Development of smart sensors system for machine fault diagnosis

Expert Systems with Applications 36 (2009) 11981–11991

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Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa

Development of smart sensors system for machine fault diagnosis Jong-Duk Son a, Gang Niu a, Bo-Suk Yang a,*, Don-Ha Hwang b, Dong-Sik Kang b a b

School of Mechanical Engineering, Pukyong National University, San 100, Yongdang-dong, Nam-gu, Busan 608-739, South Korea Power Facility Diagnosis Research Group, Korea Electrotechnology Research Institute, Changwon, Gyungnam 641-120, South Korea

a r t i c l e

i n f o

Keywords: Smart sensors Fault diagnosis Induction motor Performance comparison

a b s t r a c t Machine fault diagnosis is a traditional maintenance problem. In the past, the maintenance using tradition sensors is money-cost, which limits wide application in industry. To develop a cost-effective maintenance technique, this paper presents a novel research using smart sensor systems for machine fault diagnosis. In this paper, a smart sensors system is developed which acquires three types of signals involving vibration, current, and flux from induction motors. And then, support vector machine, linear discriminant analysis, k-nearest neighbors, and random forests algorithm are employed as classifiers for fault diagnosis. The parameters of these classifiers are optimized by using cross-validation method. The experimental results show that smart sensor system has the similar performance for applying in intelligent machine fault diagnosis with reduced product cost. Developed smart sensors have feasibility to apply for intelligent fault diagnosis. Ó 2009 Elsevier Ltd. All rights reserved.

1. Introduction Condition monitoring is a mean to prevent catastrophic failure of critical machinery and a maintenance scheduling tool that uses vibration, infrared or acoustic emission data to determine the need for corrective maintenance action (Jardine, Lin, & Banjevic, 2006; Mobley, 1990). By using the advantages of wireless technology, wireless sensing shows great value in application especially to where access is difficult or cabling is impossible. Wireless data can be effectively implemented in extensive applications (McLean & Wolfe, 2002; Spencer, Ruiz-Sandoval, & Kurata, 2004). Several sectors of industrial application such as automotive, production automation process, robotics, and aeronautic, etc need sensors provided the local computation capabilities which include microprocessor, microcontroller and digital signal processing (Ziani, Bennouna, Amamou, & Barboucha, 2000). Recent advances in the development of microcontroller and MEMS sensors have made realization of smart sensor in combining with the signal processing circuitry possibility. Furthermore, they are going to have a significant impact on a variety of application such as industrial process automation. Usually, traditional integrated sensor can be divided into three parts (Ramamurthy, Prabhu, & Gadh, 2004): sensing element (e.g. resistors, capacitor, transistor, piezoelectric materials, and photodiode), signal conditioning and processing (e.g. amplifications, linearization, compensation, and filtering), and a sensor interface (e.g. * Corresponding author. Tel.: +82 51 629 6152; fax: +82 51 629 6150. E-mail address: [email protected] (B.-S. Yang). 0957-4174/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2009.03.069

the wires, plugs and sockets to communicate with other electronic components). However, the smart sensor is expected to have the capability that functionality and architecture as well as raw data acquisition are based on the existence of micro-processing unit. Current wireless smart sensor researches are performed in structural health monitoring. The purposes of development of this type of sensors are network and scalable, energy-saving, smart and programmable, capable of fast data acquisition, reliable and accurate over the long term, cost little to purchase and easy-to-install (Corsi, 2007; Yazdi, Mason, Najafi, & Wise, 1996). In recent years, several signal analyzers have been developed with high performance and functionality. But maintenance costs of them are expensive for applying on common machine. Instead, smart sensors can be used for maintenance strategy with low cost. Intelligent classification systems have been employed to assist the condition monitoring tasks by correctly interpreting the fault data. Different classification techniques are often used for identifying samples as different groups based on the data generated by various sensors. Based on the comparing of classification, we evaluate the relative performances between conventional system and smart sensor system. In this paper, a smart sensor system has been developed for machine condition monitoring and fault diagnosis. The performance of smart sensor system for fault diagnosis is compared with that of conventional sensor system. Three types of signals are analyzed in induction motors diagnosis. Four classifiers are used for fault diagnosis. The paper is organized as follows. In Section 2, we introduce the vibration, current, flux based smart sensors. Section 3

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describes a configuration of fault diagnosis system. And background knowledge of feature calculation, extraction and classification. And next, an experiment of induction motor diagnosis is introduced in Section 4. Section 5 compares classification performance of each system. At last, conclusions and suggestions are offered in Section 6 based on the above evaluation results.

2. Smart sensors 2.1. Configuration of smart sensors The developed smart sensor consists of four modules: sensor, interface, server, and fault diagnosis module. The input part

Table 1 Specification of wireless smart sensor. CPU

Other specification

Conventional system

 Calculation: 32-bit floating-point arithmetic computations

 ADC: 24-bit resolution with anti-aliasing  Max Sampling freq.: 42 kHz  Real-time data analyzer

CPU(ATmega128)

Wireless bridge

Smart sensor system

 Communication: UART

    

 Embedded: ISP, JTAG  Memory: 4K EEPROM  Clock: 16 MHz

The other specification

Name: Wiport Company: Latronix Networking: 802.11 b Communication speed: 230,400 bps Security: IEEE 802.11i-PSK, WPA-PSK,TKIP

   

Time, FFT Display Data Receive Data Synthesis

Diagnosis Algorithm (Matlab)

Server

Fault Diagnosis Base Station

Acceleration

WLAN communication

Current Flux

Interface Module

Mux

WLAN Module

SD Memory Card

HPF AMP

A/D Converter

LPF

(16bit Resolution)

Memory

Packet Data

Sensor Module

CPU

External DC Power Fig. 1. System diagram of smart sensor system.

• Setting START (Power ON)

• Recognize Smart Sensors

Gain, Sampling Rate Wireless Transmission

• Signal Condition and A/D Conversion • Making Packet Data and Response to Base Station

Fig. 2. Flowchart smart sensors.

• Synthesis Packet Data • Time, FFT Display

END (Power OFF)

ADC: 16 bit SD-memory: 1 Gbyte Max sampling freq.: 8192 Hz Power input: 5 V DC

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J.-D. Son et al. / Expert Systems with Applications 36 (2009) 11981–11991 Table 2 Definition of packet information. Start bit

Header

Server

Sensor ID

Frequency range

Gain

End bit

Change line

(a) Base-station $

PKW

S

01  99

18

03

0  0D

0  0A

Start bit

Header

Client

Sensor ID

Gain

A/D data

End bit

Change

(b) Smart sensor $

PKW

C

01  99

03

[1024]

0  0D

0  0A

Fig. 4. Apparatus for accuracy test of smart sensor signal.

Fig. 3. Current, vibration and flux based smart sensors.

consists of four channels. Signal output level of accelerometer, current, and flux sensor within ±2.5 V. The signal is filtered by high pass filter (HPF) and low pass filter (LPF) that are included in chip

circuit element. An 8 bit processor of ATmega128 is employed to controls 16 bit analog digital conversion (ADC), gain and wireless communication. SD-memory card is used for data backup when wireless network break down. Smart sensor checks the network status in real-time. The internal communication of data is RS-232 from memory to wireless module. The maximum baud rate is 115,200 bps. Base-station is divided into two parts: server and fault diagnosis. The detail specification of the smart sensor is presented in Table 1 and Fig. 1. The server is used to synthesize and receive the data before fed into data analysis part. Fault diagnosis part is addressed to perform signal processing in time domain, frequency domain, and calculation of fault diagnosis algorithm. The data communication of the system is based on two-way communication depicted in Fig. 2. When the power of smart sensor is turned on, base-station can recognize the smart sensor that has a natural IP address. Base-station sets the information of setting value that is gain and frequency range. Then, it sends the setting information to smart sensor. Next, smart sensor acquires signal according to the information and sends it into base-station, vice versa. The definition of communication packet is shown in Table 2. Finally, base-station displays the time waveform and fast Fourier transform (FFT) spectrum, which are saved as ASCII format data. The fault diagnosis procedure is an off-line calculation module using Matlab program. Packet information is based on National Marine Educators Association (NMEA) shown in Table 2. Symbol ‘‘$” means the start bit of signal. ‘‘PKW” is a simplified character of Pukyong National University. ‘‘S” and ‘‘C” is character of server and client. Sensor ID can extend until 99 number. Frequency ‘‘1  8” means data acquisition frequency from 50 to 3200 Hz. Gain ‘‘0  3” is amplification of analog signal from 0 to 30 dB. And ‘‘0  0D, 0  0A” means end bit.

Table 3 Specification of sensors. Accelerometer

Current

Flux

 Company: B&K 4371  Frequency range: 12.6 kHz  Sensitivity: 100 mV/g

 Company: Tektronix A621  Frequency range: 5 Hz  50 kHz  Sensitivity: 100 mV/A

 Output impedance: 50  Coil: 3 turn

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Fig. 5. Results of low pass filter.

2.2. Validation of signals The developed smart sensors embedded with current, vibration and flux are made as shown in Fig. 3. It has 4-channel input sup-

plied by DC 5 voltage (V) power. And the specification is shown in Table 3. Even though smart sensor is cheaper than conventional system, but it need verification of data reliability, which is the main objection in our experiment. Therefore, we employed response of white noise for filter test and response of function generator analog input for signal accuracy test. LPF filter is designed under 3200 Hz using 8 order filtering microchip. This experiment was employed by using white noise input of ±1 V supplied to this circuit and the response signals were averaged 1000 times. Fig. 5 shows the filtered frequency level that is 43 dB. It is lower than base signal level 40 dB. To verify the ADC of smart sensor signal, we use function generator input on smart sensors in Fig. 4. Function generator made ±1 V and 100 Hz signal and amplification levels were 0, 10, 20, and 30 dB. Command PC and conventional analyzer receive ADC data and plots the time and FFT using Matlab. Different levels of amplification were shown in Fig. 6, and the dB scales were displayed in Fig. 7. Data acquisition time of two systems is different. Therefore, the phase differences are occurred in time waveform. The background noises in the smart sensor are more than conventional analyzer in dB scale. The range of noise levels were from 56.6 to 122 dB.

Conventional anlayzer Smart sensor

1.5

1 0.8

0.5

A m plitude(V )

A m plitude(V )

1

0 -0.5

0.6 0.4

-1

0.2 -1.5 0

0.01

0.02

0.03

0.04

0

0.05

0

X: 100 Y: 0.03448

500

1000

1500

2000

2500

3000

3500

4000

3000

3500

4000

Frequency(Hz)

Time(s)

(a) 0 dB Conventional analyzer Smart sensor

1.5

X: 100 Y: 1.1

1

0.5

A m plitude(V )

A m plitude(V )

1

0 -0.5

0.8 0.6 0.4

-1 0.2

-1.5 0

0.01

0.02

0.03

0.04

0.05

0

0

500

1000

Time(s)

1500

2000

2500

Frequency(Hz)

(b) 30 dB Fig. 6. Time waveform and frequency spectrum of ADC data.

J.-D. Son et al. / Expert Systems with Applications 36 (2009) 11981–11991

Fig. 7. dB scale FFT of ADC data.

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Report of Result Fault Diagnosis

Test

SVM RF k-NN LDA

Cross Validation (R>Max)

Data Training

Training

Test

Training

RMS

Kurtosis

Division of Data

Validation

Feature Extraction

KPCA

AR

Classification , Parameter Optimization

Crest Factor

Shape Factor

Calculation of Feature (21)

Database

Save Data

Data Server

Receive Data

Fig. 8. Flowchart of fault diagnosis algorithm.

Table 4 Feature calculation information. Sensor type

Position

Features of signals Time domain (10)

Frequency domain (3)

Auto regression (8)

Vibration (acceleration)

Vertical Horizontal

 Mean  RMS

 Root mean square frequency

 AR coefficients ða1  a8 Þ

Current

Phase A Phase B Phase C

 Shape factor  Skewness  Kurtosis

 Frequency center  Root variance frequency

Flux

1 2

    

Crest factor Entropy error Entropy estimation Histogram lower Histogram upper

3. Theories of fault diagnosis The fault diagnosis system flowchart is shown in Fig. 8 (Widodo & Yang, 2007; Widodo, Yang, & Han, 2007). Server module receives data from wireless smart sensors. It then saves ASCII format file. Saved data was calculated, and 21 features shown in Table 4 are

calculated from each channel. The features need feature extraction because many features occur in calculation time delay and accuracy decrease. Extracted features are divided into training, validation, testing data sets. Training procedure contains crossvalidation. This method performs the optimization of parameters of classifiers. Finally, diagnosis results are reported.

J.-D. Son et al. / Expert Systems with Applications 36 (2009) 11981–11991

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3.1. Feature extraction Kernel principal component algorithm (KPCA) is one approach of generalizing linear PCA into nonlinear case using the kernel method (Cao, Chua, Chong, Lee, & Gu, 2003; Choi, Lee, Lee, Park, & Lee, 2005). If a PCA is aimed at decoupling nonlinear correlations among a given set of data (with zero mean), xj 2 Rm , j ¼ 1; . . . ; N through diagonalyzing their covariance matrix, the covariance can be expressed in a linear? Feature space F instead of the nonlinear input space, i.e. the idea of KPCA is to firstly map the original input vectors xj into a high-dimensional feature space Uðxj Þ and then to calculate the linear PCA in Uðxj Þ. The linear PCA in Uðxj Þ corresponds to a nonlinear PCA in xj . By mapping xj into Uðxj Þ whose dimension is assumed to be larger than the number of training samples l, KPCA solves the eigenvalue problem:

e j; kj uj ¼ Cu

j ¼ 1; . . . ; N;

(a) Accelerometer

(b) Current sensor

ð1Þ

e ¼ 1 PN Uðxj ÞUðxj ÞT is the sample covariance matrix of where C j¼1 N e uj is the correUðxj Þ. kj is one of the non-zero eigenvalues of C. sponding eigenvector. Eigenvalues k P 0 and u 2 F n f0g. The u along with the largest k obtained by Eq. (1) become the first PC (Principal Component) in F, and the u along with the smallest k become the last principal component.

(c) Flux sensor Fig. 10. Vibration, current, flux sensors.

3.2. Parameter optimization (cross-validation) Cross-validation method introduced by Stone (1974) takes a more sophisticated approach to just one feature set. In k-fold cross-validation, the dataset is randomly partitioned into k disjoint blocks (the folds), of (approximately) equal size d ðd  N=kÞ. The learning algorithm runs k times. In the ith time, the ith training set is formed by the initial dataset without the ith fold, while the test set is formed using the ith fold alone. Let ^ hi be the ratio of classified instances to the total number of tested instances in the ith run. The estimator ^ hk of the accuracy for the k-fold cross-validation P hi =k. method is calculated as 1 ^ hk ¼ ki¼1 ^ Fig. 11. Installation of accelerometer, current, and flux sensors.

3.3. Classification Support vector machines (SVM) (Cristianini & Taylor, 2000; Vapnik, 1995; Vapink & Chapelle, 1999) is a supervised learning method used for classification and regression based on statistical learning theory. This classifier is implemented by mapping the training data into a feature space and the aid of kernel function. It separates the data using a large margin hyperplane (Cristianini & Taylor, 2000). For two-class data set, we examine a hyperplane that separates the data points ‘‘neatly”, with maximum distance to the closest data point from both classes – this distance is called the margin. The vectors that are closest to this hyperplane are called the support vectors. By applying a nonlinear kernel function that transforms data points into high-dimensional feature space, SVM can also treat nonlinear classification problem. Some common

kernels include: polynomial, radial basis function (RBF), linear, and sigmoid. According to the different classification problems, the different kernel functions can be selected to obtain the optimal classification results. Linear discriminant analysis (LDA) (Duda, Hart, & Stork, 2000) projects features from parametric space onto feature space through a linear transformation w. Suppose that we have a set of n d-dimensional samples x1 ; . . . ; xn , n1 in the subset D1 labeled x1 and n2 in the subset D2 labeled x2 . If we perform a linear combination of the components of x, the scalar dot product y ¼ wT x can be obtained. This method computes a hyperplane in the input space that minimizes the within-class variance and maximizes the between-class variance.

Fig. 9. Experimental apparatus for smart sensor system.

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k-nearest neighbors (k-NNs) (Duda et al., 2000) are a non-parametric classifier based on non-parametric estimation of the class densities. k-NN classifiers find k-nearest samples in some reference set, by taking a majority vote among the classes of these k samples. It turns out that it is the estimation of the posterior probability by

the proportions of the classes among the k samples. The aim is to find the nearest neighbors of an undefined test pattern within a hyper-sphere of pre-defined radius in order to determine its true class. Nearest neighbor methods can detect a single or multiple number of nearest neighbors. A single nearest- neighbor method ðk ¼ 1Þ is primarily suited to recognizing data where we have sufficient confidence in the fact that class distributions are non-overlapping and the features used are discriminatory. In most practical applications, however, the data distributions for various classes are overlapping and more than one nearest neighbors are used for majority voting. Random forests algorithm (RF) introduced by Breiman (2001) is a general term for ensemble methods using tree-type classifiers. RF builds a large amount of decision trees out of sub-dataset from a unique original training set by using bagging which is a meta-algorithm to improve classification and regression models according to stability and classification accuracy. Bagging reduces variance and helps to avoid over-fitting synchronously. This procedure extracts cases randomly from original training data sets and the bootstrap sets are used to construct each of the decision trees in the RF. Each tree classifier is named as component predictor. The RF makes decision by counting the votes of component predictors on each class and then selecting the winning class in terms of number of votes accumulated. So, the entire algorithm includes two important phases: the growth period of each tree and the voting period. The growth period is to train each decision tree classifier, and the sub-datasets are selected from whose training data set by using bagging random strategy. Then the test data is classified by majority voting. About one-third of the cases are left out of the bootstrap samples and not used in the construction of a particular tree. The samples left out of the kth tree are run through the kth tree to get a classification. In this way, a test set classification is obtained for each case in about one-third of the trees which can be used to assess the accuracy of the classifier.

Fig. 12. Experiment of smart sensors.

Table 5 Basic specification of the tested motor. Rotor speed Frame Bearing (DE) Bearing (NDE) Current Line frequency

1760 rpm 132M 6208zzc3 6208zzc3 28.2/16.3 A 60 Hz

Poles Weight Voltage Power Number of rotor bar Number of stator slot

4 70 kg 380 V 7.5 kW 28 46

Table 6 Description of faulty types of the tested motor.

4. Experiment

Faults types

Fault description

Bowed shaft Mass unbalance Misalignment Bearing fault Broken rotor bar Stator fault

Deflection 0.3 mm at mid-span 20 g at No. 1, 3 end ring 0.3 mm at bearing outer race Flaking brinelling 2 ea 10 short-turns

Normal

10

0 .5

3 0 2 0

5

0

-5

Amplitude(V)

Amplitude(A)

2

Amplitude(m/s )

In this section, an experiment is carried out, which employ smart sensors, instead of the conventional analyzer, to receive the ADC data. And base-station receives data by WLAN communication in Fig. 9. Three types of sensors were used in Fig. 10. Sensors installation is shown in Fig. 11. Accelerometers are installed on vertical and

1 0 0

0

-1 0 -2 0

1 -10 0 0

Bowed rotor

0 . 0 2

0 . 0 4

0 .0 6

0 . 0 8

0 . 1 T im e (s )

0 .1 2

0 . 1 4

0 .1 6

2. 20 0- 3

0 .1 8

0

0 .0 2

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0 .1 T im e (s )

0 . 1 2

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0 . 1 6

0 . 1 8

-5

0 . 0 2

0 . 0 4

0 .0 6

0 . 0 8

0 . 1 T im e (s )

0 .1 2

0 . 1 4

0 .1 6

0- 2. 20 0

0 .1 8

0 .0 4

0 .0 6

0 .0 8

0 .1 T im e (s )

0 . 1 2

0 . 1 4

0 . 1 6

Amplitude(A)

0 .0 6

0 . 0 8

0 . 1 T im e (s )

0 .1 2

0 . 1 4

0 .1 6

0- 2. 20

0 .1 8

0 . 0 8

0 . 1 T im e (s )

0 .1 2

0 . 1 4

0 .1 6

0 .1 8

0 . 2

0 . 0 2

0 . 0 4

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0 .1 T im e (s )

0 .1 2

0 .1 4

0 .1 6

0 .1 8

0 . 2

0 . 0 2

0 . 0 4

0 . 0 6

0 .0 8

0 .1 T im e (s )

0 .1 2

0 .1 4

0 .1 6

0 .1 8

0 . 2

0 . 0 2

0 . 0 4

0 .0 6

0 . 0 8

0 . 1 T im e (s )

0 .1 2

0 . 1 4

0 .1 6

0 .1 8

0 . 2

0 . 0 2

0 . 0 4

0 . 0 6

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0 .1 T im e (s )

0 .1 2

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0 . 2

0 . 0 2

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0 .1 T im e (s )

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0 . 2

0.02

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0

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- 00 .. 55 0 0 . 2

0 . 1 8

1 0 Amplitude(V)

Amplitude(A)

0

-5

0

0

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0

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0 . 0 4

0 .0 6

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0 . 1 T im e (s )

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-2 0 0 .2 20 0

0 .1 8

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0 .1 T im e (s )

0 . 1 2

0 . 1 4

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- 00 .. 55 0 . 2 0

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0 -5

Amplitude(V)

1 0

5

Amplitude(A)

2

-0 0 .. 5 5 0 . 2 0

0 . 1 8

0

2

Amplitude(m/s )

0 .0 2

Amplitude(V)

Amplitude(m/s )

2

0 . 0 4

1 0 Amplitude(m/s )

0 .0 6

-1 0

0 . 0 2

5

- 11 50

0 . 0 4

0

1 0

0

-5

Faulted bearing

0

-1 0

0

5

-1 1 0 0 0

Misalignment

Amplitude(V)

Amplitude(A)

0

1 0 0 -1

Rotor unbalance

0 . 0 2

1 0

2

Amplitude(m/s )

5

- 00 .. 55 0 0 . 2

0

0

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-1 0 -1 5

0

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0 .1 T im e (s )

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-0 .5 0 . 2 0

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Amplitude(V)

5

Amplitude(A)

2

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1 0

Broken rotor bar

0

0

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-1 0 - 1 05

0

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- 00 .. 55 0 . 2 0

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1 0

0

-5

-1 0

Amplitude(V)

Short-turn stator winding

Amplitude(A)

2

Amplitude(m/s )

5

0

0

-1 0

0

0.02

0.04

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0.12

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(a) Vibration

0.18

-2 0 0.2

0

0.02

0.04

0.06

0.08

0.1 Time (s)

0.12

0.14

0.16

0.18

-0 .5 0.2

0

(b) Current

Fig. 13. Time waveforms of acquired signals by smart sensors.

0.1 Time (s)

(c) Flux

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J.-D. Son et al. / Expert Systems with Applications 36 (2009) 11981–11991 0.26

0.16

0.11 0.108

0.25

0.155

0.106

0.24 0.15

Accelerometer

0.104

0.23 0.145

0.102 0 .02.1 6

0

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0.22

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00..2216

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Flux

0.22 0.21 0

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1

2

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0.21

0.148 7 0

6

1

(a) RMS

2

3

4

5

6

7

0.2

0

(b)Kurtosis

(c) Crest fact

Fig. 14. Features trends of acquired signals by smart sensors.

Original feature of smart sensor system

KPCA of smart sensor system

Bowed shaft Broken rotor bar Misalignment Bearing fault Mass unbalance Normal Stator fault

0.1 0.05

0.6

0.4

0

0.2

-0.05

0

-0.1

-0.2

-0.15 0.11

Bowed shaft Broken rotor bar Misalignment Bearing fault Mass unbalance Normal Stator fault

-0.4 0.4

0.108

0.05 0.106

0.2

0 -0.1

0.102

0.1 0

-0.2

-0.15 0.1

0.3 0.2

0

-0.05

0.104 -0.2

-0.1 -0.4

(a) Original features

-0.2

(b) Feature extracted using KPCA Fig. 15. Features extraction of smart sensors system.

Table 7 Optimized classifiers parameters using cross-validation. Classifier

Test range of parameter

SVM

   

LDA k-NN

No need  k: 1, 2, 3, 4

No need k¼1

No need k¼1

RF

 Number of trees = 400, 450, 500  Seeds = 123

 Number of trees = 400  Seeds = 123

 Number of trees = 400  Seeds = 123

Kernel function: linear, poly, RBF C: 1, 10, 100, 1000 Arg. = 23, 22, 21, 20, 21 Method: one against all

Selected parameters of conventional system     

Kernel function: RBF C=1 Arg. = 22 Method: one against all Cross-validation error: 0%

horizontal direction of induction motor. Current sensors are clamped 3-phase power input. Flux sensors are putted in induction motor stator slot. Two smart sensors are used to reduce data acquisition time as depicted in Fig. 12. Maximum sampling frequencies of two systems are 8192 Hz, and data number is 8192. Seven induction motors are tested in this experiment with specifications listed in Table 5. These tested motors are set to operate at full-load conditions with one load-motor. Among the seven tested motors, one is normal (healthy) which is used as a benchmark mo-

Selected parameters of smart sensor system     

Kernel function: RBF C=1 Arg. = 23 Method: one against all Cross-validation error: 0%

tor. The left are fault motors involved bowed rotor, rotor unbalance, misalignment, faulty bearing, broken rotor bar, and shortturn stator winding, respectively, as shown in Table 6.

5. Result of fault diagnosis Acquired time waveforms are shown in Fig. 13. Although the time series data contain abundant information, The real useful

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Table 8 Diagnosis accuracy of train and test set. System type

Accuracy rate of each classifier (%)

Conventional system

Train Test

Smart sensors system

Train Test

SVM

LDA

k-NN

RF

100 100

100 100

100 100

100 100

100 100

100 100

100 95.7

97.1 91.4

Table 9 Classification comparisons of each sensor. Sensor type

Validation type

Accuracy rate of each classifier (%) SVM

LDA

k-NN

RF

Train Test

100 100

100 100

100 100

100 100

Current

Train Test

100 100

100 100

100 100

100 100

Flux

Train Test

98.6 98.6

100 100

100 100

100 100

Train Test

85.7 82.9

100 100

100 100

100 80

Current

Train Test

91.4 91.4

100 100

100 100

100 87.1

Flux

Train Test

78.6 80

100 100

100 100

100 80

Conventional system Vibration (accelerometer)

Smart sensor system Vibration (accelerometer)

information often be buried due to noise signal. Hence feature calculation is imperative, which is introduced in Section 3.1. The calculated feature information of each sensor are shown in Table 4. Each sensor channel includes 21 features. Therefore, totally 147 features are collected from 7 channels. Feature trends of extraction are shown in Fig. 14. After feature extraction the features are better clustering than original features. After feature calculation, much unnecessary information also is contained. Therefore, the feature extraction is purposed for effectual estimation of conditions of machine. Feature extraction procedure is easy to make clusters from original features in Fig. 15. Next, a 10-fold cross-validation is conducted. Cross-validation methods are induced to optimize the classifier parameters. The optimized parameters are shown in Table 7. It can be seen that the differences are slight. Generally speaking, the bias of results tends to slight improvements, which is the result of better average performance. Classification methods have parameters that can perform high accuracy. Classification results of three types of sensors are shown in Table 8. Comparisons of each sensor classification are shown in Table 9. It can be seen that most of conventional classification results research 100%, except for flux sensor of SVM. But the smart sensor results is not good for SVM and RF. The reason is that smart sensor signal to noise ratio (SNR) is not better than conventional one in Fig. 7.

6. Conclusion This article presents the development of induction motor fault diagnosis using a novel smart sensors system. The smart sensor system can take pace of expensive traditional sensors for fault testing of induction motors. The purposed system was demonstrated through a comparison experiment of induction motor fault diagnosis. Exponential results show the developed techniques can satisfy the following abilities:

 Signal conditioning (high pass filtering, low pass filtering, and gain amplification).  Sensor IP address can detect automatic sensor identification.  Dynamic data acquisition (8192 Hz sampling rate; 8192 data number; 16 bit ADC resolution)  Comparable price range to conventional equipment-based and cable-based data acquisition, and monitoring system.  Data backup function can save the data to SD-memory due to wireless network instability.  Secondary, this paper compares the performances of two systems using three types of sensors for induction motor diagnosis. Based on the classification accuracy, stability and complexity of used classifiers and signals as above, conclusions can be summarized as follows:  To compare the performance of classifiers of the two systems. Except for SVM and RF classifiers, smart sensor system is similar with conventional system. SVM and RF classifier results are not good because the features of smart sensor have more scattered than ones of conventional system.  To compare the performances of classifiers in multi-class diagnosis problem. LDA and k-NN are the good ones, which have perfect classification accuracy and stability. The next is SVM, and the worst is RF due to its classification instability for various signals. In terms of the results in fault detection problem (normal or abnormal condition) of machine fault diagnosis, smart sensor system shows good performance hence can instead of the conventional sensors system if SNR effect is reduced. To overcome the SNR effect, appropriate signal amplitude is recommended because smart sensor system has low cost chip product, and the ADC performance is lower than conventional analyzer. Additionally, it is necessary to have amplifier for change the signal amplitude levels. And different levels of ADC digital signals are compensated in basestation.

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