The fault diagnosis classification method based on wavelet decomposition and weighted permutation entropy (WPE) by the extreme learning machine (ELM) is proposed to address the complexity and non-smoothness of rolling bearing vibration signals. The wavelet decomposition based on ‘db3’ is used to decompose the signal into four layers and extract the approximate and detailed components, respectively. Then, the WPE values of the approximate (CA) and detailed (CD) components of each layer are calculated and composed to be the feature vectors, which are finally fed into the extreme learning machine with optimal parameters for classification. The comparative study of the simulations based on WPE and permutation entropy (PE) shows that the classification method of seven kinds of signals of normal bearing signals and six types of fault states (7 mils and 14 mils) based on WPE (CA, CD) with the number of nodes in the hidden layers of ELM determined by the five-fold cross-validation has the best performances, the training accuracy can reach 100%, and the testing accuracy can reach 98.57% with 37 nodes of the hidden layer by ELM. The proposed method using WPE (CA, CD) by ELM provides guidance for the multi-classification of normal bearing signals.

Wind turbines are more prone to faults due to long operation in harsh environments. It is necessary to implement fault diagnosis for wind turbines to ensure the stability and reliability of the circuit system safety as well as to reduce the probability of faults. In this paper, a fault detection method based on Wavelet Leaders (WL) and limit learning machine is proposed to achieve fault prediction for fault-prone bearings. The WL algorithm is first applied to analyze the multiple fractality of several different states of faulty bearings, select the stable fault parameter features for extraction and finally use the ELM classification tool to achieve the classification of faults. The experimental results demonstrate that this method achieves fault classification detection and provides a new idea for faulty bearing detection.

Due to the harsh environment in which wind turbines work for long periods of time, the internal drive train is prone to fatigue failure. Rolling bearings are an important component within wind turbines, so it is essential to implement condition detection and fault diagnosis of rolling bearings. This paper proposes a fault signal feature extraction method based on VMD and Tsallis entropy. Firstly, the bearing signals of four different states are decomposed using VMD decomposition, and the IMF components generated after the decomposition are quantitatively characterized using Tsallis entropy, and the features with stability and differentiation are selected to form the feature vector of the fault and input to a support vector machine for classification. The experimental results show that the proposed method can distinguish different fault bearing signals and has some improvement in classification effect compared with KNN, fine tree and intermediate tree classifiers. The proposed method presents a new idea for the fault diagnosis of rolling bearings in wind turbines.