Estimation of Bearing Fault Severity in Line-Connected and Inverter-Fed Three-Phase Induction Motors

This paper addresses a comprehensive evaluation of a bearing fault evolution and its consequent prediction concerning the remaining useful life. The proper prediction of bearing faults in their early stage is a crucial factor for predictive maintenance and mainly for the production management schedule. The detection and estimation of the progressive evolution of a bearing fault are performed by monitoring the amplitude of the current signals at the time domain. Data gathered from line-fed and inverter-fed three-phase induction motors were used to validate the proposed approach. To assess classification accuracy and fault estimation, the models described in this paper are investigated by using Artificial Neural Networks models. The paper also provides process flowcharts and classification tables to present the prognostic models used to estimate the remaining useful life of a defective bearing. Experimental results confirmed the method robustness and provide an accurate diagnosis regardless of the bearing fault stage, motor speed, load level, and type of supply.

[1]  Sanjay H Upadhyay,et al.  A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings , 2016 .

[2]  Myeongsu Kang,et al.  High-Performance and Energy-Efficient Fault Diagnosis Using Effective Envelope Analysis and Denoising on a General-Purpose Graphics Processing Unit , 2015, IEEE Transactions on Power Electronics.

[3]  G. S. Maruthi,et al.  Application of MEMS Accelerometer for Detection and Diagnosis of Multiple Faults in the Roller Element Bearings of Three Phase Induction Motor , 2016, IEEE Sensors Journal.

[4]  Vicente Climente-Alarcon,et al.  Combination of Noninvasive Approaches for General Assessment of Induction Motors , 2015, IEEE Transactions on Industry Applications.

[5]  Xin Zhou,et al.  Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data , 2016 .

[6]  Farhat Fnaiech,et al.  Application of higher order spectral features and support vector machines for bearing faults classification. , 2015, ISA transactions.

[7]  M. W. Degner,et al.  Stator Windings Fault Diagnostics of Induction Machines Operated From Inverters and Soft-Starters Using High-Frequency Negative-Sequence Currents , 2009 .

[8]  Alexander E Prosvirin,et al.  Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers , 2020, Sensors.

[9]  Zhiwen Liu,et al.  Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings , 2013, Neurocomputing.

[10]  Ruifang Liu,et al.  Modelling of the Bearing Breakdown Resistance in Bearing Currents Problem of AC Motors , 2019, Energies.

[11]  Vicente Climente-Alarcon,et al.  Time-frequency vibration analysis for the detection of motor damages caused by bearing currents , 2017 .

[12]  Gérard-André Capolino,et al.  Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art , 2015, IEEE Transactions on Industrial Electronics.

[13]  Alessandro Goedtel,et al.  Harmonic identification using parallel neural networks in single-phase systems , 2011, Appl. Soft Comput..

[14]  Gérard-André Capolino,et al.  Advances in Diagnostic Techniques for Induction Machines , 2008, IEEE Transactions on Industrial Electronics.

[15]  Bertrand Raison,et al.  Models for bearing damage detection in induction motors using stator current monitoring , 2008, 2004 IEEE International Symposium on Industrial Electronics.

[16]  Brigitte Chebel-Morello,et al.  Linear feature selection and classification using PNN and SFAM neural networks for a nearly online diagnosis of bearing naturally progressing degradations , 2015, Eng. Appl. Artif. Intell..

[17]  Alessandro Goedtel,et al.  Application of intelligent tools to detect and classify broken rotor bars in three-phase induction motors fed by an inverter , 2016 .

[18]  Chuan Li,et al.  Bearing fault diagnosis under unknown variable speed via gear noise cancellation and rotational order sideband identification , 2015 .

[19]  Farhat Fnaiech,et al.  Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis , 2014, 2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR).

[20]  Yukio Mizuno,et al.  A Comparative Study between Machine Learning Algorithm and Artificial Intelligence Neural Network in Detecting Minor Bearing Fault of Induction Motors , 2019, Energies.

[21]  Diego Cabrera,et al.  Observer-biased bearing condition monitoring: From fault detection to multi-fault classification , 2016, Eng. Appl. Artif. Intell..

[22]  Yourong Li,et al.  A selective fuzzy ARTMAP ensemble and its application to the fault diagnosis of rolling element bearing , 2016, Neurocomputing.

[23]  Tommy W. S. Chow,et al.  Weighted local and global regressive mapping: A new manifold learning method for machine fault classification , 2014, Eng. Appl. Artif. Intell..

[24]  Fardin Dalvand,et al.  A Novel Bearing Condition Monitoring Method in Induction Motors Based on Instantaneous Frequency of Motor Voltage , 2016, IEEE Transactions on Industrial Electronics.

[25]  António J. Marques Cardoso,et al.  Stator Fault Diagnostics in Squirrel Cage Three-Phase Induction Motor Drives Using the Instantaneous Active and Reactive Power Signature Analyses , 2014, IEEE Transactions on Industrial Informatics.

[26]  Erik Leandro Bonaldi,et al.  Detection of Localized Bearing Faults in Induction Machines by Spectral Kurtosis and Envelope Analysis of Stator Current , 2015, IEEE Transactions on Industrial Electronics.

[27]  Hongyang Li,et al.  Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory , 2020, Energies.

[28]  Alessandro Goedtel,et al.  Bearing fault identification of three-phase induction motors bases on two current sensor strategy , 2017, Soft Comput..

[29]  Irina Trendafilova,et al.  A fault diagnosis methodology for rolling element bearings based on advanced signal pretreatment and autoregressive modelling , 2016 .

[30]  Sheng Fu,et al.  Bearing Fault Diagnosis Based on Shallow Multi-Scale Convolutional Neural Network with Attention , 2019, Energies.

[31]  Annette Muetze,et al.  Practical Rules for Assessment of Inverter-Induced Bearing Currents in Inverter-Fed AC Motors up to 500 kW , 2007, IEEE Transactions on Industrial Electronics.

[32]  LiChuan,et al.  Observer-biased bearing condition monitoring , 2016 .

[33]  Bhim Singh,et al.  Investigation of Vibration Signatures for Multiple Fault Diagnosis in Variable Frequency Drives Using Complex Wavelets , 2014, IEEE Transactions on Power Electronics.

[34]  Jawad Faiz,et al.  Locating Broken Bars in Line-Start and Inverter-Fed Induction Motors Using Modified Winding Function Method , 2012 .

[35]  Hao Chen,et al.  Monitoring of Rotor-Bar Defects in Inverter-Fed Induction Machines at Zero Load and Speed , 2011, IEEE Transactions on Industrial Electronics.

[36]  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..

[37]  Arun K. Samantaray,et al.  Rolling element bearing defect diagnosis under variable speed operation through angle synchronous averaging of wavelet de-noised estimate , 2016 .

[38]  Czeslaw T. Kowalski,et al.  Selected Rolling Bearing Fault Diagnostic Methods in Wheel Embedded Permanent Magnet Brushless Direct Current Motors , 2019 .

[39]  Fengshou Gu,et al.  Fault Identification of Broken Rotor Bars in Induction Motors Using an Improved Cyclic Modulation Spectral Analysis , 2019, Energies.

[40]  Yu-Min Hsueh,et al.  Induction Motors Condition Monitoring System with Fault Diagnosis Using a Hybrid Approach , 2019, Energies.

[41]  C. Tassoni,et al.  Diagnosis of Bearing Faults of Induction Machines by Vibration or Current Signals: A Critical Comparison , 2010, 2008 IEEE Industry Applications Society Annual Meeting.

[42]  Qingwei Gao,et al.  A detection method for bearing faults using null space pursuit and S transform , 2014, Signal Process..

[43]  Hosseini Sadegh,et al.  Classification of acoustic emission signals generated from journal bearing at different lubrication conditions based on wavelet analysis in combination with artificial neural network and genetic algorithm , 2016 .

[44]  Ting Yang,et al.  Feature Knowledge Based Fault Detection of Induction Motors Through the Analysis of Stator Current Data , 2016, IEEE Transactions on Instrumentation and Measurement.

[45]  Robert B. Randall,et al.  Optimised Spectral Kurtosis for bearing diagnostics under electromagnetic interference , 2016 .