Feature Engineering for Ball Bearing Combined-Fault Detection and Diagnostic

Abstract

The non detection of bearing faults in rotating machines can lead to less availability, reliability and safety while increasing the maintenance costs due to unexpected breakdowns and urgent repairing. This paper deals with feature engineering to enhance the performance of an early fault detection and diagnostic of ball bearings of asynchronous electrical motors. The features of different types, ie. time, frequency and time-frequency, are extracted from both current and vibration. Then, they are selected based on a genetic algorithm to continuously capture the health state of the ball bearings. The proposed method is applied on sensor signals acquired from a test bench reproducing a real industrial system. The obtained results show the effectiveness of the method particularly for fault detection and diagnostic using current signals which can be useful in practical applications.

A Study on Deep Learning Application of Vibration Data and Visualization of Defects for Predictive Maintenance of Gravity Acceleration Equipment

“…Zhang et al [6] proposed fault detection for bearing wind turbines using ANNs (Artificial Neural Networks). Khlaief et al [13] adopted a method of learning Many studies have examined the changes in the human and animal body due to changes in gravity. The necessity of monitoring the safety and reliability of large gravity acceleration equipment has become an important issue.…”

“…Fault Detection using ML: Many studies on vibration-related failures and predictive failure diagnosis have been conducted [3,[9][10][11][12][13][14][15][16][17][18][19][20][21]. Lee et al [3] proposed a rotating mechanism system-a mixture of feature extraction and selection classifies it as a Support Vector Machine (SVM) [4].…”

“…Zhang et al [5] proposed fault detection for bearing wind turbines using ANNs (Artificial Neural Networks). Khlaief et al [13] adopted a method of learning via K-Nearest Neighbor (KNN), SVM, and Linear Discriminant Analysis (LDA) by screening features based on genetic algorithms to continuously check the state of ball bears in rotating ball bears of asynchronous electrical motors. Le et al [14] proposed an algorithm based on the ensemble machine learning (EML) for fault detection in Series dc arc and tested its performance using techniques such as bagging, boosting, and stacking various linear classifiers such as fault perceptrons, Decision Trees (DTs), and SVMs.…”

A Novel Feature Extraction Method for the Condition Monitoring of Bearings

“…Studies conducted on the health monitoring of bearings indicate that it is possible to extract health indicators (features) by using two approaches: an experience-based approach given by the manufacturer and a data-driven approach [26][27][28][29][30][31][32][33]. There is a third one called the model-based approach.…”

Novel Diagnostic Techniques for Rotating Electrical Machines—A Review

“…However, there is another group of papers which is focused on machine learning (ML) and deep learning (DL) techniques [102]; to implement effective ML and DL algorithms for bearing fault detection, good data collection is needed and, therefore, these papers sometimes refer to datasets available on-line [103], but also to wide campaigns of laboratory measurements [104,105]. In [106], a very recent survey of these papers is reported, together with a comparative study of the classification accuracy of various algorithms that use the open-source Case Western Reserve University (CWRU) bearing dataset.…”