多工况下基于域自适应的往复式压缩机故障诊断方法

Lijun Zhang, Lixiang Duatt, Xiaocui Hong, Xinyun Zhang
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引用次数: 0

摘要

往复压缩机结构复杂、工况多变,导致采集到的监测数据噪声干扰强、诊断模型通用性差等问题。针对上述问题,本文提出了一种基于域自适应的往复式压缩机故障诊断方法。它打破了传统人工智能算法中源域和目标域数据分布相同的假设。为往复压缩机设备的智能诊断提供了新的思路。首先,利用CEEMDAN对振动信号进行分解和重构;并结合小波变换,将一维信号转换成二维时频图像。最后,在分类器前加入MK-MMD层,对源域和目标域进行自适应,实现基于ResNet50的往复压缩机多工况故障诊断。实验结果表明,CEEMDAN与小波变换相结合可以有效降低噪声干扰,且时频图像包含丰富的信息。此外,采用ResNet50-MK-MMD方法进行多工况下的故障诊断,平均准确率达到97%以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fault Diagnosis Method of Reciprocating Compressor Based on Domain Adaptation under Multi-working Conditions
The complex structure and changeable working conditions of reciprocating compressor lead to the strong noise interference of collected monitoring data, the poor universality of diagnosis model and so on. A fault diagnosis method of reciprocating compressor based on domain adaptation is proposed in this paper to solve the above-mentioned problems. It breaks away from the assumption of the same distribution of source domain and target domain data in the traditional artificial intelligence algorithm. In addition, it contributes a new idea to the intelligent diagnosis of reciprocating compressor equipment. Firstly, the vibration signal is decomposed and reconstructed by CEEMDAN. Besides, in combination with wavelet transform, one-dimensional signal is converted into two-dimensional time-frequency image. Finally, a MK-MMD layer is added in front of the classifier for adaptation to the source domain and target domain, so as to realize fault diagnosis of multi-working conditions for the reciprocating compressor based on ResNet50. According to the experimental results, the combination of CEEMDAN and WT can be effective in reducing the noise-induced interference, and the time-frequency image contains rich information. In addition, the ResNet50-MK-MMD method is used for fault diagnosis under multi-working condition, with the average accuracy reaching above 97%.
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