# A Survey on Fault Diagnosis of Rolling Bearings
## 1 Introduction  
With the rapid development of technology and science, modern industry has become increasingly important in our daily life. The advancement of science and technology has led to the gradual development of large-scale and high-speed rotating machinery with integration, precision, and intelligence. Rotating machinery is an essential part of modern industry and is widely used in many fields, including energy and power, machinery manufacturing, transportation, and aerospace. Once mechanical equipment is successfully developed for production, the reliability and safety of the equipment become increasingly crucial, and the fault diagnosis and condition monitoring of the core components become an arduous task [1; 2; 3]. Roller bearings are widely used in rotating machinery and are an indispensable component that supports the rotating shaft and serves as a connector between stationary and rotating parts. Although rolling bearing damage occurs at the component level, it frequently leads to more severe equipment failures. According to statistics, rolling bearing failures account for 40-90% of all rotating machinery failures [4]. The initial failure of the rolling bearing of a wind turbine will only affect itself, and the unit will remain operational. However, as the times of abnormal operations increase, external excitations caused by broken bearings will cause the traditional system to malfunction, resulting in a fire in extreme cases. Roll bearing failure in the rolling mill will cause a reduction in the qualityof rolled products, which will lead to the production line being stopped and result in significant economic losses. Due to the complex and changing conditions operating in rotating machinery, rolling bearings often fail before their designed life ends, and their actual service life is often shorter than their design life, so a routine shutdown inspection is not the best way. Therefore, an effective and intelligent fault diagnosis of rolling bearings is of considerable practical significance for ensuring the health of rotating equipment and machinery. Fault diagnosis of rolling bearings is a multidisciplinary field that incorporates computer science, mathematics, electronics, signal processing, engineering, and other modern technologies. Rolling bearing fault diagnosis is to diagnose the bearing health status through the collected operation data. Fault diagnosis can be broadly categorized into fault detection and fault type recognition. Fault detection is to detect faults from the collected data, while fault type recognition is to recognize faults and their types from the data. During the past ten years, fault diagnosis of rolling bearings has attracted considerable attention from both academics and the industry. Figure 1 shows the number of publications on the topic of rolling bearing fault diagnosis extracted from the Scopus database. It is clear that the number of publications has gradually increased from 2011 to 2021. There are several survey papers on fault diagnosis. However, most of them focus on specified tasks or methods, such as machine learning-based methods [5] for prognostics and health management of rolling element bearings [6], Fourier transform and enhanced fast Fourier transform algorithms [7], artificial intelligence methods [8], spectral kurtosis [9], and signal processing techniques [10]. Very few of them provide a general and comprehensive survey on rolling bearing fault diagnosis using vibration signals from the perspectives of fault detection and fault type recognition. To address the above limitations, this paper reviews over 150 related publications in recent years, including over 100 publications from 2016 to 2021. These publications are well-known or representative ones in the rolling bearing fault diagnosis community.