# A Survey on Fault Diagnosis of Rolling Bearings
## 2 Background, Taxonomy, and Scope
### Taxonomy and Scope  
The purpose of rolling bearing fault diagnosis is to determine the bearing health status by analyzing the collected operation data. Diagnostics of faults revolve primarily around fault detection and fault type identification. Figure 2 shows the general flowchart of fault detection and fault type recognition. Although fault detection and fault type recognition may have some overlap, they are two different types of tasks in fault diagnosis. Specifically, fault detection is to detect faults or non-faults from the collected data, and fault type recognition is to recognize faults and their types from the data. Therefore, to solve these two tasks, different procedures are often used. For fault detection, the collected bearing signals are utilized to determine bearing status. The process often includes removing the noise and harmonic interference from the monitoring signal using signal processing methods and then manually identifying the fault by finding its characteristic frequency. Fault typerecognition refers to using the existing bearing signals to construct a diagnostic system to evaluate the unknown bearing signals. Unlike fault detection, fault type recognition methods automatically extract or construct fault features from the signals and determine the bearing health status using machine learning algorithms.  
The monitoring data of rolling bearings can be collected from oil [11], temperature [12], sound [13], vibration [14], and other media. Performing an oil analysis affects production continuity because it involves shutting down equipment and opening the cover to collect lubricant and other oil samples. Temperature measurement equipment is expensive and cannot provide a promising monitoring effect. Temperature analysis neither has good accuracy at the early stage of bearing fault nor distinguishes the fault types. Sound analysis has high technical demands for signal acquisition and identification because the acoustic signal attenuates and is susceptible to environmental noise interference. In contrast, vibration signal characteristics are stable and easy to collect, making vibration analysis a suitable condition monitoring technique. Vibration analysis has a firm theoretical basis. Research on the fault diagnosis method of rolling bearings based on vibration signal has long been a hot issue concern by domestic and foreign experts and scholars.  
This survey paper summarizes the fault diagnosis methods of rolling bearings based on vibration signals from the perspectives of fault detection and fault type recognition. First, four types of signal processing methods commonly used for fault detection of rolling bearings, i.e., morphological transformation-based methods, filter-based methods, decomposition-based methods, and deconvolution-based methods, are discussed. Then, the classical fault type recognition methods are discussed from three aspects: feature extraction, feature reduction, and classification. In addition, the recently popular deep learning based-fault type recognition methods such as convolutional neural networks, Autoencoder, deep belief networks, and recursive neural networks, are also discussed and reviewed. The taxonomy of this survey is shown in Figure 3.  
Figure 2: General flowchart of rolling bearing fault diagnosis.