# A Survey on Fault Diagnosis of Rolling Bearings
## 4 Rolling Bearing Fault Type Recognition
### Traditional Fault Type Recognition Methods
[116; 117] further proposed multi-scale dispersion entropy (MDE) and refine composite multi-scale dispersion entropy (RCMDE) based on the advantages of DE and the coarse-graining method to address the shortcomings of MSE and MPE. RCMDE and MDE offer much greater computational efficiency than MSE and MPE. Multi-group experiments have shown that RCMDE is more valuable for identifying bearing fault types [117]. (2) Feature reduction  
The more statistical features are used to describe signals, the more comprehensively the inherent information of signals is expressed. However, the high-dimensional feature set includes many redundant and negative-effect indexes/features. Dealing with such a large number of useless features typically increases the computational complexity and affects the recognition accuracy. In addition, using too many features to describe a large number of signals may lead to dimensional disaster. Therefore, it is necessary to reduce and compress the tremendous data resources effectively for extracting valuable information and knowledge. Feature transformation and feature selection methods can generate a low-dimensional feature set for fault type recognition. Feature transformation methods are categorized based on how they preserve data structure. Two types of feature transformation methods exist, the global preservation-based methods, such as principal component analysis (PCA) and linear discriminant analysis (LDA), and the local preservation-based methods, such as local preserving projections (LPP) and margin Fisher analysis (MFA). In [118; 119; 120; 121], the sample features reduced by different feature transformation methods were used to perform the identification of bearing fault types. The works in [122; 123; 124] show that the feature subset obtained by considering the local and global information in signals with different statuses is more effective for improving recognition performance. [PERSON] et al. [123] proposed the Laplacian LDA (Lap-LDA) method based on least square LDA, which can not only obtain the global structure information of the data using LDA but also obtain the local structure information of the data using the Laplacian map. [PERSON] et al. [124] proposed global-local structure analysis (GLSA), combining the advantages of LPP and PCA. Classification  
After feature extraction and feature reduction, it is necessary to train a classifier to learn the mapping between the features and the class labels of existing bearing signals for conducting automatic fault type recognition. The known instances with the transformed/selected features and the corresponding class labels are fed into a classification algorithm as the training set. The class label of each instance in the test set can be predicted by the trained classifier according to their features. In the past decade, various classification methods have been applied to rolling bearing fault type recognition, such as \(k\)-nearest neighbor (KNN), artificial neural network (ANN), support vector machine (SVM), extreme learning machine (ELM), and random forest (RF). KNN has the advantages of only one parameter and easy implementation by making classification decisions vis identifying the attributes of a limited number of neighboring training samples around the unknown/testing sample. [PERSON] et al. [108] calculated IDE-PE of the bearing signal and used KNN to classify bearing fault types. It should be noted that the performance of KNN depends on the quality of sample features. ANN is a multilayer feedforward neural network and can perform fault type recognition by adjusting the association relationship between a large number of network nodes [135; 136]. SVM has good generalization ability, but the kernel function and related parameters need to be selected. [PERSON] et al. [137] proposed a new rolling element bearing fault diagnosis method based on multi-scale fuzzy entropy, multiple class feature selection, and SVM. [PERSON] et al.