Fault Diagnosis of Bearings Based on KJADE and VNWOA-LSSVM Algorithm

Abstract

In order to accurately diagnose the faulty parts of the rolling bearing under different operating conditions, the KJADE (Kernel Function Joint Approximate Diagonalization of Eigenmatrices) algorithm is proposed to reduce the dimensionality of the high-dimensional feature data. Then, the VNWOA (Von Neumann Topology Whale Optimization Algorithm) is used to optimize the LSSVM (Least Squares Support Vector Machine) method to diagnose the fault type of the rolling bearing. The VNWOA algorithm is used to optimize the regularization parameters and kernel parameters of LSSVM. The low-dimensional nonlinear features contained in the multidomain feature set are extracted by KJADE and compared with the results of PCA, LDA, KPCA, and JADE methods. Finally, VNWOA-LSSVM is used to identify bearing faults and compare them with LSSVM, GA-LSSVM, PSO-LSSVM, and WOA-LSSVM. The results show that the recognition rate of fault diagnosis is up to 98.67% by using VNWOA-LSSVM. The method based on KJADE and VNWOA-LSSVM can diagnose and identify fault signals more effectively and has higher classification accuracy.

A new method of health condition detection for hydraulic pump using enhanced whale optimization-resonance-based sparse signal decomposition and modified hierarchical amplitude-aware permutation entropy

“…EWOA algorithm. The WOA algorithm has low accuracy and slow convergence in processing complicated optimization problems, so the von Neumann topology presented in Figure 3 is introduced into the WOA (Wu et al, 2019). Each humpback whale has a grid form of up, down, left, and right neighbours to exchange information with up, down, left, and right humpback whales.…”

“…Unfortunately, although the traditional WOA heightens the operation efficiency due to its simple search mechanism, the population multiformity of the algorithm is reduced and it is inclined to fall into the topical optimum in the late iteration. Considering the shortcomings of WOA, an enhanced whale optimization algorithm (EWOA) is put forward by introducing von Neumann topology into the WOA (Wu et al, 2019). On this basis, this paper proposes RSSD based on the EWOA (EWOA-RSSD), which cannot only adaptively search for the optimal combination of parameters required by RSSD but also the optimization efficiency as well the convergence accuracy is high.…”

A Classification Algorithm of Fault Modes-Integrated LSSVM and PSO with Parameters’ Optimization of VMD

“…e least-squares' ground projection method of the double support-vector machine reduces the diagnostic error in [28]. Meanwhile, to optimize the penalty factor C and kernel parameter of LSSVM, some new algorithms such as the Moth-flame Optimization (MFO), the von Neumann Topology Whale Optimization Algorithm (VNWOA), Quantum Particle Swarm (QPS), and Chaotic Antlion Algorithm (CAA) were introduced to implement the optimization operation for enhancing the precision of fault diagnosis in [29][30][31][32][33][34]. e experiments have verified the performance of these presented algorithms.…”

Fault Diagnosis of Rolling Bearing Based on GA-VMD and Improved WOA-LSSVM

“…VMD was proposed to search for the optimal solution of the variational model and determine the frequency center and the bandwidth by Dragomiretshiy et al [19]. Wu et al [20] adopted the VMD algorithm to decompose the vibration signal to obtain multiple sets of modal components, and then introduced the kernel function joint approximate diagonalization of eigenmatrices (KJADE) to calculate the features of each modal component in the time-domain, frequency-domain and time-frequency domain. Ren et al [21] proposed VMD to decompose vibration data, and combined the multi-scale permutation entropy and feature transfer learning to extract the feature information.…”