Fault Diagnosis of Variable Load Bearing Based on Quantum Chaotic Fruit Fly VMD and Variational RVM

Abstract

Under normal circumstances, bearings generally run under variable loading conditions. Under such conditions, the vibration signals of the bearing malfunctions are often nonstationary signals, which are difficult to process effectively. In order to accurately and effectively diagnose the failure types and damage degree of bearings under variable load conditions, an intelligent diagnostic model based on the variational mode decomposition (VMD) of quantum chaotic fruit fly optimization algorithm (QCFOA) and a multiclassification variational relevance vector machine (VRVM) is proposed. First, the key parameters of the VMD are selected using the QCFOA. Secondly, the known bearing fault signal is decomposed by the optimized VMD, and the center frequency and marginal spectral entropy (MSE) of each natural modal component are extracted to construct two-dimensional MSE. Then, the probit model is used to replace the logistic model, and a simpler and more practical multiclassification model is constructed. The two-dimensional MSE of each intrinsic modal component is used as a learning sample for VRVM. Finally, the bearing fault data under 1 hp load are taken as training samples, and the bearing fault data under two loads of 0 hp and 3 hp are used as test samples to verify the effectiveness of the intelligent diagnosis model. The experimental results show that the intelligent fault diagnosis method proposed in this paper can accurately diagnose the type of fault and the degree of damage and has high robustness.

Advancing Bearing Fault Diagnosis under Variable Working Conditions: A CEEMDAN-SBS Approach with Vibro-Electric Signal Integration

“…To evaluate the effectiveness of the methodology proposed in this study for diagnosing bearing faults using vibration and electrical signals, a comparative analysis was undertaken against existing fault diagnosis approaches designed for variable working conditions. Our method was specifically benchmarked against several state-of-the-art techniques, including the STFT-CNN method presented in [59], the DFSM method outlined in [60], the CEEMDAN-SSA-RVM method detailed in [61], the TL-CNN method from [62], the KNN method featured in [63], and the V-RVM method described in [64]. Table 7 displays the comparative results, indicating that the proposed method attains accuracies of 99.40% and 98.30% when utilizing vibration and electrical signals.…”

Evaluation on Filter Performance of Variational Mode Decomposition and Its Application in Separating Closely Spaced Modes

“…Recent developments of the VMD method mainly concentrate on the field of fault diagnosis. With the assistance of the particle swarm optimization [14][15][16], multikernel support vector machine [17], the k-nearest neighbour algorithm [18], and other novel algorithms [19][20][21], promising results have been obtained. In a similar manner with the EMD denoising, the VMD has also been introduced to signal denoising based on the detrended fluctuation analysis [22,23].…”