BP neural network recognition algorithm for scour monitoring of subsea pipelines based on active thermometry

Optik 125 (2014) 5426–5431

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BP neural network recognition algorithm for scour monitoring of subsea pipelines based on active thermometry Xuefeng Zhao a,∗ , Qin Ba a , Lei Zhou b , Weijie Li a , Jinping Ou a,c a b c

School of Civil Engineering, Dalian University of Technology, Dalian 116024, China Engineering Company, Offshore Oil Engineering Co., Ltd., Tianjin 300451, China School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China

a r t i c l e

i n f o

Article history: Received 3 October 2013 Accepted 27 May 2014 Keywords: Scour Monitoring Active thermometry BP neural network Subsea pipelines

a b s t r a c t Scour monitoring is an important concern for subsea pipeline systems. The active-thermometry-based scour monitoring is based on the difference of heat transfer properties between sediment and sand, recognizes the surrounding media though temperature changing patterns during heating and cooling processes, and hence detects the free spans. Based on the scour monitoring system, a two-layer BP neural network was employed to process the monitoring data and achieved media recognition. The network’s inputs were normalized temperature time histories. The network’s outputs denoted different media: sediment and water. To validate the method, three experiments were conducted; one was used for training the network and the other two for testing. Also, the effect of noise on the network’s performance was studied through simulation. The results demonstrated the feasibility and robustness of the neural network. © 2014 Elsevier GmbH. All rights reserved.

1. Introduction As the increase of word’s demand for energy and the decrease of inland storage of oil and gas, subsea oil and gas are playing increasingly important role in human society. Being the lifeline of transmission of subsea oil and gas, subsea pipelines have gained wide application. Since first subsea pipeline was set up the in the Gulf of Mexico in 1954, the world have seen the total length of implemented subsea pipelines reached hundreds of thousands kilometers over the last 60 years [1]. This number is growing at high speed. With subsea pipelines exposed to complicated hydrodynamic and geomorphic environment, it is a difficult problem to maintain the integrity of pipeline systems effectively, for both researchers and engineers. In order to achieve this goal at minimum costs, limited preventative action is better than major repair. And the formal is based on a feasible monitoring system. Among the many objectives of monitoring system of subsea pipelines, scour monitoring stands as a crucial one. The subsea pipelines are typically embedded into the seabed with a depth of around 1–1.5 m. However, hanging sections come into being under the lasting corrosion of vibrant waves and currents. Once the hanging length exceeds the design limit, high stress and fatigue stress

∗ Corresponding author. Tel.: +86 411 84706443; fax: +86 411 84706414. E-mail address: [email protected] (X. Zhao). http://dx.doi.org/10.1016/j.ijleo.2014.06.018 0030-4026/© 2014 Elsevier GmbH. All rights reserved.

can occur and lead to the failure of the whole pipeline system. According to Arnold’s statistical analysis on the subsea pipeline failure in Mississippi River delta during 1958–1965, scour and seabed movement consisted of the major causes [2]. Similar conclusion was reached through Demar’s analysis on the pipeline failure in the Gulf of Mexico during 1967–1975 [2]. Consequently, over the last several years, many researchers conducted extensive research and proposed several methods to solve the problem of scour monitoring. Jin et al. successively proposed to employ distributed optical fiber sensors to detect free spans through natural vibration frequency [3] and a novelty detector of abnormity in strain signals [4] of pipelines. Job and Hawkins [5] discussed related topics in free span monitoring based on the actual dynamic responses monitored by accelerometers. Elshafey et al. [6] proposed to use surface strain variation to detect free spans, which was tested through a set of experiments and finite element simulation. Yet, while significant progress has been made, the above solutions are all based on indirectly measuring free spans vibration or strain, which renders these methods limited by high construction difficulty and low feasibility when the vibration is small. To provide a more practical solution, we [7,8] provided a scour monitoring system based on active thermometry. The distinct heat transfer mechanisms between in solids and in liquids lead to different patterns of temperature change in the heating and cooling processes. These patterns could be easily recorded by temperature sensors deployed parallel to pipelines and in return used

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Fig. 1. System overview.

for monitoring scour. According to the different need of landfall section and long distance pipelines, we employed DS18B20s and distributed Brillouin optical sensors as the temperature sensors of the scour monitoring system, respectively. Good experimental results were obtained. The methodology does not require the monitoring system to be mounted on the surface of pipes and hence saves many construction problems and makes it highly applicable to practice. However, the previous studies were either based on simplified heat conduction models [7] which required further verification of field experiments or adopted a verification algorithm [8] which could hardly be directly used in real application. In this paper, we introduced artificial neural network (ANN) to accomplish the key step of active-thermometry-based scour monitoring, i.e. surrounding media identification. It is a proper supplement to the previous studies and also necessary preparation for practical application. Stemming from the multidiscipline study of physics, psychology and neurophysiology, the ANN possesses great capability of nonlinear approximation and pattern recognition and is widely used in many fields across mathematics, science and engineering, as well as medical science, finance and national defense [9]. As a very important innovation in ANN, the back propagating (BP) algorithm was first proposed by Paul Werboss [10] and independently discovered and promoted afterwards by David Rumelhart, Geoffrey Hinton and Ronald Williams [11], David Parker [12], and Yann Le Cun. Currently the BP-based multilayer perceptron is one of the most widely used neural networks [13]. With properties such as large scale parallel distributed processing, fault-tolerance, self-organized learning and self-adaptivity, it is suitable to process the massive amount of data in scour monitoring of long distance pipelines and expected to achieve good performance in front of the possible errors and weak recognizability in practical application. This paper employed BP neural network to process the monitoring data and implement pattern recognition. What follows in this article starts with the composition and experimentation of the active-thermometry-based scour monitoring system, then explains principle of scour monitoring using BP neural network, and lastly presents the results and analysis, which demonstrated the feasibility and advantages of BP neural network used in scour monitoring. 2. Materials and experiments The same setup of scour monitoring system and experimentation was constructed as in [7]. As shown in Fig. 1, the system had three major components: a thermal cable with an external power supply of AC 220V, a Data Acquisition Unit (DAU), which matched the sensors used in the thermal cable, and a Data Processing Unit (DPU), whose role was played by a computer. The thermal cable was the key sensing unit. It is 21 m long and the outer cross-section dimension was 12 mm × 15 mm. It was composed of a self-regulating heating belt, sixteen DS18B20 temperature sensors and hot pyrocondensation pipes. The heating belt

Fig. 2. Experimental Setup.

(2 mm H × 10 mm W) was made of metal heating wires, ameliorative polyolefin and insulation material layer and generated heat at a maximum power of 25 W/m. The sixteen DS18B20 temperature sensors were glued with 502 glue on the heating belt, with 3 m from the ends of the belt and 1 m spacing between them (Fig. 1: The Layout Scheme). The operating temperature range was −50 ◦ C–125 ◦ C and the accuracy was ±0.5 ◦ C. Temperature was sampled nearly every 10 s. The main wire of the thermal cable consisted of three lines, two power supply lines (L1 and L2) for the heating belt and DS18B20s, respectively, and one data line (L3) for DS18B20s (Fig. 1: The Inner Make Up). After all lines were carefully connected, the hot pyrocondensation pipes were used to package the heating belt and the distributed DS18B20s. The pipes were made of radiation cross linked polyethylene and could protect the heating belt and DS18B20s from water. The data lines (L3) of the thermal cable were connected to the DAU, which was acted by the STA-D Series DS18B20 remote digital temperature acquisition unit developed by Beijing Sailing Technology Company. The acquired temperature data could thus be converted to digital format and transmitted to a computer (DPU) through an USB interface. The computer stored and analyzed the monitoring data. A small-scaled seabed environment was constructed in the laboratory as shown in Fig. 2. A 21 m long section was partitioned from a 48 m long water channel (1 m wide and 1.5 m high) with brick walls (0.6 m high) blocking both ends. And therein a controllable water cycle could be created. Three steel tubes (6 m long, 2.5 mm thick and 100 mm diametric) were welded end-to-end and formed an 18 m long steel tube. Placed in the middle of the separated water channel section (20 cm to the bottom), the tube acted as a submarine pipeline (Fig. 2(a)). Secured with custom-made hooks, the thermal cable ran parallel to the tube with each end of the cable extending 1.5 m past the ends of the tube. In order to create a scenario with a

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free span of submarine pipeline, the 21 m water channel was partitioned into three sections (Fig. 2(b)): two outer sections (roughly 7 m long), which were filled with 50 cm high sands, and a middle section (6 m long), which was devoid of sand and used to simulate the free span. Three experimental tests had been conducted as the same as in [7,8]. Before tests, the sands should be fully saturated to approximate the real sediment condition of pipelines. Hence water had been continuously added into the water channel to maintain a constant water level of 70 cm for 2 h (Fig. 2(d)). Then tests began as follows: Firstly, turn on the computer; connect L2 to the power; the 16 DS18B20s start to record temperature, lasting for 10 min. Then connect L1 to the power; the heating belt starts to generate heat. It is the heating period. When it reaches the amount of time for heating, disconnect L1. Keep DS18B20s sampling and it is the cooling period. Lastly when tests finish, disconnect L2. Following the steps, three tests of different time periods had been conducted. The first test consisted of 2 h of heating and 1 h of cooling. Both the second and the third tests consisted of 3 h of heating and 1 h of cooling. The room temperature was recorded every 30 min throughout the tests. 3. Method The basic idea of this method was to use BP neural network to recognize the surrounding media of pipelines through the temperature time histories acquired by the temperature sensors within the thermal cable, which showed distinct patterns during the heating and cooling process due the different mechanisms and coefficients of heat transfer between in-water and in-sand scenarios. Subsequently, the free spans lengths could be quickly figured out based on the spatial distribution of sensors and their corresponding media identification outcomes. Within different media, heat conducts in different modes and thus the temperature changes according to different patterns in heating and cooling process. The mode of heat transfer in sediments is heat conduction. Our problem was idealized as an infinite line source in an infinite, homogeneous, isotropic medium. By using “Transient heat method” [14,15], for large value of t(t  r2 /(4˛) and t − t1  r2 /(4˛), respectively), we could obtain:

  ⎧ q 4˛ ⎪ t ≤ t1 ⎨ T = 4 ln t + ln r 2 −  ⎪ t q ⎩ T =

4

ln

t − t1

(1)

t > t1

where T is the excess temperature; T = T − T0 ; T0 is initial temperature;  the Euler’s constant ( = 0.5772); q the quantity of heat released per unit length of the line source during heating, which starts at 0 and stops at t1 ; a the thermal diffusivity of the solid (˛ = /c); , , c the thermal conductivity, the density and the specific heat of the solid, respectively. r the distance to line source; t1 the time heat stops. For free spans, the pipelines and thermal cables are surrounded by seawater and the mode of heat transfer is convection. Since the cross-section area of the thermal cable was small, its thermal resistance was neglected. By using lumped parameter method [7], the analytical temperature solution is:

  t  ⎧ q ⎪ t ≤ t1 ⎨ T = hA 1 − exp − tc  t−t  ⎪ 1 ⎩ T = (T (t1 ) − T0 ) · exp −

c

(2) t > t1

where  c = cV/hA is called the time constant. h is the convective heat transfer coefficient;  and c are the density and the specific

Fig. 3. Neural network.

heat, respectively; and A and V are the convective area and volume per unit length of the thermal cable, respectively. Through observation of Eqs. (1) and (2), obvious difference of temperature curves can be found between in-sediment and inwater scenarios. The right end of Fig. 3 shows this difference with typical temperature curves of both in-sediment and in-water scenarios. For conduction in sediment, heat transfers relatively slowly; the temperature changes logarithmically: it increases and decreases at a decreasing rate in the periods of heating and cooling, respectively; and it takes longer to reach a stable temperature in both periods. Differently for convection in water, heat exchanges quickly; the temperature changes exponentially: in heating period it rockets to a plateau and stays stable, and in cooling period, it plummets to the initial temperature. Based on the distinction, a BP neural network was used for pattern recognition. In application, the temperature time histories acquired by thermal cable constitute two set of curves characterized by different changing patterns. These two curves sets correspond to the media of sediment and water, respectively. A two-layer BP neural network is of great capability of pattern recognition and able to identify the media through temperature curves. Hagan et al. [14] pointed that combination of multilayer perceptron and back propagating learning rules breaks the limitation of linear separability for single-layer perceptron and can solve any classification problems. What is more, a two-layer neural network is capable of approximating any practical functions as long as the hidden layer has enough neurons and the transfer function is sigmoid function. Therefore, a two-layer neural network and Tan-Sigmoid nonlinear function were adopted in this study. The Tan-Sigmoid function is f (x) =

2 −1 1 + e−2x

(3)

Fig. 3 shows the structure of the employed neural network. m is the number of neurons in the hidden layer, n is the dimension of the normalized temperature time histories T, and T is the inputs of the network. IW1, 1 , b1 , LW2, 1 , b2 are the weights and bias for hidden layer and output layer, respectively. Since only two patterns, i.e. in-sediment and in-water, need to be recognized, the output vector ␣2 is a two-dimensional vector, [1,0] denotes the insediment and [0,1] denotes in-water. As a steepest descent method to achieve least mean squared errors, BP algorithm calculates the derivatives of the weights and bias by chain rule, which is very slow. Thus the conjugate gradient back-propagation method was used to improve the computing efficiency. Before training, a scaling function was used so that the inputs and targets always fall in the range of [−1,1] and [0,1], respectively. The scaling function is: y=

x − xmin (ymax − ymin ) + ymin xmax − xmin

(4)

After the surrounding media throughout the thermal cable have been recognized as water or sediment by neural network, free spans lengths can be easily obtained according to the spatial distribution of the temperature sensors along the thermal cable, which runs along the pipelines. Sensors recognized as in-water belong to the free span sections. Therefore, the free spans lengths are the lengths including all the consecutive temperature sensors recognized as

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30

Excessive Temperature Δ T (ºC)

Temperature T (ºC)

50 45 Point 1-5, 12-16 In Sediment

40 35 30

Point 6-11 In Water

25 20 0

5429

2000

4000

6000

8000

10000

25 20 Point 1-5, 12-16 In Sediment

15 10 5

Point 6-11 In Water

0

12000

Time t (s)

-5

0

50

100

150

200

250

300

200

250

300

(a)

Fig. 4. The temperature time history acquired by 16 DS18B20s in the first test.

4. Results Fig. 4 shows the temperature curves acquired by the 16 DS18B20s in the first test, which including 2 h of heating and 1 h of cooling. Every curve in the figure is constituted of 800 temperature values sampled throughout the experiment. As demonstrated in the last section, the curves fall distinctively into two groups, corresponding to in-sediment and in-water scenarios, respectively. Number the 16 DS18B20s in order from Point 1 to 16. Points 1–5 and 12–16 are buried in sediments and correspond to the upper set of the curves, and Points 6–11 are immersed in water and correspond to the lower set of the curves. 4.1. Normalization Before running the neural network to implement pattern recognition, it is necessary to normalize the inputs. This includes two steps: to obtain the excessive temperatures and to normalize the dimensions of the input temperature sequences. First, the excessive temperatures T should be obtained by subtracting the initial temperature of corresponding points before heat began. As demonstrated by Eqs. (1) and (2), it is the excessive temperature T that is directly affected by heat transfer mechanisms. It thus can be used to deduce the discrimination of in-sediment and in-water scenarios. The acquired absolute temperature also depends on the initial temperature, which might be very different over different sensing points, especially in practical application. Therefore, to exclude the possible effect of the initial temperature, they had been subtracted and the excessive temperature was to be sent into the neural network. Second, the temperature time sequences were normalized to share a fixed length through interpolation. The specific structure of the neural network is related to the input dimensions. However, in order to achieve good performance in different conditions of real application, the monitoring system might need to run for different time periods, which produce data with different lengths. Thus, normalization is required to suit a universally applicable neural network. Furthermore, the possible data in real application might be too massive to be directly used for computing. According to our experience, the acquired data were highly redundant for the problem of patterns recognition. Interpolation decreased the amount of data to be stored and computed, enhanced the recognition efficiency, and hence reduced the cost of the monitoring system. Generally, it is better when the normalized length is smaller since in that case the computed data are small. But the characteristics

Excessive Temperature Δ T (ºC)

30

in-water. The accuracy of free spans detection for the scour monitoring system is the spacing of the temperature sensors.

25 20 Point 1-5, 12-16 In Sediment

15 10 5

Point 6-11 In Water

0 -5

0

50

100

150

(b) Fig. 5. Normalized temperature curves (a) in the first test; (b) in the second test.

of the temperature curves might be missing after interpolation. So there is a balance to be made and the normalized length cannot be too small. In this study, it was chosen to be 300, including 200 temperature samples in the period of heating and 100 in the period of cooling. It worked fine, but needs careful thoughts and further studies in real application. How to implement normalization using proper data mining techniques is a further topic. Fig. 5 shows the normalized temperature curves for the first and second test. Different from the first test, the second consisted of 3 h of heating and 1 h of cooling. Even though this difference existed, the patterns and the differences of patterns are the same between in Fig. 5(a) and (b). This indicates the possibility of a universally applicable neural network. 4.2. Pattern recognition results A two-layer BP neural network as shown in Fig. 3 was used to implement pattern recognition upon the normalized temperature sequences. More specifically, the hidden layer contained 10 neurons (m = 10) and the normalized input length is 300 (n = 300). Firstly the data of the second test were used to train the network. Then the data of the first and third tests, which had different heating time periods, were sent to the trained network to check the performance. Fig. 6 is the confusion matrix shows the results of pattern recognition of the three tests including the training one. The horizontal axis (Target Class) is the true outputs according to the experimental setup; the vertical axis is the outputs produced by the network. Due to the clear difference of temperature changing patterns, the network outputs were very accurate, as you can see

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the simulation results. The horizontal axis is the percentage of the noise level. It ranges from 1% to 15% and increases in 0.5% in every step. The four curves are the RMS, the accuracy for in-sediment scenario, the accuracy for in-water scenario and the overall accuracy, respectively. It can be seen that the neural network had very good anti-noise capacity. The accuracy of in-water scenario is 100% accurate throughout the simulation within 15% noise level. While accuracies of in-sediment scenario are lower, it reaches up to at least 80% within 8% noise level. Considering the small sampling number in our experiments used for network training, the outcome is very impressive. When large number of samples is employed in practical application, better performance is expected. This is a preliminary study with scope for further research. Some examples are: more experiments with bigger running time differences and various free span lengths; field experimental studies and validation; data mining techniques and noise filtering methods integrated into the neural network; real-time neural network implementing pattern recognition during heating.

Output Class

Confusion Matrix Without Noise

S

30 62.5%

0 0.0%

100% 0.0%

W

0 0.0%

18 37.5%

100% 0.0%

100% 0.0%

100% 0.0%

100% 0.0%

S

S: In-Sediment W: In-Water

W Target Class

Fig. 6. The confusion matrix without noise. 1

100

0.8

80

0.6

60

RMS

RMS

In-sediment Accuracy In-water Accuracy

0.4

40

Overall Accuracy

0.2

0 0

Recognition Accuracy

%

5. Conclusions

20

2

4

6

8

Noise Level

10

12

14

0

16

%

Fig. 7. The effect of noise on performance of the neural network.

by the high numbers of correct responses in the green squares (the principle diagonal) and the low numbers of incorrect responses in the red squares (0, the second upper anti-diagonal). The lower right blue squares illustrate the overall accuracies (100%). The lower left and lower middle denote the recognition accuracies for temperature sensors in sediment and water, respectively, which are later used in Fig. 7. Furthermore, the network’s performance was quantified using the mean of squared errors, which was calculated as follows. RMS =

 (t − ˛ )T (t − ˛ ) i i i i i

(5)

n

where ˛i = ˛2 and t1 (ti ∈ {[1 0]T , [0 1]T }) are the outputs and the corresponding targets of the ith input. n is the total number of the elements of the outputs. In this case of 16 inputs in one test and two elements in every input, n = 32. The RMSs of the three tests were shown in Table 1. The performance was very satisfying with a magnitude order of 10−8 . With these results, we believed that the trained neural network well presented the difference of heat transfer between in-sediment and in-water scenarios. It was thus expected to function well in practical application. To demonstrate the robustness of the neural network, the monitored data of the first test were added with different Gaussian noises and sent as inputs to the trained neural network. Fig. 7 shows Table 1 The RMS without noise. Test

RMS (×10−8 )

One Two Three

5.80 5.86 6.25

A two-layer BP neural network was employed as an important component in the active-thermometry-based scour monitoring system of subsea pipelines. It was used to realize media recognition through the normalized temperature time histories. Analysis results of three experiments demonstrated the feasibility of the BP neural network used in the scour monitoring system. Without the need of a physical model, the trained BP neural network well presented the distinction of heat transfer between in-sediment and in-water scenarios and accurately produced outputs of pattern recognition for different heating time. Furthermore, the effect of noises on the network’s performance was discussed through simulation. The results showed very good anti-noise ability of the network. To conclude, the BP neural network presented promising potential with great capability of pattern recognition, universal applicability and robustness in the application of the scour monitoring system. Acknowledgments This research was financially supported by National Basic Research Program of China (2011CB013702), Key Projects in the National Science & Technology Pillar Program during the Twelfth Five-Year Plan Period (2011BAK02B02), National Science Foundation of China (5092100), “863 programs”-National High Technology Research and Development Program (2008AA092701-6) and the Science Fund for Creative Research Groups from the National Science Foundation of China under grant no. 51221961. References [1] W. Jin, E. Zhang, J. Shao, D. Liu, Cause analysis and countermeasure for submarine pipeline failure, Bull. Sci. Technol. 20 (6) (2004) 529–533. [2] J.B. Herbich, Offshore Pipeline Design Elements, Marcel Dekker Inc., New York, 1981. [3] W. Jin, J. Shao, E. Zhang, Basic strategy of health monitoring on submarine pipeline by distributed optical fiber sensor, in: Proceedings of the 22nd International Conference on Offshore Mechanics and Arctic Engineering (OMAE03), 2003, pp. 531–536. [4] W. Jin, J. Shao, A practical algorithms on health monitoring of submarine pipeline and its application, in: Proceedings of the 25th International Conference on Offshore Mechanics and Arctic Engineering (OMAE2006), 2006, pp. 309–313. [5] P. Job, M. Hawkins, In situ vibration monitoring of pipeline free spans, in: Proceedings of the ASME 27th International Conference on Offshore Mechanics and Arctic Engineering (OMAE2008), 2008, pp. 351–360. [6] A.A. Elshafey, M.R. Haddara, H. Marzouk, Free spans monitoring of subsea pipelines, Ocean Syst. Eng. 1 (1) (2001) 59–72.

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