A self-adaptive multiple-fault diagnosis system for rolling element bearings

Abstract

The inevitable simultaneous formation of multiple faults in bearings generates severe vibration, propelling it for premature component failure and unnecessary downtime. For accurate diagnosis of multiple faults, Machine Learning (ML) models need to be trained with the signature of different multi-faults, which increases the data acquisition time and expenses. This paper proposes a self-adaptive vibration signature-based fault diagnostic method for detecting multiple bearing faults using various single fault vibration signatures. A time-frequency based hybrid signal processing technique, which involves Discrete Wavelet Transform (DWT) and Hilbert Transform (HT) was adopted for signal decomposition, followed by the implementation of a sliding window-based feature extraction process. Seven optimised metaheuristic algorithms were used to find the best feature sets, which were further used for the training of three ML models. The result showed that the proposed methodology has tremendous potential to detect multiple bearing fault conditions in any possible combination by using single fault data. It will be helpful where accessibility to a large amount of data is limited for multiple fault diagnosis.

An imbalanced sample intelligent fault diagnosis method using data enhancement and improved broad learning system

“…Besides, there are many other swarm intelligence optimization methods, such as particle swarm optimization (PSO) [36], Henry gas solubility optimization (HGSO) [37] and so on, that can be used to extract feature. These methods can also achieve good results.…”

Smart Sensor-Based Monitoring Technology for Machinery Fault Detection

“…Machine component defects will influence machine performance quality and reliability. Based on this investigation, up to 75% of imperfections in small- and medium-size rotating machines are related to defects in rolling element bearings [ 1 , 2 ]. This work will focus on condition monitoring and fault diagnosis of rolling element bearings.…”

Methodology for the Detection of Contamination and Gradual Outer Race Faults in Bearings by Fusion of Statistical Vibration–Current Features and SVM Classifier

“…The diagnosis of multiple simultaneous faults has also been studied, showing interesting alternatives to the detection of combined defects. In this area, works like [21] implemented a vibration-based system to detect sixteen defect conditions in bearings consisting of the combinations of OR, IR, ball, and cage damages simulated from single fault signals using DWT, Hilbert Transform (HT), statistical features, metaheuristic algorithms and comparing the results of a Feed-Forward Back-Propagation (FFBP) Artificial Neural Network (ANN), a Support Vector Machine (SVM) with a Gaussian kernel, and a kNN classifier, resulting in a maximum outcome of 91.6% effectiveness from the ANN. In reference [22], a wavelet-based moving average control chart is studied to diagnose the combination of OR, IR, and rolling element faults using vibration signals and comparing the results to those obtained with Empirical Mode Decomposition (EMD) and Hilbert envelope spectrum analysis, showing positive results with the method and also revealing the difficulty of identifying the faults if the signals have similar energy dispersion patterns.…”