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A Deep Learning Model for Fault Diagnosis with a Deep Neural Network and Feature Fusion on Multi-Channel Sensory Signals

机译：深度神经网络故障诊断的深度学习模型多通道感觉信号的特征融合

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摘要

Collecting multi-channel sensory signals is a feasible way to enhance performance in the diagnosis of mechanical equipment. In this article, a deep learning method combined with feature fusion on multi-channel sensory signals is proposed. First, a deep neural network (DNN) made up of auto-encoders is adopted to adaptively learn representative features from sensory signal and approximate non-linear relation between symptoms and fault modes. Then, Locality Preserving Projection (LPP) is utilized in the fusion of features extracted from multi-channel sensory signals. Finally, a novel diagnostic model based on multiple DNNs (MDNNs) and softmax is constructed with the input of fused deep features. The proposed method is verified in intelligent failure recognition for automobile final drive to evaluate its performance. A set of contrastive analyses of several intelligent models based on the Back-Propagation Neural Network (BPNN), Support Vector Machine (SVM) and the proposed deep architecture with single sensory signal and multi-channel sensory signals is implemented. The proposed deep architecture of feature extraction and feature fusion on multi-channel sensory signals can effectively recognize the fault patterns of final drive with the best diagnostic accuracy of 95.84%. The results confirm that the proposed method is more robust and effective than other comparative methods in the contrastive experiments.
机译：收集多通道感官信号是一种可行的方式来提高诊断机械设备的性能。在本文中，提出了一种与多通道感觉信号上的具有特征融合结合的深度学习方法。首先，采用由自动编码器组成的深度神经网络（DNN）来自适应地学习来自感官信号的代表特征和症状与故障模式之间的近似非线性关系。然后，在从多通道感觉信号中提取的特征的融合中使用位置保存投影（LPP）。最后，基于多个DNN（MDNNS）和SOFTMAX的新型诊断模型是用熔融的深度特征的输入构建。该方法在汽车最终驱动器中验证了智能故障识别，以评估其性能。实现了一种基于背部传播神经网络（BPNN），支持向量机（SVM）和具有单个感官信号和多通道感觉信号的提出的深度架构的多个智能模型的对比分析。多通道感官信号上的特征提取和特征融合的建议深度架构可以有效地识别最终驱动器的故障模式，最佳诊断精度为95.84％。结果证实，该方法比对比实验中的其他比较方法更加稳健，有效。

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