Rolling Bearing Fault Diagnosis Based on Support Vector Machine Optimized by Improved Grey Wolf Algorithm

Abstract

This study targets the low accuracy and efficiency of the support vector machine (SVM) algorithm in rolling bearing fault diagnosis. An improved grey wolf optimizer (IGWO) algorithm was proposed based on deep learning and a swarm intelligence optimization algorithm to optimize the structural parameters of SVM and improve the rolling bearing fault diagnosis. A nonlinear contraction factor update strategy was also proposed. The variable coefficient changes with the shrinkage factor α. Thus, the search ability was balanced at different early and late stages by controlling the dynamic changes of the variable coefficient. In the early stages of optimization, its speed is low to avoid falling into local optimization. In the later stages of optimization, the speed is higher, and finding the optimal solution is easier, balancing the two different global and local optimization capabilities to complete efficient convergence. The dynamic weight update strategy was adopted to perform position updates based on adaptive dynamic weights. First, the dataset of Case Western Reserve University was used for simulation, and the results showed that the diagnosis accuracy of IGWO-SVM was 98.75%. Then, the IGWO-SVM model was trained and tested using data obtained from the full-life-cycle test platform of mechanical transmission bearings independently researched and developed by Nanjing Agricultural University. The fault diagnosis accuracy and convergence value of the adaptation curve were compared with those of PSO-SVM (particle swarm optimization) and GWO-SVM diagnosis models. Results showed that the IGWO-SVM model had the highest rolling bearing fault diagnosis accuracy and the best diagnosis convergence.

Path Planning of Obstacle-Crossing Robot Based on Golden Sine Grey Wolf Optimizer

“…The GWO is a kind of swarm intelligence algorithm [21]. Because of its strong convergence performance and relatively simple algorithm structure, it has been applied to parameter optimization [22,23], fault diagnosis [24,25], path planning [26][27][28] and other fields. However, the GWO also has problems, such as a too singular initial population, slow convergence speed and the ease with which it falls into the local optimum.…”

A Novel Method for Rolling Bearing Fault Diagnosis Based on Gramian Angular Field and CNN-ViT

“…Signal processing methods mainly include wavelet transform (WT) [ 2 , 3 ], local mean decomposition (LMD) [ 4 ], empirical modal decomposition (EMD) [ 5 ], and short-time Fourier transform (STFT) [ 6 , 7 ]. The main shallow machine learning methods are support vector machine (SVM) [ 8 , 9 ], k-nearest neighbor (KNN) [ 10 ], and random forest (RF) [ 11 ]. Zheng et al [ 12 ] implemented fault classification by calculating the composite multiscale fuzzy entropy (CMFE) of vibration signals based on ensemble support vector machine (ESVM).…”

Research on an Improved Auxiliary Classifier Wasserstein Generative Adversarial Network with Gradient Penalty Fault Diagnosis Method for Tilting Pad Bearing of Rotating Equipment

“…Zhang et al [8] proposed a CNN-based multi-channel data fusion neural network for rolling bearing fault diagnosis, using bearing data collected by eight vibration sensors on the SB25 aero-engine bearing bench test for experimental verification of the model. Shen et al [9] proposed an improved Gray Wolf optimizer algorithm based on a support vector machine and swarm intelligence optimization algorithm for rolling bearing fault diagnosis. The proposed algorithm was verified experimentally using the experimental data set published by CWRU and the data obtained from the mechanical transmission bearing life-cycle test platform independently developed by Nanjing Agricultural University.…”