### State of the Art of Gearbox Fault Diagnosis
#### 1.2.2 Fault Identification Aspect  
After vibration signals are pre-processed and fault features are extracted, fault classification methods then need to be used to identify faults. In the last decade, emerging machine-learning-based intelligent fault classification methods, such as Convolution Neural Network (CNN), DRN, Extreme Learning Machine (ELM), Transfer Learning (TL), Support Vector Machine (SVM), etc., drew much attention of researchers, and a lot of interesting research work can be found in a survey of the literature.  
For instance, [PERSON] et al. (2019) proposed an adaptive CNN based on Nesterov momentum and the adaptive learning rate rule for bearing fault diagnosis. [PERSON] et al. (2019) put forward a multiscale CNN method to realize feature extraction and fault classification for wind turbine gearbox diagnosis. An anti-noise Neural Network (NN) was presented by [PERSON] et al. (2019) to diagnose bearing faults with changing loads and noise in the vibration signals. [PERSON] et al. (2019) proposed a multi-task learning-based DRN for bearing fault diagnosis. To deal with the limited sample problem, an improved Deep Learning (DL) model was developed by [PERSON] et al. (2019) to realize gearbox fault diagnosis. [PERSON] et al. (2020) proposed a Bayesian ELM to identify concurrent wind turbine gearbox faults based on Huang transforms and correlation techniques. [PERSON] et al. (2021) adopted the TL technique to transfer knowledge from vibration datasets, including massive data to situations without enough sample data. A hybrid DL method involving CNN, SVM, and PSO was designed by [PERSON] et al. (2021) to realize feature extraction and classification of wind turbine gearbox bearing faults. [PERSON] et al. (2020) combined DL and TL to deal with the unlabeled data in the application. [PERSON] et al. (2021) proposed a rolling bearing fault diagnosis method based on CNN and SVM to reduce manual intervention in the feature extraction process. Another two illuminating research studies include References (2020; 2020). [PERSON] et al. (2020) combined fast Fourier transform, a frequency amplitude selection method, and the mean of vector sum method to process vibration signals and to extract feature vectors for rotor fault diagnosis of electrical motors. Then, nearest neighbor, linear discriminant analysis, and linear SVM were adopted to classify faults. [PERSON] et al. (2020) presented another hybrid method, which combined EEMD, the Levy flight-based moth-flame optimization, and the naive Bayes, to realize fast bearing fault diagnosis.  
In the above literature review, it can be seen that many interesting research findings based on intelligent diagnosis methods have been achieved. However, these methods also have their limitations and suitability as follows. (1) When NN-based methods are adopted, a large amount of fault samples are normally needed, which leads to a long training time in which local minimums could be trapped; and (2) when SVM is applied, it is not intuitive to determine its regularization coefficient, and the training time is long with a large-scale training set. Considering the above-mentioned methods, if no knowledge transfer is explicitly needed, if only limited samples are available, and if fast training speed is required, ELM is a suitable fault classification method due to its simplicity, fast training speed, and good generalization ability (2020). Thus, ELM was selected as the gearbox fault classification method in this paper.