Abstract
Due to the fact that measured vibration signals from a bearing are complex and non-stationary in nature, and that impulse characteristics are always immersed in stochastic noise, it is usually difficult to diagnose fault symptoms manually. A novel hybrid fault diagnosis approach is developed for denoising signals and fault classification in this work, which combines successfully variational mode decomposition (VMD) and a one-dimensional convolutional neural network (1D CNN). VMD is utilized to remove stochastic noise in the raw signal and to enhance the corresponding characteristics. Since the modal number and penalty parameter are very important in VMD, a particle swarm mutation optimization as a novel optimization method and the weighted signal difference average as a new fitness function are proposed to optimize the parameters of VMD. The reconstructed signals of mode components decomposed by optimized VMD are used as the input of the 1D CNN to obtain fault diagnosis models. The performance of the proposed hybrid approach has been evaluated using sets of experimental data on rolling bearings. The experimental results demonstrate that the VMD can eliminate signal noise and strengthen status characteristics, and the proposed hybrid approach has a superior capability for fault diagnosis from vibration signals of bearings.
Highlights
Rolling bearings are key components of rotating machines.Once there is a serious failure, it may lead to unexpected downtime and thereby resulting in huge financial losses or safety issues
To verify the effectiveness of the proposed method in bearing fault diagnosis, two open-source datasets are used for research in this paper, including Case Western Reserve University (CWRU) Bearing Data Center dataset [41] and the Society for Mechanical Failure Prevention Technology (MFPT) dataset
We propose a novel PSMO method which has the advantages of global optimum of genetic algorithm (GA) and the convergence speed of particle swarm optimization (PSO)
Summary
Rolling bearings are key components of rotating machines. Once there is a serious failure, it may lead to unexpected downtime and thereby resulting in huge financial losses or safety issues. Proposed a feature extraction method based on parameter optimization of VMD and sample entropy, and further used to support SVM for fault diagnosis. In order to solve the problems of choosing the values of VMD parameters, a 1.5-dimensional diagnostic method based on the optimized VMD with genetic algorithm (GA) is proposed for fault diagnosis [24]. In order to solve these two challenging issues in bearing fault diagnosis, a novel hybrid fault diagnosis approach is developed for the denoising of signals and fault classification in this work This combines successfully with the variational mode decomposition (VMD) and one dimensional convolutional neural network (1-D CNN). A novel hybrid fault diagnosis approach is proposed to solve the two challenging issues of non-stationary vibration signal from bearing. PE method has the advantages of its simplicity, extremely fast calculation, robustness, and invariance with
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