Comparison of Induction Machine Bearing Fault Detection Methods using MCSA, SA and GoFT

Nowadays, induction machines are widely used in various sectors, their robustness allows them to operate even in fault conditions; this can lead to permanent damage to the machine and economic losses as a result of machine replacement and/or production downtime. This reflects the importance of monitoring methods or strategies to detect imminent failures. Bearing Failure can be detected by treating signals such as sound, vibration and/or current. This work is based on the comparison of the following methods: Spectral Analysis and 2 types of Goodness-of-Fit Tests; the database is obtained in the laboratory using real and controlled damage; these methods are performed using Motor Current Signature Analysis. The strategies mentioned above, as well as the comparison of their accuracy, are validated experimentally.

[1]  Bidyadhar Subudhi,et al.  Corrosion Fault Diagnosis of Rolling Element Bearing under Constant and Variable Load and Speed Conditions , 2015 .

[2]  Hayde Peregrina-Barreto,et al.  Induction motors fault detection using independent component analysis on phase current signals , 2018, 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).

[3]  Danijela Cabric,et al.  Kuiper test based modulation level classification under unknown frequency selective channels , 2017, 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[4]  Abdul Gafoor Shaik,et al.  Application of stockwell transform in bearing fault diagnosis of induction motor , 2016, 2016 IEEE 7th Power India International Conference (PIICON).

[5]  Andrew D. Ball,et al.  An application to transient current signal based induction motor fault diagnosis of Fourier-Bessel expansion and simplified fuzzy ARTMAP , 2013, Expert Syst. Appl..

[6]  F. Filippetti,et al.  Monitoring of induction motor load by neural network techniques , 2000 .

[7]  P. Kripakaran,et al.  Diagnosis of bearing fault in induction motor by zero sequence current , 2017, 2017 International Conference on Innovative Research In Electrical Sciences (IICIRES).

[8]  A. Iqbal,et al.  ANN-based for detection, diagnosis the bearing fault for three phase induction motors using current signal , 2013, 2013 IEEE International Conference on Industrial Technology (ICIT).

[9]  H. Razik,et al.  Fault detection of broken rotor bars in induction motor using a global fault index , 2006, IEEE Transactions on Industry Applications.

[10]  Bertrand Raison,et al.  Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring , 2004, IEEE Transactions on Industrial Electronics.

[11]  Hayde Peregrina-Barreto,et al.  Bearing fault detection in induction motors using MCSA and statistical analysis , 2018, 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).

[12]  Alessandro Goedtel,et al.  Detecting Bearing Faults in Line-Connected Induction Motors Using Information Theory Measures and Neural Networks , 2015 .

[13]  Inseok Yang,et al.  Bearing fault effect on induction motor stator current modeling based on torque variations , 2012, 2012 12th International Conference on Control, Automation and Systems.

[14]  Hong-Hee Lee,et al.  Probabilistic frequency-domain discrete wavelet transform for better detection of bearing faults in induction motors , 2016, Neurocomputing.

[15]  Eugene Demidenko,et al.  Kolmogorov-Smirnov Test for Image Comparison , 2004, ICCSA.