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首页> 外文期刊>Journal of Mechanical Science and Technology >A sparse auto-encoder method based on compressed sensing and wavelet packet energy entropy for rolling bearing intelligent fault diagnosis
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A sparse auto-encoder method based on compressed sensing and wavelet packet energy entropy for rolling bearing intelligent fault diagnosis

机译：基于压缩检测的稀疏自动编码方法和小波包能量熵，用于滚动轴承智能故障诊断

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摘要

Improving diagnostic efficiency and shortening diagnostic time is important for improving the reliability and safety of rotating machinery, and has received more and more attention. When using intelligent diagnostic methods to diagnose bearing faults, the increasingly complex working conditions and the huge amount of data make it a great challenge to diagnose fault quickly and effectively. In this paper, a novel fault diagnosis method based on sparse auto-encoder (SAE), combined with compression sensing (CS) and wavelet packet energy entropy (WPEE) for feature dimension reduction is proposed. Firstly, vibration signals of each fault type are projected linearly through compressed sensing to obtain compressed signals, which are merged into a low-dimensional compressed signal matrix of multiple fault types. Secondly, the WPEE of low-dimensional compressed signal matrix of multi-fault type is determined, and the eigenvector matrix of bearing fault diagnosis is formed, which greatly reduces the dimension of the eigenvector matrix. Finally, SAE are constructed by adding sparse penalty to auto-encoder (AE) for high-level feature learning and bearing fault classification, and it not only further learns the high-level features of data, but also reduces the feature dimension. Compared with traditional feature extraction methods and the standard deep learning method, the proposed method not only guarantees high accuracy, but also greatly reduces the diagnosis time.
机译：提高诊断效率和缩短诊断时间对于提高旋转机械的可靠性和安全性是重要的，并且受到越来越多的关注。在使用智能诊断方法诊断轴承故障时，越来越复杂的工作条件和大量数据使其成为诊断故障的巨大挑战，可以快速且有效地诊断故障。本文提出了一种基于稀疏自动编码器（SAE）的新型故障诊断方法，结合压缩感测（CS）和小波分组能量熵（WPEE），用于特征尺寸减小。首先，通过压缩感测来线性地投射每个故障类型的振动信号，以获得压缩信号，该压缩信号被合并成多个故障类型的低维压缩信号矩阵。其次，确定了多故障类型的低维压缩信号矩阵的WPEE，形成了轴承故障诊断的特征矩阵，这大大降低了特征向量矩阵的尺寸。最后，通过为高级特征学习和轴承故障分类添加稀疏惩罚来构建SAE，不仅进一步了解数据的高级功能，而且还减少了特征维度。与传统特征提取方法和标准深度学习方法相比，所提出的方法不仅可以保证高精度，而且大大降低了诊断时间。

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