed in large number of pods and other debris which increase sensor identification difficult more greatly. (2) The rapeseed plant is a disorderly flowering plant and grain maturity varies greatly over the harvesting season. Even in the same field, the harvest may consist of green kernels, brown kernels and black kernels and moisture content has a large range (8%e 32%), which makes the detection and identification of the oilseed kernel difficult. (3) Compared with rice and wheat, rapeseed has a larger grain/straw ratio (2.5e5), and the components discharged from the cleaning shoe are complicated. According to test bench results analysis, horn shells account for about 60% in mass, straws account for about 30%, impurities account for 6%, and full grains are only 4% (Xu, 2008); images of the physical components within the discharged materials as shown in Fig. 1. Those features place high requirements on the adaptability of the sieve loss sensor. The existing accuracy of a typical rice cleaning loss sensor in China is about 150e200 grains s1, which can meet the requirements for monitoring of rice cleaning, but existing sensors are not applicable to the rapeseed since there are large amounts of grain lost in the grain loss sensor installation position. Generally speaking, a
2.2.
Overall design of the rapeseed monitoring system
Although the effects of sensitive plate material, dimensions, and damping ratio on the effect of grain signal attenuation have been studied, and detection frequency has been improved for whole-plate cleaning loss sensors. However, they are still unable to meet the rapeseed loss monitoring requirements specified above. Based on our previous experience on designing of rice grain loss sensor (Liang et al., 2015 & 2016), an integrated multi-block cleaning loss monitoring structure is proposed: four monitoring units are arranged in the lateral direction of tail sieve, and the monitored data from the four units is combined to estimate the overall rapeseed grain loss ratio, thus ensuring the required monitoring accuracy can be guaranteed. The monitoring systems composed an integrated multi-block sensor, a signal processing circuit and a host computer. The signal processing circuit mainly includes amplification circuits, band pass filtering circuit, full wave rectification circuit, and envelope comparison circuit. The host computer was developed on the basis of FPGA basic board (Xilinx, San Jose, CA, USA). The structure of the monitoring system is shown in Fig. 2. When the monitoring device was in operation, a charge signal was generated by mechanical vibration during the grain collision process. The charge signal was then transmitted to the signal processing circuit via the RG174 signal line. The signal processing circuit composed of a charge amplifying circuit, a band pass filter, a precision full-wave rectification, an envelope detector, and voltage comparator. The standard pulse signal modulated by the signal processing was input to the host computer through an external interrupt. The realtime cleaning loss was calculated based on the built-in mathematical model and displayed. As the rapeseed properties vary greatly in different fields and at different harvest times, in order to reflect the current grain loss level, the parameters of the cleaning loss monitoring model, such as the
Fig. 1 e Physical diagram of components within the discharged materials.
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Fig. 2 e Monitoring system of rapeseed cleaning loss.
rapeseed grains one-thousand mass need to be adjusted according to the situation in the field.
2.3.
Structure of the sensor
The integrated multi-block sieve loss sensor, shown in Fig. 3, one monitoring unit was mainly composed of a sensitive plate, a vibration isolating rubber and a support plate. The support plate was 1050 mm in length and 90 mm in width. Four sensitive plates with a size 210 mm 120 mm 0.5 mm (length width thickness) were arranged laterally above the support plate. The gaps between the sensitive plates were 2 mm. Piezoelectric ceramics have large piezoelectric constant, high sensitivity, mature technology and low cost, and can respond to a small deformation of 0.001 mm. It is therefore a suitable material for detecting dynamic changes in small grain collisions. In this paper, a P5-3B piezoelectric ceramic (20 10 1 mm (length width thickness)) with a piezoelectric coefficient of 580 PC N1 was attached to the centre of the sensitive plate, and sealed with a silicone glue to construct to a monitoring unit (Liang et al., 2015). From modal analysis to the sensitive plate shown in Fig. 4 it was seen that the sensitive plate has large deformation in the centre, thus if the sensitive element was pasted in the centre of the plate, overall sensitively could be guaranteed. The sensitive plate was connected to the lateral connecting plate by a VD type vibration isolating rubber (Kailash, 2017). The supporting plate
connected with connecting plate though bolts. The amount angle of the sensor could be adjusted in the range of 0ºe90 by changing the position of the rubber supports. The material of the sensitive plate directly affects the signal attenuation and collision signal amplitude. Generally speaking, the thicker the sensitive plate, the smaller the relative deformation of the monitoring board, and the less the charge it produces. As the deformation rate of the monitoring plate was small when the rapeseed grains collide the plate, in order to guarantee the overall sensitivity of the sensor, a stainless steel 304 plate with a thickness of 0.5 mm was selected as the sensitive plate.
2.4. Dynamic characteristic of material collision process with plate Since rapeseeds and its MOG have different Young's moduli, coefficient of restitution for the sensitive plate and mass, the maximum normal overlap amax and the collision time ti displayed significant changes according to conservation of energy principle, where amax and ti can be deduced by the following equations: 2=5 15 mv2n amax z * *1=2 16 E R
(1)
ti z2:94amax =vn
(2) *
*
where, vn is vertical collision velocity, E , R is given by: E* ¼
Fig. 3 e Structure diagram of assembled cleaning loss sensor.
1 1 y21 1 y22 þ E1 E2
Fig. 4 e Modal analysis to the sensitive plate.
(3)
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1 1 R ¼ þ R1 R2 *
(4)
where v1 and v2 are the Poisson's ratio of the two materials; R1 and R2 are the radius of the two elements, E1, E2 are the Young's modulus of elasticity. Due to piezoelectric properties, and the polarization direction of piezoelectric ceramic, the value of collision output voltage was proportional to the maximum collision force Fn max. The value of force was proportional to the maximum normal overlap amax, which meant that the larger amax in sensitive plates, the larger Fn max. From Eq. (2) it can be seen that there was also great difference in rise time with different values of amax. Variations in normal collision forces indicate that rise time tr was equal to a quarter of the signal period. Differences in Fn max and tr produced corresponding differences in signal frequency and voltage amplitude, thus understanding Fn max and tr was a critical step in discriminating grains (Liang. et al., 2016). The discrete element method (DEM) is a numerical technique to model the motion of an assembly of particles which interact with each other through collisions. At each time step, the trajectory of each particle in a system can be obtained using a numerical time integration scheme and all forces acting on the particles like contact forces and body forces are summed which can be described by Newton's second law of motion. In the inertial coordinate, the governing equations for the acceleration of the particle centre and particle angular can be written as: dv ¼ Fn þ Ft þ mg m dt I
du ¼ R$U ðFn þ Ft Þ dt
(5)
(6)
where m is the particle mass, v and u are the translational and rotational velocities of the particle, Fn and Ft are the normal and the tangential impact forces, R and U are the rotating radius and the unit vector of rotating shaft, g is the gravitational acceleration, I is the moment of inertia. Therefore, the translational velocity vt of the contact point of the plate can be deduced: vt ¼ v þ R$U u
2.4.1.
(7)
Normal contact model
The contact model, which without doubt plays an important role in numerical simulations, is of paramount importance to DEM. In this paper the HertzeMindlin contact model which can be considered as a ‘spring-dashpot’ configuration was used to calculate the force changing process of the grain. During the elastic mode of loading for two contact spheres, the contact is treated as elastic and was governed by the Hertz formula, the normal contact force Fn can be expressed by: 4 Fn ¼ E* R*1=2 d3=2 n 3
(8)
where, dn is the normal overlaps, E*, R*is given by Eqs. (3) and (4).
2.4.2.
Tangential contact model
The theory of Mindlin was used for the elastic frictional contact between two spheres in the tangential direction. Let Ft ðnÞ and Ft ðnþ1Þ be the tangential contact forces before and after an increment of tangential displacement Dd, respectively. The relationship between Ft ðnÞ and Ft ðnþ1Þ is given by the following incremental formula: Where KT is computed according to Mindlin and Deresiewicz (1953), and the effective shear modulus G*can be calculated by: G* ¼
2 y1 2 y2 þ G1 G2
(9)
where, G1 and G2 are the shear modulus of the two materials. Additionally, there are normal damping force Fdn and tangential damping force Fdt which can be written as: Fdn
rffiffiffi 5 pffiffiffiffiffiffiffiffiffiffiffi* rel b Sn m vn ¼ 2 6 rffiffiffi 5 pffiffiffiffiffiffiffiffiffiffiffi* rel b St m vt 6
Fdt ¼ 2
(10)
(11)
rel where, vrel n is the normal relative velocity, vt is the tangential relative velocity, Sn, st , b can be deduced as:
pffiffiffiffiffiffiffiffi Sn ¼ 2E* R* a
(12)
pffiffiffiffiffiffiffiffi St ¼ 8G* R* a
(13)
ln e b ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 ln e þ p2
(14)
where a is the contact radii, e is the coefficient of restitution.
2.5.
Collision process simulation and results analysis
Rape straw is mainly composed of an outer epidermis and cavernous body, and the elastic modulus and the density of the outer epidermis and the internal cavernous body differ from each other greatly. To make the simulation closer to the reality, the straw model was set up by the outer spherical pellet and the internal cavernous mass in the EDEM 2.7 (DEM Solutions Ltd., Edinburgh, UK). The typical diameter of the outer epidermis was set at 1 mm and the diameter of the cavernous ball 3 mm and the density inside and outside were set accordingly. The established straw model is shown in Fig. 5(b). The angular fruit shell is an inner cavity structure and its outer shell has an elongated structure. The shell was assembled from more than 1700 small balls with diameter of 0.5 mm with one end of the shell piled into the tip, and the other stacked into the form of an ellipsoid end, as shown in Fig. 5 (c). The specific properties of rape seed, straw and shell are shown in Table 1, and the contact parameters between each material and sensitive plate are shown in Table 2. Contact model is an important foundation for discrete element simulation. The HertzeMindlin sliding contact model
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used here has often been used in other agricultural material simulations (Li, WebbPandiella, & Campbell, 2003). In the geometry panel of the EDEM software, the sensitive plate model with a thickness 0.5 mm, 210 mm length and 120 mm width was established. To simulate the impact behaviour, the grain dropped from a height of 350 mm, the corresponding time step and the data saving interval were 106 s. After each simulation, collision information was obtained in the postprocessing module in the EDEM 2.7 software (Analyst module in EDEM 2.7). Collision process between material and monitoring board in EDEM shown in Fig. 6. From Fig. 6 it can be seen that the collision process can be divided into 3 phases, that is, falling, collision, and rebound. As the rapeseed has a spherical surface, the collision attitude has a slight impact on grain collision force characteristics. However, because of their shape, collision attitude has a significant impact on collision force for both straw and shell. Therefore, it is necessary to analysis the effect of the collision attitude on collision signal to lay the foundation for correctly determining the corner frequency of the filter circuit.
2.5.1.
Table 1 e The mechanical characteristic parameters of model. Material properties Density (kg m3) Poisson ratio Shear modulus (Pa)
Rapeseed Straw
Shell
880 550 300 0.25 0.45 0.35 5.2 107 4.4 106 0.8 106
Sensitive plate 7850 0.30 7.5 1010
Table 2 e The contact characteristic parameters of model. Contact Rapeseed characteristics Sensitive plate Recovery coefficient Static friction coefficient Rolling friction coefficient
StrawSensitive plate
ShellSensitive plate
0.62
0.30
0.20
0.46
0.80
0.8
0.01
0.01
0.01
Rapeseed collision results analysis
The collision force between grain and sensitive plate is shown in Fig. 8, from which it can see that the average impact force rise time is 16 ms, the corresponding vibration signal frequency is 15.6 kHz, and the peak force is 0.73 N (Fig. 7). To verify software simulation results, grain impact experiments using fresh rapeseed grains were carried out in the laboratory by mounting the instrumented piezoelectric element in the centre of the sensitive plate. In each experiment, a single rice grain was dropped from a height of 300 mm (Point A) to impact with the plate, the signals were then processed by a charge amplifier and the generated voltage signal was recorded by a storage digital oscilloscope (DS0-X 3024A, Aglient Technologies, Beijing, China) with a bandwidth of 200 MHz. Grain impact experimental system is shown in Fig. 8. Typical grain collision signal acquired by digital oscilloscope are shown in Fig. 9. In Fig. 9 grain 1 and grain 2 represent two rapeseeds with different moisture contents for the same variety. Grain 1 has a moisture content 11.33% whilst grain 2 has a moisture content of 25.02%. From Fig. 9 it can be seen that the rapeseed collision signal is a period attenuating signal, the signal rise time has a slight difference from each other. This shows that the rise time of the rapeseed grain with greater moisture content (grain 2) is larger than the signal from rapeseed grain with less moisture (grain 1). This is because rapeseed grain with more moisture (grain 2) exhibits a greater damping and consumes more collision energy than the drier rapeseed grain (grain 1).
Rapeseed has a wide range of moisture content during harvest, and the physical properties such as elastic modulus, shear modulus and mass density are closely related to moisture content. Therefore, it is necessary to study the rapeseeds collision signal characteristics with different moisture contents to increase its applicability during harvest. Generally speaking, with increasing moisture content, rapeseed becomes plump and the seed coat soften, and therefore the elastic modulus decreases. The elastic modulus and density of rapeseed under different moisture content were examined by Xu, Tang, and Cheng (2017). Here, moisture content is divided into 6 groups in the range of 10%e35% during the EDEM simulation, different parameters were set separately, and the corresponding collision force rise times under different moisture contents as shown in Fig. 10. As shown in Fig. 10, when the grain moisture content is low contact force increases rapidly and the maximum contact force is greater, the rise time is also lower; with high moisture content, the contact force rise time increases correspondingly. Especially for rapeseeds with moisture content in the range 25e30%, contact time experiences a rapid increase, which may be related to the decrease of elastic modulus and increased adhesion. The rise time of collision force is dissipated in range 14e20 ms, the collision signal frequency ranges for rapeseed with moisture content of 10%e35% are within 13e17 kHz. Because the high moisture content (30%) is not suitable for mechanical harvesting, the corner frequency of the band pass
Fig. 5 e Established model for main components within the threshed output.
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Fig. 6 e Collision process between material and sensitive plate in EDEM. method is suitable for studying the collision process of the threshed outputs with the sensitive plate.
2.5.2.
Fig. 7 e Contact force of rapeseed with-sensitive plate.
Fig. 8 e Grain impact experimental system.
2.5.3. filter was set as 14e20 kHz. However, the difference between the simulated and measured rise times with different moisture content are not significant, and this further confirmed the feasibility of using the EDEM simulation. After verifying the simulation results by measurement in the laboratory, the DEM
Short straw collision results analysis
Short straws significantly influence sensor monitoring accuracy. From Fig. 11 it can be seen that the maximum short straw collision force is about 1.2 N, and the corresponding rise time is much larger than that for the rapeseed at about 170 ms. As short straw with different lengths may collide with the sensitive plate at different attitudes during harvesting, it is necessary to investigate the influence of collision angle and the short straw length on rise time. It was assumed that the angle between length direction of the straw and the collision plane was the impact angle, marked as q. Variations of tr for short straw collisions with the sensitive plate at conditions of vn ¼ 2.5 m s1, straw length l ¼ 10e90 mm, q ¼ 0e90 are shown in Fig. 12. From Fig. 12 it can be seen that rise-time tr2 was relatively short when the impact angle q ¼ 0 or 90 , and force rise time tr increased with the increasing of straw length l when q was fixed. It was also found that force rise time tr increased rapidly in the range of 0 < q < 45 . It reached a relatively high value when q ¼ 45 , and then decreased with increasing q. Generally speaking, the rise time of the short straw was distributed in the range of 90e200 ms.
Shell collision results analysis
As the shell has a low density and low elasticity modulus, and was always short, field experiments showed that most of collision had two forms: q ¼ 0 or 90 , the corresponding collision force is shown in Fig. 13, from which it can be seen that the corresponding collision force was generally 0.25 N, but the rise time tr was much longer and >264 ms.
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Fig. 9 e Typical grain collision signal acquired by digital oscilloscope. (NB: grid in axial direction (time) is 20 ms, grid in vertical direction (amplitude) is 200 mv).
Fig. 10 e The relationship between the rising time of contact force and the moisture content.
2.6.
Design of the signal processing circuit
From above the simulation and measurement results it can be seen that there is a distinct boundary in rise time when
Fig. 11 e Contact force signal of short straw with-sensitive plate.
different materials collide with the sensitive plate. The rise time for rapeseed grain is distributed in the range of the 12e15 ms, the short straw is distributed in the range of 90e200 ms, and the rise time of the shell is generally larger than 260 ms. If the corner frequency of the pass filter was set in 14e20 kHz, thus the rapeseed grains can be discriminated correctly. Based on the simulation results, the designed signal processing circuit shown in Fig. 14. As shown in Fig. 14, the signal processing circuit included a charge amplification circuit, a band-pass filter circuit, a precision full wave rectifying circuit, envelope detection and a standard square wave generator circuit. The charge amplifier with a magnification of 10 amplified the charge signals from piezoelectric ceramic. The filter is essentially a frequency selection device. According to the different frequency bands occupied by the signal and noise, the MFB band-pass filter was
Fig. 12 e Variation of rise time tr2 during short straw collisions with sensitive plate.
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selected as the filter structure to filter out interference signal such as straw and shell, only keep grain signals. In addition, the centre frequency of band-pass can be changed by adjusting R4. Due to the significant difference in the voltage amplitude produced by grain impact sensitive plate, the grain impact signal can be identified according to the peak value of the impact signal voltage. As lost rapeseeds often have different normal velocity, rotation angle and contact angle, whether the generated voltage signal is the positive or negative is unknown. However, to detect the peak value of the impulse voltage signal accurately, a set of precision full wave rectifying circuit composed of a half -wave precision rectifying circuit and adder circuit was designed, which made all the output signal positive. The attenuation signal produced by the sensor was by contrast irregular. The envelope detection circuit was designed with a precision full wave rectifying circuit to obtain envelope curve of the grain impact signal, and as a result the subsequent counting error was reduced greatly (Liang et al., 2016). The FPGA (Xilinx, San Jose, CA, USA) basic board was chosen as the core of the host computer. The generated standard square wave signal is sent to the FPGA board through the external fracture. When the external interruption changes from high level to low, the trigger program counts the numerical value. Using the clock function, the counted value can be sent to display. There is a USB port on the side of the upper computer, which can store the monitored data in real time. The host computer also displayed the total loss by mass according to a proportional coefficient between sensor monitored value and the actual grain loss k found in the program. When the loss exceeded a predetermined value, the system will alarm the operator. The PC program is written in the Altera Quartus II 15.0 (Intel, City of Santa Clara, CA, USA). The developed host computer as shown in Fig. 15. Pre-experiment was needed to determine the proportional coefficient between sensor monitored value and actual grain loss k according to the following equation:
k¼
QL qsm
(15)
where, QL is actual cleaning loss, g; q is one grain mass, g; sm is number of monitored particles.
2.7.
Calibration experiment
To verify performance of the developed cleaning loss sensor, an indoor test was carried out on the calibration test bench (as shown in Fig. 16) under different grain flow conditions (50e300 grains s1) and different mixtures. The speed of the conveyor belt, with a dimension of 1.5 m in length and 0.5 m in width, could adjusted in the range of 0e0.5 m s1. The support frame is in the front of the conveyor belt and the sensor sensitive plate (single block) mounted on the support plate. The angle, height and rear position of the support plate can be adjusted to enable the material can impact the sensor at different speeds, heights and angles (Liang et al., 2015).
2.7.1.
Effects of grain flow rates on monitoring accuracy
The number of 50, 100, 150, 200, 250 and 300 “Zheyou 51” rapeseeds with a moisture content of 16.4% was selected as the calibration material. As shown in Fig. 16, selecting a 300 mm length interval on the conveyor belt, and spread the rapeseeds evenly over the interval. The governor was adjusted to let the conveyor belt run with a velocity of 0.3 m s1, so that all the grains can fell onto the sensor surface within 1.0 s (Liang et al., 2015). Each set of experiments was repeated five times and the averaged value used, as shown in Table 3. Examining Table 3 it can be seen that the grain drop density is relatively low when the rapeseed flow rate is in the range of 50e150 grains s1, and the interaction among grains is also small, and the monitoring accuracy high (above 95%). When the grain flow rate is 100 grains s1, the detected number of grains was larger than the measured amount of rapeseeds; this might due to double impact occurring for some large grains. When the grain flow rate is in the range 200e300 grains s1, accuracy is reduced. But the error is still 10%. In the large grain flow rate test, the counted number of grains was smaller than the measured number of grains. This is might due to the large amount of grain that collide with the sensor surface within a short interval; there is a large possibility for two or more grains impacting on the sensitive plate at the same time, and resulting in a “leakage”. In addition, it is possible that a small parts of the kernels did not fall onto the sensitive plate due to barriers between the grains (Liang et al., 2015). The allowed grain loss limit is 4% according to the Chinese standard, it is estimated that the grain flow rate is about 150e200 grains s1 at the sensor installation position.
Fig. 13 e The contact force of the shells-sensitive plate. R1
+5V
+ -5V
C2 C3
R2
+
R3 R5
GND GND
UU2
R6
+ -5V R7 GND
GND
+5V UU3
D2
R9
+5V UU4
+ -5V
R10
R13
-
GND
UU6
R15 1
2
2
5
GND R14 GND
6
C5
C6 Vout GND
Fig. 14 e Diagram of the signal processing circuit.
R16
7
555
+
D3 C4
R11
UU5
4
+5V UU1
GND
8
-
D1
1
R12 R4
3
R8
C1
GND
127
b i o s y s t e m s e n g i n e e r i n g 1 7 8 ( 2 0 1 9 ) 1 1 8 e1 3 0
Fig. 15 e The host computer of the system.
Fig. 16 e Cleaning loss sensor calibration test bench.
The above test results showed that the monitoring accuracy of rapeseed cleaning sensor was 90%, which can meet the basic requirements for monitoring field cleaning loss. In addition, the rapeseed was tested in the laboratory using three levels of moisture content and the accuracy rate was >90%.
2.7.2.
Mixture calibration test
The shell, straw and light residues have a great influence on sensor monitoring accuracy. According to the ratio of each component within the discharged mixture, seven different calibration mixture were prepared. To test the ability of the designed signal processing circuit the mixture was spread evenly over the conveyor belt within 300 mm, and the drop height is set to 300 mm. Each test is repeated 3 times, and the averaged value recorded. The results are shown in Table 4. It can be seen from Table 4 that the straw and shell signals were almost completely filtered out, and they has almost no influence on sensor monitoring accuracy. It can also be seen from the results of tests 4, 5 and 6 that a large amount of mixture material can have large influence on identifying grains. This is mainly due to grains being unable to contact with the sensitive plate and the shell or residue shielding the grains. In test 7, when a part (~60%) of the straw and the shell were shielded by the combing, the interference factor was greatly reduced, and the precision was improved (Liang et al., 2015).
2.7.3.
Calibration test of different rapeseed varieties
There are many varieties of rapeseed in China, and the differences among varieties in grain size and one thousand-grain
mass are significant. To test the adaptability of the designed sensor to different varieties of rapeseed, a calibration test was carried out in the laboratory using a grain flow of 150 grains s1. The rapeseed varieties are: Zheyou 51, Qinyou No. 11, Qingyou No. 1, Oyster Sauce 737, Nanyou 868. The abovementioned varieties were obtained in the harvest season. The experiment results are shown in Table 5, from which it can be seen that the designed sensor has a good monitoring accuracy of 94% for the 5 varieties, and therefore has a good applicability to rapeseed of different varieties and regions.
3.
Field experiment
A field experiment was carried out in Huafeng Farm, Yancheng City, Jiangsu Province, May 2018. The developed sensor location in TH988 type longitudinal flow full feed combine harvester as shown Fig. 17. The harvested rapeseed variety is the “Zhejiang
Table 3 e Accuracy of cleaning loss sensor under different grain flow rates. No.
1 2 3 4 5 6
Flow (grain s1)
Average counted (grains)
Monitoring accuracy (%)
Standard deviation
50 100 150 200 250 300
48.8 103 142.5 185 228 271.8
97.6 97.0 95.0 92.5 91.2 90.6
0.95 1.05 1.26 1.83 2.04 3.11
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51”. Its basic properties were: average plant height 1206 mm, fruit pod height 640 mm, coronal diameter 335 mm, yield 2895 kg ha1, grain moisture content 10.7%, one thousand seeds mass is 3.422 g. Before the test, the combine harvester was started and allowed to achieve normal working state. The threshold voltage of the sensor was adjusted until the monitored values in the display instrument were stable, which indicated that the sensor was working normally (Liang et al., 2015). The harvesting distance was 20 m and the proportional coefficient k ¼ 3.51 was determined by repeat the pre-experiment three times. The full test consisted of 6 groups and was carried out according to pre-test protocol. The results of six groups of main test results are shown in Table 6. Each data is defined as follows: Qm ¼ sm q
p¼
(17)
where, p is detection error, %. It can be seen from Table 6 that the sensor can provide good monitoring accuracy with the pre-test determining proportional coefficient k. The averaged monitoring error was 5.8%, which satisfies the expected target value (error < 10%). Although the monitored grains can be in the range 150e230 grains s1 during a period with larger grain losses, and the overall monitoring accuracy was 10%, which proved that the integrated multi-block sensor has a good adaptability, and the monitoring resolution of the sensor is significantly improved compared to using the rice cleaning loss sensor. The experiment also found that increasing the combing teeth will reduce the interference of the straw and the shell on the sensitive plate. In addition, the angle of sensor installation angel is critical important to get a better monitoring accuracy.
(16)
QT ¼ kQm
ðQT QL Þ 100% QL
where, q is one grain mass, q ¼ 3.422/1000 g; sm is monitored grain number; Qm is actual test quality, g; k is scale factor, k ¼ 3.51; QT is theoretical monitoring quality, g.
Table 4 e Different mixture calibration test results. No. 1 2 3 4 5 6 7
Calibration material
Monitor grain volume/g
Monitoring accuracy/%
Standard deviation
Shell (30 g) Straw (20 g) Shell (30 g) þ straw (20 g) þ residue (2 g) Rapeseed (150 grain) þ shell (30 g) Rapeseed (150 grains) þ straw (20 g) Rapeseed (150 grains) þ shell (30 g) þ straw (15 grains) þ residue (2 g) Rapeseed (150 grains) þ shell (6 g) þ straw (4 g) þ residue (1 g)
0.7 0.33 1 138 139.7 137 141.3
e e e 92.0 93.1 91.3 94.2
e e e 1.03 1.16 1.24 1.40
Table 5 e Calibration results of different varieties of rapeseed. Varieties Zheyou51 Qinyou11 Qingyou1 Nanyou868 Fengyou737
Thousand mass(g)
Moisture content (%)
Planting area
Monitoring accuracy (%)
4.01 3.95 4.2 3.54 3.87
13.9 16.7 15.6 20.3 19.4
Zhejiang area Jiangsu, Zhejiang and Shanghai area Chongqing area Sichuan area Anhui, Huaihe area
96.2 94.2 95.4 94.0 95.6
Fig. 17 e Sensor installation location and field test.
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Table 6 e Field trial results of rapeseed cleaning loss. Group
Detection time t, s
Detection of the number of particles sm, particles
Actual quality of detection Qm, g
1 2 3 4 5 6 Average
49 48 43 40 36 32 41
5035 5225 4175 4923 2455 2459 4045
17.22 17.88 14.29 18.85 8.40 8.41 14.18
Theoretical test Actual cleaning loss Detection quality QT, g quality QL, g error p (%) 60.44 62.76 50.16 66.16 29.48 29.52 49.75
61.60 66.88 48.72 57.76 31.25 30.60 49.47
1.9 6.2 þ2.9 þ14.5 5.7 3.5 5.8
Remarks: The mean value of the error in the last column refers to the mean value obtained by taking the absolute value of the error calculated in test No. 1e6.
4.
Conclusions (1) To monitoring rapeseed loss in field harvesting process, an integrated multi-block sieve loss sensor is mainly composed of a sensitive plate, a vibration isolating rubber and a support plate was developed, and experiment result indicated that grain detection resolution of the system is greatly improved by adopting an integrated multi-block structure. (2) Through simulation and calibration experiment found that the grain collision signal frequency is in 14e20 kHz, and the signal frequency caused by straw and shell collision is 1 kHz. A signal processing circuit with amplification, filtering, comparison, etc. was designed to extract the grain signal. (3) Through the calibration test, the whole system can detect 1000 grains s1 of rapeseed, and the monitoring accuracy of the developed cleaning sensor can up to 90%. Also, the calibration test shows that the system has a good applicability with rapeseeds of different moisture content, different varieties and from different regions. (4) The field test results showed that the averaged monitoring error of the rapeseed cleaning loss sensor was 5.8%. The sensor has the ability to operate under complex harvesting conditions.
Acknowledgments The authors gratefully acknowledge the National Key Research and Development Program of China, China (2016YFD0702101), the National Natural Science Foundation of China, China (51475217), Jiangsu Distinguished Professor Program, China, and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, China (PADP) for the financial support.
references
Craessaerts, G., Josse, D. B., Bart, M., & Wouter, S. (2010a). Fuzzy control of the cleaning process on a combine harvester. Biosystems Engineering, 106(2), 103e111.
Craessaerts, G., Wouter, S., Bart, M., & Josse, D. B. (2010b). Identification of the cleaning process on combine harvesters, Part II: A fuzzy model for prediction of the sieve loss. Biosystems Engineering, 106, 97e102. Daniz, Y., & Huseyin Caner, S. (2015). Development of measurement system for grain loss of some chickpea varieties. Measurement, 66, 73e79. Eldredge, K. W. (1985). Grain loss indicator. U.S. Patent No. 4490964. Ing, N. L. D., Ing, O. D. D., & Ing, P. E. D. (1976). Combine harvester grain loss monitor e electronics with loss mass per unit area indicator and grain mass adjuster. DE2527090. Kailash, C., & Himanshu, C. (2017). Optimum balancing of slidercrank mechanism using equimomental system of point-masses. Jaipur, India: Department of Mechanical Engineering, Malaviya National Institute of Technology Jaipur. Li, J. F. (2006). Improvement design of the structure of combine harvester grain loss sensor and laboratory calibration. Agricultural Equipment AMP in Vehicle Engineering, 11, 10e13. Liang, Z. W., Li, Y. M., Zhao, Z., et al. (2015b). Structure optimization of a grain impact piezoelectric sensor and its application for monitoring separation losses on tangentialaxial combine harvesters. Sensors, 15(1), 1496e1517. Liang, Z. W., Li, Y. M., Zhao, Z., & Chen, Y. (2013). Structure optimization and performance experiment of grain loss monitoring sensor in combine harvester in using modal analysis. Transactions of the Chinese Society of Agricultural Engineering, 29(4), 22e29. Liang, Z. W., Li, Y. M., Zhao, Z., Xu, L. Z., & Lu, Y. (2015a). Optimum design of grain sieve losses monitoring sensor utilizing partial constrained viscoelastic layer damping (PCLD) treatment. Sensors and Actuators A: Physical, 233, 71e82. Liang, Z. W., Li, Y. M., Zhao, Z., & Xu, L. Z. (2016). Sensor for monitoring rice grain sieve losses in combine harvesters. Biosystems Engineering, 147(1), 51e56. Li, J. F., & Jie, Z. (2007). Analyses of combine harvester grain loss measurement. Journal of Agricultiral and Mechanical Research, 12, 248e250. Li, Y. M., Liang, Z. W., & Zhao, Z. (2013). Experiment and vibration characteristics of sensitive plate on grain loss monitoring sensor. Transactions of the Chinese Society for Agricultural Machinery, 44(10), 104e111. Liu, C., & Leonard, J. (1993). Monitoring actual grain loss from an axial flow combine in real time. Computers and Electronics in Agriculture, 9, 231e242. Li, J., Webb, C., Pandiella, S. S., & Campbell, G. M. (2003). Discrete particle motion on sievesda numerical study using the DEM simulation. Powder Technology, 133, 190e202. Maertens, K., Ramon, H., & Josse, D. B. (2004). An on-the-go monitoring algorithm for separation processes in combine harvesters. Computers and Electronics in Agriculture, (43), 197e207.
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Mindlin, R. D., & Deresiewicz, H. (1953). Elastic Spheres in Contact under Varying Oblique Force. Journal of Applied MechanicsTransactions of the ASME, 20, 327e344. Strubbe, G. (1991). Grain loss sensor for combine harvester. US 5046362. Xu, L. Z. (2008). Design of main working parts of 4LYB1-2.0 rape combine harvester. Transactions of the Chinese Society for Agricultural Machinery, 39(8), 54e58. Xu, Q., Tang, F. Y., & Cheng, X. D. (2017). Experimental study on the relationship of compression properties of rapeseed and
moisture content. Grain Science and Technology and Economy, 42(1), 32e35. Zhao, Z., Li, Y. M., Liang, Z. W., & Chen, Y. (2012). Optimum design of grain impact sensor utilising polyvinylidene fluoride films and a floating raft damping structure. Biosystems Engineering, 112(3), 227e235. Zhou, X. L., Zhu, R. X., Zhou, X.j., & Tang, Y. (2010). The monitoring system for cleaning loss of the grain combine harvester based on sensor technology. Journal of Agricultural Mechanization Research, 2, 1e3.
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Research Paper
Development of rapeseed cleaning loss monitoring system and experiments in a combine harvester Lizhang Xu, Chuncai Wei, Zhenwei Liang*, Xiaoyu Chai, Yaoming Li, Qi Liu Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University, Zhenjiang Jiangsu, 212013, China
article info
To monitor rapeseed loss in real-time during the harvesting process, a rapeseed loss
Article history:
monitoring system based on the signal analysis of impacts, was developed. Firstly, to
Received 12 August 2018
understand grain and MOG (material other than grain) collision signal characteristic,
Received in revised form
models for different threshed outputs components were established in the EDEM software,
1 November 2018
and the corresponding collision signal characteristic for different components were ob-
Accepted 1 November 2018
tained, thereby laying the foundation for the design of a signal processing circuit. Secondly, using a P5-3B type piezoelectric ceramic as a sensitive element, a multi-block cleaning loss sensor and a signal processing circuit with amplification, filtering, rectification, compari-
Keywords:
son functions and early warning functions were developed. Finally, performance calibra-
Rapeseed
tion tests with different grain flow rates, different rapeseed varieties and mixture were
Sieve loss
carried out in the laboratory. Calibration and field testing showed that the detection ac-
Monitoring
curacy of the developed rapeseed loss monitoring sensor was high and the overall per-
Sensor
formance was acceptable.
Field experiment
1.
Introduction
Cleaning losses are one of the main types loss that occur in rapeseed machine harvesting, and the process of cleaning is also one of the main bottlenecks restricting the development of mechanised rapeseed harvesting in China. At present, rapeseed cleaning is mainly adjusted manually (Xu, 2008), with the main disadvantage being that the measurement lags and has a large error. The method cannot provide accurate current grain loss levels for the operator to adjust relevant working parameters in time and this causes a direct loss of income for the famers. Much research has been carried out to
* Corresponding author. E-mail address: [email protected] (Z. Liang). https://doi.org/10.1016/j.biosystemseng.2018.11.001 1537-5110/© 2018 IAgrE. Published by Elsevier Ltd. All rights reserved.
© 2018 IAgrE. Published by Elsevier Ltd. All rights reserved.
monitor grain sieve losses in combines (Ing et al., 1976; Maertens.,2004; Zhao, Li, Liang, & Chen, 2012). As early as 1977 Liu et al. (1993), proposed using electroacoustic principle to monitor grain loss in real-time. A high sensitive microphone was used to pick up the collision sound signals generated as wheat grain and stem collided with the sensitive plates; the full grain collision signal was filtered out by the signal processing circuit and then the grain loss was calculated. However, it was found that the accuracy of the sensor was poor due to the interference of mechanical vibration and internal noise in practise. Strubbe et al. (1991), selected plates with a resonant frequency of 8e25 kHz as the sensitive plate, and experiment results indicated that the
b i o s y s t e m s e n g i n e e r i n g 1 7 8 ( 2 0 1 9 ) 1 1 8 e1 3 0
Nomenclature a q e E* E1 E2 QT u Fn Fn max Ft Fdn Fdt g G* G1 G2 amax tr ti R1 k I m m* n QL q sm U v vn vt vrel n vt rel dn dt a n1 n2 R* R2
Contact radii, m Grain impact angle, (0) Coefficient of restitution Effective Young's modulus, MPa Young's modulus of grain, MPa Young's modulus of sensor, MPa Theoretical monitoring quality Rotational velocity, rad s1 Normal contact force, N Maximum normal impact force, N Tangential contact force, N Normal damping force, N Tangential damping force, N Gravitational acceleration, m s2 Effective shear modulus, MPa Shear modulus of seed, MPa Shear modulus of sensor, MPa Maximum normal overlap, m Rise time, s Collision time, s Radius of the grain Proportional coefficient Moment of inertia, kg.m2 Mass of grain, g Equivalent mass, g Contact normal vector Measured cleaning loss One grain mass, g Number of monitored grains Unit vector of rotating shaft Vector of particle velocity m s1 Normal velocity, m s1 Tangential velocity, m s1 Normal relative velocity, m s1 Tangential relative velocity, m s1 Normal overlaps, m Tangential overlaps, m Normal overlap, m Poisson's ratio of material Poisson's ratio of plate Effective radius Radius of the sensitive plate
grain collision frequency is 15 kHz, which improved grain signal response and the monitoring accuracy improved significantly. Craessaerts. et al., 2010 used pressure sensors to measure the pressure difference in different parts of the vibrating sieve. By using the fuzzy control theory, a nonlinear model for predicting grain sieve was established, and the best position for the cleaning loss monitoring sensor in combine harvester was indicated. Deniz and Huseyin Caner (2015), measured cereal losses (chickpea) with PW4C3-300 pressure sensors. By analysing material collision response, grain loss was identified and calculated. The effectiveness of the sensor was verified by field experiments. Agricultural machinery companies have also conducted in-depth research on
119
cleaning loss monitoring technology. Some commercial grain loss sensors, such as the MK4 type grain loss monitor developed by RDS Technology Ltd. Minchinhampton, Stroud, Gloucestershire, UK, the LH756 type loss monitor by Teejet company, Wheaton Facility, IL,USA), and the grain loss sensor designed by the Australian KEE group (Langar Way, Landsdale, Perth WA, Australia) has become one of an indispensable accessory for farmers. The Ferguson 860 combine harvester made by British Ferguson company, the Case 2366IH type combine harvester by Keyes Co, the JD9660STS type harvester by the United States John Deere Company and the Lexion 760 harvester by the Class company (Eldredge, 1985) also installed grain loss sensors. In Europe and the USA, research on cleaning loss monitoring sensors has been carried out for around 30 years and the relevant technology is mature. However, in recent years, some scientists from China have carried out some research on grain loss monitoring by using PVDF, multi contact induction film and piezoceramic as sensitive elements. Li (2006) and Li and Jie (2007), proposed using PVDF with its high piezoelectric constant and wide frequency response to construct a sensitive plate to monitor grain loss. Zhou, Zhu, Zhou, and Tang (2010) used a multi contact induction film to monitor the grain signal. When the grain impacted the film, the induction film formed a closed loop and a pulse signal was obtained. However, the design cost is high and the mechanism has not been applied in a harvest. Liang, Li, Zhao and Chen (2013) used modal analysis with different thicknesses and different boundaries by using ANSYS software with to predict the performance of sensitive plates of 304 stainless steel, T6 aluminium, and brass. Grain collision tests were carried out to study the relationship between the sensitive plate vibration characteristics and detecting performance by using YT-5L piezoelectric ceramic as sensitive element. The material and structure of the sensitive plate was selected. Li et al., (2013) designed a grain loss monitoring system, in which the symmetry sensors was used to eliminate machine vibration interference. The stimulating signals were processed by charge amplifier band-pass and anti-aliasing filter, and the signals of cleaning losses were then extracted effectively. The single-chip computer unit was used for monitoring and counting in real-time. The results of laboratory test and field test showed that the designed monitoring system can effectively monitor combined harvesters cleaning losses, and the maximum measurement error was 2.81%, which met the practical monitoring requirements. From the above it can be seen that researchers in China are focussing on the monitoring of rice, wheat and soybeans loss during harvest. However, rapeseed is an important crops in China for the production of edible oil. Currently, there is no literature on monitoring rapeseed sieve loss in a combine harvester. Field results have indicated that it is not effective to monitor rapeseed sieve losses using sensors developed for rice and wheat. Therefore, it is necessary to design a sensor to specifically monitor rapeseed loss according to the properties of rapeseed. Firstly, on the basis of studying the characteristics of rapeseed cleaning and ejecting materials, discrete element software was used to simulate the collision mechanics process of rapeseeds against monitoring plates with different compositions and different moisture content, and
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the parameters of signal processing circuits were determined by experiments. Then, a multi-block cleaning loss monitoring sensor was developed. Finally, performance calibration tests with different grain flow rates, different rapeseed varieties and mixture were carried out in the laboratory, and a field experiment was carried out.
2.
rapeseed loss monitoring sensor with a monitoring accuracy of 300e1000 is required. From the above analysis, it can be concluded that the requirements for a rapeseed loss sensor are as follows: (1) Due to the large number of loss grains per unit time, the developed sensor needs to meet the overall detection frequency of 1000 grains s1. (2) The sensor should have a good monitoring performance for rapeseed with multiple varieties and different maturity (moisture content 8%e30%).
Experimental section
2.1. Difficulties and technical requirements for rapeseed cleaning loss monitoring There are a number of rapeseed varieties in China, the typical varieties are designated mustard, cabbage and Chinesecabbage types. The differences among the varieties are significant: (1) Mustard-type rapeseed has a thousand-grain mass of 1.5e2.5 g where as the thousand-grain mass for cabbage-type is 3e4 g, and thousand-grain mass for Chinese-cabbage-type can be up to 6e7 g. The mass of a single rapeseed is only 1/7 of rice grain, and only 1/5 in volume. Small rapeseeds are mixed in large number of pods and other debris which increase sensor identification difficult more greatly. (2) The rapeseed plant is a disorderly flowering plant and grain maturity varies greatly over the harvesting season. Even in the same field, the harvest may consist of green kernels, brown kernels and black kernels and moisture content has a large range (8%e 32%), which makes the detection and identification of the oilseed kernel difficult. (3) Compared with rice and wheat, rapeseed has a larger grain/straw ratio (2.5e5), and the components discharged from the cleaning shoe are complicated. According to test bench results analysis, horn shells account for about 60% in mass, straws account for about 30%, impurities account for 6%, and full grains are only 4% (Xu, 2008); images of the physical components within the discharged materials as shown in Fig. 1. Those features place high requirements on the adaptability of the sieve loss sensor. The existing accuracy of a typical rice cleaning loss sensor in China is about 150e200 grains s1, which can meet the requirements for monitoring of rice cleaning, but existing sensors are not applicable to the rapeseed since there are large amounts of grain lost in the grain loss sensor installation position. Generally speaking, a
2.2.
Overall design of the rapeseed monitoring system
Although the effects of sensitive plate material, dimensions, and damping ratio on the effect of grain signal attenuation have been studied, and detection frequency has been improved for whole-plate cleaning loss sensors. However, they are still unable to meet the rapeseed loss monitoring requirements specified above. Based on our previous experience on designing of rice grain loss sensor (Liang et al., 2015 & 2016), an integrated multi-block cleaning loss monitoring structure is proposed: four monitoring units are arranged in the lateral direction of tail sieve, and the monitored data from the four units is combined to estimate the overall rapeseed grain loss ratio, thus ensuring the required monitoring accuracy can be guaranteed. The monitoring systems composed an integrated multi-block sensor, a signal processing circuit and a host computer. The signal processing circuit mainly includes amplification circuits, band pass filtering circuit, full wave rectification circuit, and envelope comparison circuit. The host computer was developed on the basis of FPGA basic board (Xilinx, San Jose, CA, USA). The structure of the monitoring system is shown in Fig. 2. When the monitoring device was in operation, a charge signal was generated by mechanical vibration during the grain collision process. The charge signal was then transmitted to the signal processing circuit via the RG174 signal line. The signal processing circuit composed of a charge amplifying circuit, a band pass filter, a precision full-wave rectification, an envelope detector, and voltage comparator. The standard pulse signal modulated by the signal processing was input to the host computer through an external interrupt. The realtime cleaning loss was calculated based on the built-in mathematical model and displayed. As the rapeseed properties vary greatly in different fields and at different harvest times, in order to reflect the current grain loss level, the parameters of the cleaning loss monitoring model, such as the
Fig. 1 e Physical diagram of components within the discharged materials.
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Fig. 2 e Monitoring system of rapeseed cleaning loss.
rapeseed grains one-thousand mass need to be adjusted according to the situation in the field.
2.3.
Structure of the sensor
The integrated multi-block sieve loss sensor, shown in Fig. 3, one monitoring unit was mainly composed of a sensitive plate, a vibration isolating rubber and a support plate. The support plate was 1050 mm in length and 90 mm in width. Four sensitive plates with a size 210 mm 120 mm 0.5 mm (length width thickness) were arranged laterally above the support plate. The gaps between the sensitive plates were 2 mm. Piezoelectric ceramics have large piezoelectric constant, high sensitivity, mature technology and low cost, and can respond to a small deformation of 0.001 mm. It is therefore a suitable material for detecting dynamic changes in small grain collisions. In this paper, a P5-3B piezoelectric ceramic (20 10 1 mm (length width thickness)) with a piezoelectric coefficient of 580 PC N1 was attached to the centre of the sensitive plate, and sealed with a silicone glue to construct to a monitoring unit (Liang et al., 2015). From modal analysis to the sensitive plate shown in Fig. 4 it was seen that the sensitive plate has large deformation in the centre, thus if the sensitive element was pasted in the centre of the plate, overall sensitively could be guaranteed. The sensitive plate was connected to the lateral connecting plate by a VD type vibration isolating rubber (Kailash, 2017). The supporting plate
connected with connecting plate though bolts. The amount angle of the sensor could be adjusted in the range of 0ºe90 by changing the position of the rubber supports. The material of the sensitive plate directly affects the signal attenuation and collision signal amplitude. Generally speaking, the thicker the sensitive plate, the smaller the relative deformation of the monitoring board, and the less the charge it produces. As the deformation rate of the monitoring plate was small when the rapeseed grains collide the plate, in order to guarantee the overall sensitivity of the sensor, a stainless steel 304 plate with a thickness of 0.5 mm was selected as the sensitive plate.
2.4. Dynamic characteristic of material collision process with plate Since rapeseeds and its MOG have different Young's moduli, coefficient of restitution for the sensitive plate and mass, the maximum normal overlap amax and the collision time ti displayed significant changes according to conservation of energy principle, where amax and ti can be deduced by the following equations: 2=5 15 mv2n amax z * *1=2 16 E R
(1)
ti z2:94amax =vn
(2) *
*
where, vn is vertical collision velocity, E , R is given by: E* ¼
Fig. 3 e Structure diagram of assembled cleaning loss sensor.
1 1 y21 1 y22 þ E1 E2
Fig. 4 e Modal analysis to the sensitive plate.
(3)
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1 1 R ¼ þ R1 R2 *
(4)
where v1 and v2 are the Poisson's ratio of the two materials; R1 and R2 are the radius of the two elements, E1, E2 are the Young's modulus of elasticity. Due to piezoelectric properties, and the polarization direction of piezoelectric ceramic, the value of collision output voltage was proportional to the maximum collision force Fn max. The value of force was proportional to the maximum normal overlap amax, which meant that the larger amax in sensitive plates, the larger Fn max. From Eq. (2) it can be seen that there was also great difference in rise time with different values of amax. Variations in normal collision forces indicate that rise time tr was equal to a quarter of the signal period. Differences in Fn max and tr produced corresponding differences in signal frequency and voltage amplitude, thus understanding Fn max and tr was a critical step in discriminating grains (Liang. et al., 2016). The discrete element method (DEM) is a numerical technique to model the motion of an assembly of particles which interact with each other through collisions. At each time step, the trajectory of each particle in a system can be obtained using a numerical time integration scheme and all forces acting on the particles like contact forces and body forces are summed which can be described by Newton's second law of motion. In the inertial coordinate, the governing equations for the acceleration of the particle centre and particle angular can be written as: dv ¼ Fn þ Ft þ mg m dt I
du ¼ R$U ðFn þ Ft Þ dt
(5)
(6)
where m is the particle mass, v and u are the translational and rotational velocities of the particle, Fn and Ft are the normal and the tangential impact forces, R and U are the rotating radius and the unit vector of rotating shaft, g is the gravitational acceleration, I is the moment of inertia. Therefore, the translational velocity vt of the contact point of the plate can be deduced: vt ¼ v þ R$U u
2.4.1.
(7)
Normal contact model
The contact model, which without doubt plays an important role in numerical simulations, is of paramount importance to DEM. In this paper the HertzeMindlin contact model which can be considered as a ‘spring-dashpot’ configuration was used to calculate the force changing process of the grain. During the elastic mode of loading for two contact spheres, the contact is treated as elastic and was governed by the Hertz formula, the normal contact force Fn can be expressed by: 4 Fn ¼ E* R*1=2 d3=2 n 3
(8)
where, dn is the normal overlaps, E*, R*is given by Eqs. (3) and (4).
2.4.2.
Tangential contact model
The theory of Mindlin was used for the elastic frictional contact between two spheres in the tangential direction. Let Ft ðnÞ and Ft ðnþ1Þ be the tangential contact forces before and after an increment of tangential displacement Dd, respectively. The relationship between Ft ðnÞ and Ft ðnþ1Þ is given by the following incremental formula: Where KT is computed according to Mindlin and Deresiewicz (1953), and the effective shear modulus G*can be calculated by: G* ¼
2 y1 2 y2 þ G1 G2
(9)
where, G1 and G2 are the shear modulus of the two materials. Additionally, there are normal damping force Fdn and tangential damping force Fdt which can be written as: Fdn
rffiffiffi 5 pffiffiffiffiffiffiffiffiffiffiffi* rel b Sn m vn ¼ 2 6 rffiffiffi 5 pffiffiffiffiffiffiffiffiffiffiffi* rel b St m vt 6
Fdt ¼ 2
(10)
(11)
rel where, vrel n is the normal relative velocity, vt is the tangential relative velocity, Sn, st , b can be deduced as:
pffiffiffiffiffiffiffiffi Sn ¼ 2E* R* a
(12)
pffiffiffiffiffiffiffiffi St ¼ 8G* R* a
(13)
ln e b ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 ln e þ p2
(14)
where a is the contact radii, e is the coefficient of restitution.
2.5.
Collision process simulation and results analysis
Rape straw is mainly composed of an outer epidermis and cavernous body, and the elastic modulus and the density of the outer epidermis and the internal cavernous body differ from each other greatly. To make the simulation closer to the reality, the straw model was set up by the outer spherical pellet and the internal cavernous mass in the EDEM 2.7 (DEM Solutions Ltd., Edinburgh, UK). The typical diameter of the outer epidermis was set at 1 mm and the diameter of the cavernous ball 3 mm and the density inside and outside were set accordingly. The established straw model is shown in Fig. 5(b). The angular fruit shell is an inner cavity structure and its outer shell has an elongated structure. The shell was assembled from more than 1700 small balls with diameter of 0.5 mm with one end of the shell piled into the tip, and the other stacked into the form of an ellipsoid end, as shown in Fig. 5 (c). The specific properties of rape seed, straw and shell are shown in Table 1, and the contact parameters between each material and sensitive plate are shown in Table 2. Contact model is an important foundation for discrete element simulation. The HertzeMindlin sliding contact model
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used here has often been used in other agricultural material simulations (Li, WebbPandiella, & Campbell, 2003). In the geometry panel of the EDEM software, the sensitive plate model with a thickness 0.5 mm, 210 mm length and 120 mm width was established. To simulate the impact behaviour, the grain dropped from a height of 350 mm, the corresponding time step and the data saving interval were 106 s. After each simulation, collision information was obtained in the postprocessing module in the EDEM 2.7 software (Analyst module in EDEM 2.7). Collision process between material and monitoring board in EDEM shown in Fig. 6. From Fig. 6 it can be seen that the collision process can be divided into 3 phases, that is, falling, collision, and rebound. As the rapeseed has a spherical surface, the collision attitude has a slight impact on grain collision force characteristics. However, because of their shape, collision attitude has a significant impact on collision force for both straw and shell. Therefore, it is necessary to analysis the effect of the collision attitude on collision signal to lay the foundation for correctly determining the corner frequency of the filter circuit.
2.5.1.
Table 1 e The mechanical characteristic parameters of model. Material properties Density (kg m3) Poisson ratio Shear modulus (Pa)
Rapeseed Straw
Shell
880 550 300 0.25 0.45 0.35 5.2 107 4.4 106 0.8 106
Sensitive plate 7850 0.30 7.5 1010
Table 2 e The contact characteristic parameters of model. Contact Rapeseed characteristics Sensitive plate Recovery coefficient Static friction coefficient Rolling friction coefficient
StrawSensitive plate
ShellSensitive plate
0.62
0.30
0.20
0.46
0.80
0.8
0.01
0.01
0.01
Rapeseed collision results analysis
The collision force between grain and sensitive plate is shown in Fig. 8, from which it can see that the average impact force rise time is 16 ms, the corresponding vibration signal frequency is 15.6 kHz, and the peak force is 0.73 N (Fig. 7). To verify software simulation results, grain impact experiments using fresh rapeseed grains were carried out in the laboratory by mounting the instrumented piezoelectric element in the centre of the sensitive plate. In each experiment, a single rice grain was dropped from a height of 300 mm (Point A) to impact with the plate, the signals were then processed by a charge amplifier and the generated voltage signal was recorded by a storage digital oscilloscope (DS0-X 3024A, Aglient Technologies, Beijing, China) with a bandwidth of 200 MHz. Grain impact experimental system is shown in Fig. 8. Typical grain collision signal acquired by digital oscilloscope are shown in Fig. 9. In Fig. 9 grain 1 and grain 2 represent two rapeseeds with different moisture contents for the same variety. Grain 1 has a moisture content 11.33% whilst grain 2 has a moisture content of 25.02%. From Fig. 9 it can be seen that the rapeseed collision signal is a period attenuating signal, the signal rise time has a slight difference from each other. This shows that the rise time of the rapeseed grain with greater moisture content (grain 2) is larger than the signal from rapeseed grain with less moisture (grain 1). This is because rapeseed grain with more moisture (grain 2) exhibits a greater damping and consumes more collision energy than the drier rapeseed grain (grain 1).
Rapeseed has a wide range of moisture content during harvest, and the physical properties such as elastic modulus, shear modulus and mass density are closely related to moisture content. Therefore, it is necessary to study the rapeseeds collision signal characteristics with different moisture contents to increase its applicability during harvest. Generally speaking, with increasing moisture content, rapeseed becomes plump and the seed coat soften, and therefore the elastic modulus decreases. The elastic modulus and density of rapeseed under different moisture content were examined by Xu, Tang, and Cheng (2017). Here, moisture content is divided into 6 groups in the range of 10%e35% during the EDEM simulation, different parameters were set separately, and the corresponding collision force rise times under different moisture contents as shown in Fig. 10. As shown in Fig. 10, when the grain moisture content is low contact force increases rapidly and the maximum contact force is greater, the rise time is also lower; with high moisture content, the contact force rise time increases correspondingly. Especially for rapeseeds with moisture content in the range 25e30%, contact time experiences a rapid increase, which may be related to the decrease of elastic modulus and increased adhesion. The rise time of collision force is dissipated in range 14e20 ms, the collision signal frequency ranges for rapeseed with moisture content of 10%e35% are within 13e17 kHz. Because the high moisture content (30%) is not suitable for mechanical harvesting, the corner frequency of the band pass
Fig. 5 e Established model for main components within the threshed output.
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Fig. 6 e Collision process between material and sensitive plate in EDEM. method is suitable for studying the collision process of the threshed outputs with the sensitive plate.
2.5.2.
Fig. 7 e Contact force of rapeseed with-sensitive plate.
Fig. 8 e Grain impact experimental system.
2.5.3. filter was set as 14e20 kHz. However, the difference between the simulated and measured rise times with different moisture content are not significant, and this further confirmed the feasibility of using the EDEM simulation. After verifying the simulation results by measurement in the laboratory, the DEM
Short straw collision results analysis
Short straws significantly influence sensor monitoring accuracy. From Fig. 11 it can be seen that the maximum short straw collision force is about 1.2 N, and the corresponding rise time is much larger than that for the rapeseed at about 170 ms. As short straw with different lengths may collide with the sensitive plate at different attitudes during harvesting, it is necessary to investigate the influence of collision angle and the short straw length on rise time. It was assumed that the angle between length direction of the straw and the collision plane was the impact angle, marked as q. Variations of tr for short straw collisions with the sensitive plate at conditions of vn ¼ 2.5 m s1, straw length l ¼ 10e90 mm, q ¼ 0e90 are shown in Fig. 12. From Fig. 12 it can be seen that rise-time tr2 was relatively short when the impact angle q ¼ 0 or 90 , and force rise time tr increased with the increasing of straw length l when q was fixed. It was also found that force rise time tr increased rapidly in the range of 0 < q < 45 . It reached a relatively high value when q ¼ 45 , and then decreased with increasing q. Generally speaking, the rise time of the short straw was distributed in the range of 90e200 ms.
Shell collision results analysis
As the shell has a low density and low elasticity modulus, and was always short, field experiments showed that most of collision had two forms: q ¼ 0 or 90 , the corresponding collision force is shown in Fig. 13, from which it can be seen that the corresponding collision force was generally 0.25 N, but the rise time tr was much longer and >264 ms.
b i o s y s t e m s e n g i n e e r i n g 1 7 8 ( 2 0 1 9 ) 1 1 8 e1 3 0
125
Fig. 9 e Typical grain collision signal acquired by digital oscilloscope. (NB: grid in axial direction (time) is 20 ms, grid in vertical direction (amplitude) is 200 mv).
Fig. 10 e The relationship between the rising time of contact force and the moisture content.
2.6.
Design of the signal processing circuit
From above the simulation and measurement results it can be seen that there is a distinct boundary in rise time when
Fig. 11 e Contact force signal of short straw with-sensitive plate.
different materials collide with the sensitive plate. The rise time for rapeseed grain is distributed in the range of the 12e15 ms, the short straw is distributed in the range of 90e200 ms, and the rise time of the shell is generally larger than 260 ms. If the corner frequency of the pass filter was set in 14e20 kHz, thus the rapeseed grains can be discriminated correctly. Based on the simulation results, the designed signal processing circuit shown in Fig. 14. As shown in Fig. 14, the signal processing circuit included a charge amplification circuit, a band-pass filter circuit, a precision full wave rectifying circuit, envelope detection and a standard square wave generator circuit. The charge amplifier with a magnification of 10 amplified the charge signals from piezoelectric ceramic. The filter is essentially a frequency selection device. According to the different frequency bands occupied by the signal and noise, the MFB band-pass filter was
Fig. 12 e Variation of rise time tr2 during short straw collisions with sensitive plate.
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selected as the filter structure to filter out interference signal such as straw and shell, only keep grain signals. In addition, the centre frequency of band-pass can be changed by adjusting R4. Due to the significant difference in the voltage amplitude produced by grain impact sensitive plate, the grain impact signal can be identified according to the peak value of the impact signal voltage. As lost rapeseeds often have different normal velocity, rotation angle and contact angle, whether the generated voltage signal is the positive or negative is unknown. However, to detect the peak value of the impulse voltage signal accurately, a set of precision full wave rectifying circuit composed of a half -wave precision rectifying circuit and adder circuit was designed, which made all the output signal positive. The attenuation signal produced by the sensor was by contrast irregular. The envelope detection circuit was designed with a precision full wave rectifying circuit to obtain envelope curve of the grain impact signal, and as a result the subsequent counting error was reduced greatly (Liang et al., 2016). The FPGA (Xilinx, San Jose, CA, USA) basic board was chosen as the core of the host computer. The generated standard square wave signal is sent to the FPGA board through the external fracture. When the external interruption changes from high level to low, the trigger program counts the numerical value. Using the clock function, the counted value can be sent to display. There is a USB port on the side of the upper computer, which can store the monitored data in real time. The host computer also displayed the total loss by mass according to a proportional coefficient between sensor monitored value and the actual grain loss k found in the program. When the loss exceeded a predetermined value, the system will alarm the operator. The PC program is written in the Altera Quartus II 15.0 (Intel, City of Santa Clara, CA, USA). The developed host computer as shown in Fig. 15. Pre-experiment was needed to determine the proportional coefficient between sensor monitored value and actual grain loss k according to the following equation:
k¼
QL qsm
(15)
where, QL is actual cleaning loss, g; q is one grain mass, g; sm is number of monitored particles.
2.7.
Calibration experiment
To verify performance of the developed cleaning loss sensor, an indoor test was carried out on the calibration test bench (as shown in Fig. 16) under different grain flow conditions (50e300 grains s1) and different mixtures. The speed of the conveyor belt, with a dimension of 1.5 m in length and 0.5 m in width, could adjusted in the range of 0e0.5 m s1. The support frame is in the front of the conveyor belt and the sensor sensitive plate (single block) mounted on the support plate. The angle, height and rear position of the support plate can be adjusted to enable the material can impact the sensor at different speeds, heights and angles (Liang et al., 2015).
2.7.1.
Effects of grain flow rates on monitoring accuracy
The number of 50, 100, 150, 200, 250 and 300 “Zheyou 51” rapeseeds with a moisture content of 16.4% was selected as the calibration material. As shown in Fig. 16, selecting a 300 mm length interval on the conveyor belt, and spread the rapeseeds evenly over the interval. The governor was adjusted to let the conveyor belt run with a velocity of 0.3 m s1, so that all the grains can fell onto the sensor surface within 1.0 s (Liang et al., 2015). Each set of experiments was repeated five times and the averaged value used, as shown in Table 3. Examining Table 3 it can be seen that the grain drop density is relatively low when the rapeseed flow rate is in the range of 50e150 grains s1, and the interaction among grains is also small, and the monitoring accuracy high (above 95%). When the grain flow rate is 100 grains s1, the detected number of grains was larger than the measured amount of rapeseeds; this might due to double impact occurring for some large grains. When the grain flow rate is in the range 200e300 grains s1, accuracy is reduced. But the error is still 10%. In the large grain flow rate test, the counted number of grains was smaller than the measured number of grains. This is might due to the large amount of grain that collide with the sensor surface within a short interval; there is a large possibility for two or more grains impacting on the sensitive plate at the same time, and resulting in a “leakage”. In addition, it is possible that a small parts of the kernels did not fall onto the sensitive plate due to barriers between the grains (Liang et al., 2015). The allowed grain loss limit is 4% according to the Chinese standard, it is estimated that the grain flow rate is about 150e200 grains s1 at the sensor installation position.
Fig. 13 e The contact force of the shells-sensitive plate. R1
+5V
+ -5V
C2 C3
R2
+
R3 R5
GND GND
UU2
R6
+ -5V R7 GND
GND
+5V UU3
D2
R9
+5V UU4
+ -5V
R10
R13
-
GND
UU6
R15 1
2
2
5
GND R14 GND
6
C5
C6 Vout GND
Fig. 14 e Diagram of the signal processing circuit.
R16
7
555
+
D3 C4
R11
UU5
4
+5V UU1
GND
8
-
D1
1
R12 R4
3
R8
C1
GND
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Fig. 15 e The host computer of the system.
Fig. 16 e Cleaning loss sensor calibration test bench.
The above test results showed that the monitoring accuracy of rapeseed cleaning sensor was 90%, which can meet the basic requirements for monitoring field cleaning loss. In addition, the rapeseed was tested in the laboratory using three levels of moisture content and the accuracy rate was >90%.
2.7.2.
Mixture calibration test
The shell, straw and light residues have a great influence on sensor monitoring accuracy. According to the ratio of each component within the discharged mixture, seven different calibration mixture were prepared. To test the ability of the designed signal processing circuit the mixture was spread evenly over the conveyor belt within 300 mm, and the drop height is set to 300 mm. Each test is repeated 3 times, and the averaged value recorded. The results are shown in Table 4. It can be seen from Table 4 that the straw and shell signals were almost completely filtered out, and they has almost no influence on sensor monitoring accuracy. It can also be seen from the results of tests 4, 5 and 6 that a large amount of mixture material can have large influence on identifying grains. This is mainly due to grains being unable to contact with the sensitive plate and the shell or residue shielding the grains. In test 7, when a part (~60%) of the straw and the shell were shielded by the combing, the interference factor was greatly reduced, and the precision was improved (Liang et al., 2015).
2.7.3.
Calibration test of different rapeseed varieties
There are many varieties of rapeseed in China, and the differences among varieties in grain size and one thousand-grain
mass are significant. To test the adaptability of the designed sensor to different varieties of rapeseed, a calibration test was carried out in the laboratory using a grain flow of 150 grains s1. The rapeseed varieties are: Zheyou 51, Qinyou No. 11, Qingyou No. 1, Oyster Sauce 737, Nanyou 868. The abovementioned varieties were obtained in the harvest season. The experiment results are shown in Table 5, from which it can be seen that the designed sensor has a good monitoring accuracy of 94% for the 5 varieties, and therefore has a good applicability to rapeseed of different varieties and regions.
3.
Field experiment
A field experiment was carried out in Huafeng Farm, Yancheng City, Jiangsu Province, May 2018. The developed sensor location in TH988 type longitudinal flow full feed combine harvester as shown Fig. 17. The harvested rapeseed variety is the “Zhejiang
Table 3 e Accuracy of cleaning loss sensor under different grain flow rates. No.
1 2 3 4 5 6
Flow (grain s1)
Average counted (grains)
Monitoring accuracy (%)
Standard deviation
50 100 150 200 250 300
48.8 103 142.5 185 228 271.8
97.6 97.0 95.0 92.5 91.2 90.6
0.95 1.05 1.26 1.83 2.04 3.11
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51”. Its basic properties were: average plant height 1206 mm, fruit pod height 640 mm, coronal diameter 335 mm, yield 2895 kg ha1, grain moisture content 10.7%, one thousand seeds mass is 3.422 g. Before the test, the combine harvester was started and allowed to achieve normal working state. The threshold voltage of the sensor was adjusted until the monitored values in the display instrument were stable, which indicated that the sensor was working normally (Liang et al., 2015). The harvesting distance was 20 m and the proportional coefficient k ¼ 3.51 was determined by repeat the pre-experiment three times. The full test consisted of 6 groups and was carried out according to pre-test protocol. The results of six groups of main test results are shown in Table 6. Each data is defined as follows: Qm ¼ sm q
p¼
(17)
where, p is detection error, %. It can be seen from Table 6 that the sensor can provide good monitoring accuracy with the pre-test determining proportional coefficient k. The averaged monitoring error was 5.8%, which satisfies the expected target value (error < 10%). Although the monitored grains can be in the range 150e230 grains s1 during a period with larger grain losses, and the overall monitoring accuracy was 10%, which proved that the integrated multi-block sensor has a good adaptability, and the monitoring resolution of the sensor is significantly improved compared to using the rice cleaning loss sensor. The experiment also found that increasing the combing teeth will reduce the interference of the straw and the shell on the sensitive plate. In addition, the angle of sensor installation angel is critical important to get a better monitoring accuracy.
(16)
QT ¼ kQm
ðQT QL Þ 100% QL
where, q is one grain mass, q ¼ 3.422/1000 g; sm is monitored grain number; Qm is actual test quality, g; k is scale factor, k ¼ 3.51; QT is theoretical monitoring quality, g.
Table 4 e Different mixture calibration test results. No. 1 2 3 4 5 6 7
Calibration material
Monitor grain volume/g
Monitoring accuracy/%
Standard deviation
Shell (30 g) Straw (20 g) Shell (30 g) þ straw (20 g) þ residue (2 g) Rapeseed (150 grain) þ shell (30 g) Rapeseed (150 grains) þ straw (20 g) Rapeseed (150 grains) þ shell (30 g) þ straw (15 grains) þ residue (2 g) Rapeseed (150 grains) þ shell (6 g) þ straw (4 g) þ residue (1 g)
0.7 0.33 1 138 139.7 137 141.3
e e e 92.0 93.1 91.3 94.2
e e e 1.03 1.16 1.24 1.40
Table 5 e Calibration results of different varieties of rapeseed. Varieties Zheyou51 Qinyou11 Qingyou1 Nanyou868 Fengyou737
Thousand mass(g)
Moisture content (%)
Planting area
Monitoring accuracy (%)
4.01 3.95 4.2 3.54 3.87
13.9 16.7 15.6 20.3 19.4
Zhejiang area Jiangsu, Zhejiang and Shanghai area Chongqing area Sichuan area Anhui, Huaihe area
96.2 94.2 95.4 94.0 95.6
Fig. 17 e Sensor installation location and field test.
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Table 6 e Field trial results of rapeseed cleaning loss. Group
Detection time t, s
Detection of the number of particles sm, particles
Actual quality of detection Qm, g
1 2 3 4 5 6 Average
49 48 43 40 36 32 41
5035 5225 4175 4923 2455 2459 4045
17.22 17.88 14.29 18.85 8.40 8.41 14.18
Theoretical test Actual cleaning loss Detection quality QT, g quality QL, g error p (%) 60.44 62.76 50.16 66.16 29.48 29.52 49.75
61.60 66.88 48.72 57.76 31.25 30.60 49.47
1.9 6.2 þ2.9 þ14.5 5.7 3.5 5.8
Remarks: The mean value of the error in the last column refers to the mean value obtained by taking the absolute value of the error calculated in test No. 1e6.
4.
Conclusions (1) To monitoring rapeseed loss in field harvesting process, an integrated multi-block sieve loss sensor is mainly composed of a sensitive plate, a vibration isolating rubber and a support plate was developed, and experiment result indicated that grain detection resolution of the system is greatly improved by adopting an integrated multi-block structure. (2) Through simulation and calibration experiment found that the grain collision signal frequency is in 14e20 kHz, and the signal frequency caused by straw and shell collision is 1 kHz. A signal processing circuit with amplification, filtering, comparison, etc. was designed to extract the grain signal. (3) Through the calibration test, the whole system can detect 1000 grains s1 of rapeseed, and the monitoring accuracy of the developed cleaning sensor can up to 90%. Also, the calibration test shows that the system has a good applicability with rapeseeds of different moisture content, different varieties and from different regions. (4) The field test results showed that the averaged monitoring error of the rapeseed cleaning loss sensor was 5.8%. The sensor has the ability to operate under complex harvesting conditions.
Acknowledgments The authors gratefully acknowledge the National Key Research and Development Program of China, China (2016YFD0702101), the National Natural Science Foundation of China, China (51475217), Jiangsu Distinguished Professor Program, China, and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, China (PADP) for the financial support.
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