A Survey on Fault Diagnosis of Rolling Bearings

Abstract

The failure of a rolling bearing may cause the shutdown of mechanical equipment and even induce catastrophic accidents, resulting in tremendous economic losses and a severely negative impact on society. Fault diagnosis of rolling bearings becomes an important topic with much attention from researchers and industrial pioneers. There are an increasing number of publications on this topic. However, there is a lack of a comprehensive survey of existing works from the perspectives of fault detection and fault type recognition in rolling bearings using vibration signals. Therefore, this paper reviews recent fault detection and fault type recognition methods using vibration signals. First, it provides an overview of fault diagnosis of rolling bearings and typical fault types. Then, existing fault diagnosis methods are categorized into fault detection methods and fault type recognition methods, which are separately revised and discussed. Finally, a summary of existing datasets, limitations/challenges of existing methods, and future directions are presented to provide more guidance for researchers who are interested in this field. Overall, this survey paper conducts a review and analysis of the methods used to diagnose rolling bearing faults and provide comprehensive guidance for researchers in this field.

A meta-learning method for few-shot bearing fault diagnosis under variable working conditions

“…Their primary role in reducing friction is essential for ensuring machine dependability and efficiency. Rolling bearings frequently operate under conditions of high temperature, high speed, and heavy loads, rolling bearings making them more prone to failure [3]. These failures not only disrupt equipment functionality but also leading to * Author to whom any correspondence should be addressed.…”

“…While some DL methods have demonstrated impressive performance in bearing fault diagnosis, the primary challenges faced by intelligent diagnosis models include limited fault data and variability in working conditions [13,14]. Gathering data during equipment malfunctions is often discouraged due to safety and financial concerns, as it can shorten equipment lifespan and increase the risk of accidents and property damage [3]. This limitation elevates the risk of overfitting and makes it challenging to achieve high generalization performance in models trained on small datasets [2].…”

A fault diagnosis method for railway turnout systems based on Convolution Transformer through data augmentation

“…Ensuring railway transportation safety and efficient maintenance has always been a top priority [1][2][3] . However, this requires a considerable amount of manual effort and is inefficient.…”

An Intelligent diagnosis method for rolling bearings based on Ghost module and adaptive weighting module

“…In modern industrial systems, mechanical rotating body has evolved towards high speed, mass and integration and plays a vital role in aviation, industrial production and high-speed railways [1] . Rolling bearings, as important parts of mechanical equipment, play a role in supporting the rotating body, reducing friction and driving the transmission.…”