Condition Monitoring of Bearing Faults Using the Stator Current and Shrinkage Methods

Abstract

Condition monitoring of bearings is an open issue. The use of the stator current to monitor induction motors has been validated as a very advantageous and practical way to detect several types of faults. Nevertheless, for bearing faults, the use of vibrations or sound generally offers better results in the accuracy of the detection, although with some disadvantages related to the sensors used for monitoring. To improve the performance of bearing monitoring, it is proposed to take advantage of more information available in the current spectra, beyond the usually employed, incorporating the amplitude of a significant number of sidebands around the first eleven harmonics, growing exponentially the number of fault signatures. This is especially interesting for inverter-fed motors. But, in turn, this leads to the problem of overfitting when applying a classifier to perform the fault diagnosis. To overcome this problem, and still exploit all the useful information available in the spectra, it is proposed to use shrinkage methods, which have been lately proposed in machine learning to solve the overfitting issue when the problem has many more variables than examples to classify. A case study with a motor is shown to prove the validity of the proposal.

Low Complexity Non-Linear Spectral Features and Wear State Models for Remaining Useful Life Estimation of Bearings

“…Data-driven bearing prognostic systems are constructed using signal processing techniques with real measured sensor acquired signals, to analyse and detect trends providing valuable evidence of system degradation [12,13]. Sensing modalities to acquire bearing degradation signatures that have been widely explored in recent years include vibration signals [1,3,14,15], acoustic emissions [16,17], stator current measurements [18][19][20], thermalimaging [21], and multiple sensor fusion [22,23]. Of these, vibration signals, acquired from mounted accelerometers is often attributed as the most favourable approach for conditionbased monitoring (CbM) in general, due to the non-invasive nature of the measurement data, low cost, robustness and ease of implementation in practice [24].…”

Sensorless Speed Estimation for the Diagnosis of Induction Motors via MCSA. Review and Commercial Devices Analysis

“…This method is mainly applied to detect speed-dependent fault harmonics in the frequency spectrum of stator current [ 18 ], although other magnitudes can also be used (e.g., instantaneous power, reactive power or apparent power [ 19 , 20 , 21 ]). Many research papers, whose main purpose is not localizing the fault harmonic, since they test lab motors with perfectly-known conditions, analyze the expected fault harmonic frequency band assuming that the highest peak of the band will be the fault harmonic [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. Some other authors use filters as wavelet transform to extract sub-signals related to frequency bands where the harmonic is supposed to be [ 31 , 32 , 33 ].…”

Convolutional Neural Networks for Automated Rolling Bearing Diagnostics in Induction Motors Based on Electromagnetic Signals

“…For these reasons, and considering that bearing faults represent more than 40% of induction motor faults [2,3], for the last several decades, much attention has been focused on the condition monitoring of rolling bearings, initially based on vibration measurements and more recently on the measurement of electromagnetic signals coming from the electrical machine in which they are installed, i.e., stator current and external stray flux.…”