Fault Diagnosis Methods for an Artillery Loading System Driving Motor in Complex Noisy Environments

Abstract

With the development of modern military technology, electrical drive technology has become a power source for modern artillery. In fault monitoring of a driving motor mounted on a piece of artillery, various sensors are susceptible to interference from the complex environment, both inside and outside the artillery itself. In this study, we creatively propose a fault diagnosis model based on an attention mechanism, the AdaBoost method and a wavelet noise reduction network to address the difficulty in obtaining high-quality motor signals in complex noisy interference environments. First, multiple fusion wavelet basis, soft thresholding, and index soft filter optimization were used to train multiple wavelet noise reduction networks that could recover sample signals under different noise conditions. Second, a convolutional neural network (CNN) classification module was added to construct end-to-end classification models that could correctly identify faults. The above basis classification models were then integrated into the AdaBoost method with an improved attention mechanism to develop a fault diagnosis model suitable for complex noisy environments. Finally, two experiments were conducted to validate the proposed method. Under motor signals with varying signal-to-noise ratios (SNRs) noises, the proposed method achieved an average accuracy of 92%, surpassing the conventional method by over 8.5%.

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“…Additionally, the approach incorporates a visualization tool known as Gradient-weighted Class Activation Mapping (Grad-CAM), which improves the CNN’s interpretability by producing heatmaps on the fault’s time-frequency representations. To tackle the challenge of acquiring high-quality motor signals in complex noisy environments, Huang et al [ 23 ] proposed a fault diagnosis model combining an attention mechanism, the AdaBoost method, and a wavelet noise reduction network. Firstly, multiple wavelet bases, soft thresholding, and index soft filters are optimized to train diverse wavelet noise reduction networks capable of restoring signals under various noise conditions.…”