Large-scale mechanical equipment monitoring involves various kinds and quantities of information, and the present research on multisensor information fusion may face problems of information conflicts and modeling complexity. This paper proposes an analysis method combining correlation analysis and deep learning. According to the characteristics of monitoring data, three types of correlation coefficients between sensors in different states are obtained, and a new composite correlation analytical matrix is established to fuse the multisource heterogeneous data. The matrix represents fault feature information of different equipment states and helps further image generation. Meanwhile, a convolutional neural network-based deep learning method is developed to process the matrix and to discover the relationship between results and equipment states for fault diagnosis. To verify the method of this paper, experimental and field case studies are performed. The results show that it can accurately identify fault states and has higher diagnostic efficiency and accuracy than traditional methods.

中文翻译：

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基于传感器关联分析和深度学习的机械设备故障诊断方法研究

大型机械设备监控涉及各种信息量，目前对多传感器信息融合的研究可能会面临信息冲突和建模复杂性的问题。本文提出了一种将相关分析与深度学习相结合的分析方法。根据监测数据的特点，获得了不同状态下传感器之间的三种相关系数，并建立了一个新的复合相关分析矩阵来融合多源异构数据。该矩阵表示不同设备状态的故障特征信息，并有助于进一步生成图像。与此同时，开发了一种基于卷积神经网络的深度学习方法来处理矩阵并发现结果与设备状态之间的关系以进行故障诊断。为了验证本文的方法，进行了实验和现场案例研究。结果表明，与传统方法相比，该方法能够准确识别故障状态，具有较高的诊断效率和准确性。