[논문]Monolith and Partition Schemes with LDA and Neural Networks as Detector Units for Induction Motor Broken Rotor Bar Fault Detection

Ayhan Bulent; Chow Mo-Yuen; Song Myung-Hyun

Monolith and Partition Schemes with LDA and Neural Networks as Detector Units for Induction Motor Broken Rotor Bar Fault Detection 원문보기

KIEE international transactions on electrical machinery and energy conversion systems, v.5B no.2, 2005년, pp.103 - 110

Ayhan Bulent (Dept. of Electrical and Computer Engineering North Carolina State University) , Chow Mo-Yuen (Dept. of Electrical and Computer Engineering North Carolina State University) , Song Myung-Hyun (Dept. of Electrical and Computer Engineering North Carolina State University)

Abstract ▼ AI-Helper

Broken rotor bars in induction motors can be detected by monitoring any abnormality of the spectrum amplitudes at certain frequencies in the motor current spectrum. Broken rotor bar fault detection schemes should rely on multiple signatures in order to overcome or reduce the effect of any misinterpretation of the signatures that are obscured by factors such as measurement noises and different load conditions. Multiple Discriminant Analysis (MDA) and Artificial Neural Networks (ANN) provide appropriate environments to develop such fault detection schemes because of their multi-input processing capabilities. This paper describes two fault detection schemes for broken rotor bar fault detection with multiple signature processing, and demonstrates that multiple signature processing is more efficient than single signature processing.

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가설 설정

H0 : Incoming motor signature test data correspond to healthy state of the motor.

제안 방법

This paper presents two fault detection schemes for broken rotor bar fault detection with multiple signatxire processing and demonstrates that multiple signature processing is more effective than single signature processing. The first scheme will be named the "monolith scheme", and it is based on a single MDA or a single ANN unit representing the complete motor operating load torque region. The second scheme will be named the "partition scheme," and it consists of several small MDA or ANN units, each of which representing a particular load torque operating region.
The experiments involved collecting three phase stator induction motor current and speed data for four different load conditions of the motor both with one broken rotor bar fault and without any fault. The load conditions of the motor are 25%, 50%, 75% and full load respectively.

대상 데이터

Thus, there are a total of 8 different experiment cases. For each individual case, 20 sets of motor current data were collected. Each motor current data set contains 10, 000 samples for a duration of one second, which makes the sampling frequency 10kHz.

이론/모형

In selection of these structures, we have taken into consideration that we have a larger number of units in the hidden layer than the input layer. The LM (Levenberg- Marquardt) training algorithm is used in both schemes.
The methodology, in the investigated fault detection schemes, uses the Welch's spectrum amplitudes of motor current data at the broken rotor bar fault specific frequencies as the discriminative signatures in the fault detection decision. However, the locations of the monitored frequencies, fb, depend on the slip factor, s, which is a function of the motor mechanical speed.

참고문헌 (9)

？M.E.H. Benbouzid , G.B. Kliman, 'What stator current processing based technique to use for induction motor rotor faults diagnosis',' IEEE Power Engineering Review, August 2002
B. Li., M.-Y. Chow, Y. Tipsuwan, lC. Hung, 'Neural-network-based motor rolling bearing fault diagnosis,' IEEE Trans. Industrial Electronics on, vol. 47, no. 5 , Oct. 2000, pp. 1060-1069

상세보기
？S. Altug, M.- Y. Chow, H.J. Trussell, 'Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis,' IEEE Trans. on Industrial Electronics, vol. 46, no. 6 , Dec. 1999, pp. 1069 -1079

상세보기
？M. Haji, H. A. Toliyat, 'Pattern recogmtion - a technique for induction machines rotor broken bar detection,' IEEE Trans. on Energy Conversion, vol. 16, no. 4, Dec. 2001, pp. 312-317

상세보기
？A. Bellini, F. Filippetti, G. Franceschini, C. Tassoni, G.B. Kliman, , 'Quantitative evaluation of induction motor broken bars by means of electrical signature analysis', IEEE Trans. on Industrial Applications, vol. 37, no. 5, Sep./Oct. 2001, pp. 1248-1255

상세보기
？G.B. Kliman et al., 'Non-invasive detection of broken rotor bars in operating induction motors', IEEE Trans. on Energy Conversion vol. EC-3, no. 4, pp. 873-879, 1988
？F. Filipetti et al., 'AI Techniques in induction machines diagnosis including the speed rifle effect,' lEEE- lAS Annual Meeting Conference, San Diego, pp. 655-662, Oct 6-10, 1996
？S. Lawrence Marple, 'Digital Spectral Analysis with Applications', Prentice Hall, 1987
？S. Amari et al. 'Asymptotic statistical theory of overtraining and cross validation,' IEEE Trans. on Neural Networks, vol. 8, no. 5, Sep. 1997, pp. 985-996

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Monolith and Partition Schemes with LDA and Neural Networks as Detector Units for Induction Motor Broken Rotor Bar Fault Detection 원문보기

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Monolith and Partition Schemes with LDA and Neural Networks as Detector Units for Induction Motor Broken Rotor Bar Fault Detection 원문보기

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