The incipient fault identification of rolling bearings is of great significance in avoiding the occurrence of malignant accidents in rotating machinery. However, at early stages the fault related features are weak and easily contaminated by environmental noise, making them difficult to identify by traditional methods. Hence, in this paper, a new optimized Fourier spectrum decomposition method, termed bandwidth Fourier decomposition (BFD), is proposed for early fault detection in rolling bearings. Firstly, in the BFD method, the vibration signal is adaptively decomposed into sparse narrowband subsignals in the frequency domain through bandwidth optimization. In order to improve the performance of spectrum decomposition, a new bandwidth estimation method and an improved variable initialization strategy are proposed on the basis of spectral energy distribution. Then, the obtained sub signals are converted into timedomain bandwidth mode functions (BMFs) by inverse Fourier transform. After that, the fault characteristic frequency ratio (FCFR) is introduced to select the effective component from the decomposition results. Finally, the bearing faults are identified by matching the envelope spectrum with the defect frequency of the theoretical calculation. To verify the validity of the proposed method, simulation and experimental analysis are carried out in this paper. Preliminary results indicate that the proposed BFD can effectively enhance the recognition of incipient faults in rolling bearings. The superiority of the proposed BFD is also demonstrated by comparing it with ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD) and an improved kurtogram method.
The vibration signals collected from rolling bearings in industrial systems are highly complex and contain intense environmental noise, which challenges the performance of traditional fault diagnosis methods. Moreover, the applicability of the model in engineering practice, especially in the Industrial Internet of Things context, puts forward higher requirements for its storage and computational costs. Considering these challenges, this article proposes an enhanced lightweight multiscale convolutional neural network (CNN) for rolling bearing fault diagnosis. Our contributions mainly fall into three aspects. Firstly, the proposed model is modular and easy to expand, which combines the idea of multiscale learning with attention mechanism and residual learning, enabling the network to extract more abundant and discriminative fault features directly from the raw vibration signal. Consequently, the proposed model can perform better. Secondly, the interpretability of the multiscale learning mechanism is explored by visualizing the extraction process of multiscale features. Finally, for the first time, we introduce the depthwise separable convolution into multiscale CNN to reduce the storage and computational costs of the model, which realizes the lightweight of the model and improves its applicability in the Industrial Internet of Things context. The experimental results on the rolling bearing dataset demonstrate that, compared with the state-of-the-art multiscale CNN models, the proposed model has better discriminative fault feature extraction ability and antinoise ability, and is more suitable for practical industrial systems.
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