Evaluation of Current Signature in Bearing Defects by envelope analysis of the vibration in induction motors

Motor current signature analysis (MCSA) enables non-invasive monitoring, without interruption of machine operation in a remote and online way, allowing the identification of various types of faults of electrical and mechanical nature without the need of accessing the motor itself, but only its supply cables. Despite its advantages, it has limitations in accurately diagnosing incipient roller bearing faults. For the detection of incipient roller bearing faults, envelope analysis of vibration signals is a well-known and stablished technique used by motor condition monitoring experts for a long time, overcoming MCSA for that purpose. Thus, it is proposed in this paper, that the fault characteristic frequencies of roller bearings are identified in the current spectrum with the aid of envelope analysis on the bearing vibration signal. After this aided identification, the fault related spectral components in the current spectrum can be correctly tracked over time for trending evaluation and decision-making. This approach can represent a significant economic value in a motor condition monitoring program, since vibration envelope analysis is performed only at a first step and, after that, its results can be applied for the MCSA monitoring of all same-model motor drivers in an industrial site. This approach is even more valuable considering the concept of the Self-Supplied Wireless Current Transducer (SSWCT) also proposed in this paper. The SSWCT is an Industrial Internet of Things (IIOT) device for MCSA application in an Industry 4.0 environment. This proposed device has wireless communication interface and wireless/battery less power supply, being supplied by the energy harvested from the magnetic field of the same currents it is transducing. So, it is a completely galvanic isolated monitoring device, without batteries and without any electric connections to the industry electric system, easily installable to the motor cables, not using precious space in the electric panels of the motor control centers and not having any physical contact to the monitored asset.

[1]  Wentao Sui,et al.  Research on envelope analysis for bearings fault detection , 2010, 2010 5th International Conference on Computer Science & Education.

[2]  Erik Leandro Bonaldi,et al.  Discrimination of Synchronous Machines Rotor Faults in Electrical Signature Analysis Based on Symmetrical Components , 2017, IEEE Transactions on Industry Applications.

[3]  Germano Lambert-Torres,et al.  Detecting load failures using the induction motor as a transducer , 2008, 2008 10th International Conference on Control, Automation, Robotics and Vision.

[4]  Germano Lambert-Torres,et al.  A study of electrical signature analysis for two-pole synchronous generators , 2017, 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).

[5]  Alberto Bellini,et al.  Bearing Fault Model for Induction Motor With Externally Induced Vibration , 2013, IEEE Transactions on Industrial Electronics.

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

[7]  Wei Qiao,et al.  Bearing Fault Diagnosis for Direct-Drive Wind Turbines via Current-Demodulated Signals , 2013, IEEE Transactions on Industrial Electronics.

[8]  Thomas G. Habetler,et al.  A survey of condition monitoring and protection methods for medium voltage induction motors , 2009, 2009 IEEE Energy Conversion Congress and Exposition.

[9]  Tiantian Zhu,et al.  Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy , 2019, Energies.

[10]  J.A. Ortega,et al.  Fault detection in induction machines by using continuous and discrete wavelet decomposition , 2007, 2007 European Conference on Power Electronics and Applications.

[11]  Faris Elasha,et al.  Remaining Useful Life Prediction of Rolling Element Bearings Using Supervised Machine Learning , 2019, Energies.

[12]  Ruoyu Li,et al.  Plastic Bearing Fault Diagnosis Based on a Two-Step Data Mining Approach , 2013, IEEE Transactions on Industrial Electronics.

[13]  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.

[14]  Erik Leandro Bonaldi,et al.  Predictive Maintenance by Electrical Signature Analysis to Induction Motors , 2012 .

[15]  Elhoussin Elbouchikhi,et al.  An Efficient Hilbert–Huang Transform-Based Bearing Faults Detection in Induction Machines , 2017, IEEE Transactions on Energy Conversion.

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

[17]  Giansalvo Cirrincione,et al.  Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks , 2013, IEEE Transactions on Industrial Electronics.

[18]  Erik Leandro Bonaldi,et al.  A Study of Fault Diagnosis Based on Electrical Signature Analysis for Synchronous Generators Predictive Maintenance in Bulk Electric Systems , 2019, Energies.