A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor

Abstract

The fault detection and diagnosis (FDD) along with condition monitoring (CM) and of rotating machinery (RM) have critical importance for early diagnosis to prevent severe damage of infrastructure in industrial environments. Importantly, valuable industrial equipment needs continuous monitoring to enhance the safety, reliability, and availability and to decrease the cost of maintenance of modern industrial systems and applications. However, induction motor (IM) has been extensively used in several industrial processes because it is cheap, reliable, and robust. Rolling bearings are considered to be the main component of IM. Undoubtedly, any failure of this basic component can lead to a serious breakdown of IM and for whole industrial system. Thus, many current methods based on different techniques are employed as a fault prognosis and diagnosis of rolling elements bearing of IM. Moreover, these techniques include signal/image processing, intelligent diagnostics, data fusion, data mining, and expert systems for time and frequency as well as time-frequency domains. Artificial intelligence (AI) techniques have proven their significance in every field of digital technology. Industrial machines, automation, and processes are the net frontiers of AI adaptation. There are quite developed literatures that have been approaching the issues using signals and data processing techniques. However, the key contribution of this work is to present an extensive review of CM and FDD of the IM, especially for rolling elements bearings, based on artificial intelligent (AI) methods. This study highlights the advantages and performance limitations of each method. Finally, challenges and future trends are also highlighted.

A Novel Feature Extraction and Fault Detection Technique for the Intelligent Fault Identification of Water Pump Bearings

“…A lot of research has been conducted in the past on the diagnosis of holes or dents in the bearing raceways and it has been shown that a small-sized hole (diameter < 1 mm) can produce tinny amplitudes; it is thus a great challenge to reliably diagnose these small faults [ 26 , 27 , 28 , 29 , 30 ]. Some studies have used noise filtration and threshold-based statistical analysis algorithms to enhance the reliability of the small-sized hole diagnostics [ 26 , 27 ].…”

“…In the current decade, the use of artificial intelligence (AI) has been significantly increased for the reliable diagnostics and classification of machine faults [ 29 ]. AI techniques such as machine learning and deep learning can be trained to accomplish specific tasks by processing a large amount of data and recognizing fault trends in them [ 30 , 31 , 32 ]. There are various types of algorithms for machine learning and deep learning and the selection of the algorithm is a challenging task.…”

Motor Fault Diagnosis Algorithm Based on Wavelet and Attention Mechanism

“…However, this manual method not only requires a lot of manpower, material, and financial resources but also ensures that the fault can be eliminated in time. With the development of computer technology, large-and medium-sized motors are equipped with computer-centered condition monitoring and fault diagnosis system, in order to prevent or repair the functional failure or local failure before the failure, minimize the loss, and prevent the occurrence of catastrophic accidents [2].…”

Bearing Fault Detection in Induction Motors Using Line Currents

“…Induction motors are mainly used in residential and industrial applications such as transportation, mining, chemicals, power plants, and paper for electrical to mechanical energy conversion [1] due to their high reliability, robustness, and costeffectiveness. However, the main issue with the operation of induction motors is that such harsh industrial application has affected their reliability, causing unexpected breakdowns, resulting in high maintenance costs and motor deterioration [2,3].…”