WPConvNet: An Interpretable Wavelet Packet Kernel-Constrained Convolutional Network for Noise-Robust Fault Diagnosis

Deep learning-based methods have shown promising results in fault diagnosis, but research on interpretability and noise robustness still needs to be done. A multi-channel wide-kernel wavelet convolutional neural network is proposed to address these issues. Firstly, a first layer of multi-channel wide-kernel convolution is designed to fuse different weight information and suppress high-frequency noise. Secondly, a discrete wavelet transform block is designed to retain the low-frequency components of the discrete wavelet transform for signal denoising and feature dimension reduction. At the same time, Improved Balance Dynamic Adaptive Threshold is used to enhance the robustness of the model’s noise and the sparsity of features, making the model easier to optimize. Lastly, a power spectrum and normalized class activation mapping are designed to validate the post-hoc explanations of the model. The effectiveness and reliability of the Multi-Channel Wide Kernel Wavelet Convolutional Neural Network are verified through two gearbox datasets.

Section: Experimental Results and Analysismentioning

confidence: 99%

MC-WDWCNN: an interpretable multi-channel wide-kernel wavelet convolutional neural network for strong noise-robust fault diagnosis

Zhou,

Zhang,

Jiang

et al. 2024

“…The convolution layer and activation function play a significant role in feature extraction within deep neural networks [33]. Previous studies have drawn parallels between convolutional operations and wavelet decomposition, substituting the first convolutional layer with a wavelet convolutional layer to capitalize on the unique filtering capabilities of wavelet decomposition.…”

Section: Atrafmentioning

confidence: 99%

Audio fault diagnosis of belt conveyors based on improved variational modal decomposition and improved adaptive noise reduction convolutional network in strong noise environments

Li,

Wang,

Liang

et al. 2024

Fault diagnosis of belt conveyors is crucial for coal mine production, but audio-based fault diagnosis in underground coal mines remains challenging due to the strong noise environment. To address this problem, a method for audio fault diagnosis of belt conveyors based on improved variational modal decomposition and improved adaptive noise reduction convolutional networks in a strong noise environment is proposed. Firstly, the improved beluga whale optimization is designed by introducing the non-linear balance factor and non-linear probability and combining them with the proposed cyclical shock factor to optimize the variational modal decomposition parameters to achieve noise reduction and signal reconstruction. Secondly, an improved adaptive noise reduction convolutional network is developed using an adaptive threshold activation function and an improved loss function to enhance noise robustness and fault diagnosis accuracy. Finally, the proposed method's effectiveness is evaluated in low and strong noise environments, with experimental results demonstrating superior fault diagnosis performance. In low noise environments, the fault diagnosis accuracy is 98.61%, and in strong noise environments, it is 98.96%, outperforming existing fault diagnosis methods.

“…To overcome the above drawbacks, the interpretable of CNNs are gradually attracting attention in the field of Interpretable Deep Learning (IDL) [15,16]. Currently, interpretable methods can be categorized as pre-methods and post-methods.…”

Section: Introductionmentioning

confidence: 99%

WBUN: an interpretable convolutional neural network with wavelet basis unit embedded for fault diagnosis

Gao,

Zhang,

Zhang

et al. 2024

Convolutional Neural Network (CNN) is extensively applied in mechanical system fault diagnosis. However, the absence of transparent decision mechanisms in CNNs hinders credibility. To address these challenges, this paper proposes an interpretable wavelet basis unit convolutional network (WBUN). This network incorporates meticulously designed wavelet basis unit (WBU) functions into convolutional layer, creating the interpretable wavelet basis unit convolutional (WBUConv) layer. Convolutional kernels with clear physical significance enable the WBUConv layer to extract fault-related features in both time and frequency domains, enhancing diagnostic performance, and interpreting the CNN's attention frequency along with the convolutional kernel's training outcomes. In this paper, three WBU functions are designed to construct the corresponding WBUNs, and their effectiveness and interpretability are verified through three sets of mechanical fault diagnosis experiments. Meanwhile, experimental results demonstrate the WBUConv layer's remarkable advantages in noise robustness, convergence speed, and strong generalization ability.