Research of Bearing Fault Diagnosis Method Based on Multi-Layer Extreme Learning Machine Optimized by Novel Ant Lion Algorithm

Abstract

In this paper, a novel bearing fault diagnosis method based on multi-layer extreme learning machine (MELM) optimized by the novel ant lion algorithm (NALO) is proposed. First, using permutation entropy of different scales (MPE) to extract fault features of bearings, a group of fault feature vectors composed of permutation entropy is obtained. Then, the fault feature vectors are classified by the MELM. However, with the increase of the number of hidden layers, the random input weight and bias will also increase, which will lead to the increase of the randomness of the MELM and affect the accuracy of fault diagnosis. Therefore, this paper uses the NALO to optimize the MELM. For the NALO, opposite populations are added to the initial population to improve its global search ability. When the ant lion updates its location, the influence of pheromones left by other ants with a certain sensing distance is taken into account to prevent the ant lion from falling into the local optimal and increased the robustness. Finally, the NALO-MELM and other bearing fault diagnosis methods are applied to the bearing fault experiment of Western Reserve University to test the performance and generalization of the proposed method. INDEX TERMS Bearing fault diagnosis, multiscale permutation entropy, multi-layer extreme learning machine, ant lion algorithm, local pheromone effects.

A New Fuzzy Logic Classifier Based on Multiscale Permutation Entropy and Its Application in Bearing Fault Diagnosis

“…The minimum number of father nodes of DT is 5. The nearest neighbor number of KNN is K = 5, and the number of hidden layer nodes of ELM is 100 [21,35]. The Gaussian kernel function of the LSSVM is 0.5.…”

“…The fan-end bearing of CWRU has proven to be a more complex database [35]. In Experiment 2, we use its data to verify the effectiveness of the proposed fault diagnosis method.…”

A review on extreme learning machine

“…Zheng and Wang [239] proposed a multi-layer ELM for bearing fault detection. They developed a novel ant lion algorithm (NALO) to optimize the input weight and bias.…”

Research on Novel Bearing Fault Diagnosis Method Based on Improved Krill Herd Algorithm and Kernel Extreme Learning Machine

“…Moreover, the sample of error diagnosis mainly focuses on the rolling body, which may be related to the working environment of the rolling body and other factors. In order to objectively test the performance of the proposed method, the same data set is imported into other algorithms, such as the K-nearest neighbor algorithm (KNN) [69], naive Bayes classification algorithm (NB) [70], symbol dynamic entropy + support vector machine (SVM) [27], radial basis network (RBF) [71], extreme learning machine (ELM) [31], and multilayer extreme learning machine [32].…”

“…In recent years, with the development of big data, machine learning methods and deep learning methods have been widely used to solve practical engineering problems [21][22][23][24][25][26]. Machine learning methods or deep learning methods were applied in the field of bearing fault diagnosis, including the support vector machine (SVM) [27,28], BP neural network (BP) [29], deep convolutional transfer learning network (CNN) [30], and kernel extreme learning machine (ELM) [31,32]. ese methods are good for fault diagnosis in most cases, but some are more subjective in choosing parameters.…”