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首页> 外文会议>IEEE International Conference on Mechatronics and Automation >Fault Diagnosis Method of Reciprocating Compressor Based on Domain Adaptation under Multi-working Conditions
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Fault Diagnosis Method of Reciprocating Compressor Based on Domain Adaptation under Multi-working Conditions

机译：基于多工作条件下域适应的往复式压缩机故障诊断方法

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摘要

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%.
机译：往复式压缩机的复杂结构和可变的工作条件导致收集的监测数据的强噪声干扰，诊断模型的普遍性较差等。本文提出了一种基于域改编的往复式压缩机的故障诊断方法，以解决上述问题。从传统人工智能算法中的源域和目标域数据的相同分布的假设中断，它会断开。此外，它为往复式压缩机设备的智能诊断提供了新的想法。首先，振动信号由CeeMDAN分解和重建。此外，与小波变换组合，一维信号被转换为二维时频图像。最后，将MK-MMD层添加到分类器的前面，以便适应源域和目标域，以实现基于RENET50的往复式压缩机的多工作条件的故障诊断。根据实验结果，CeeMDAN和WT的组合可以有效地降低噪声引起的干扰，并且时频图像包含丰富的信息。此外，Reset50-MK-MMD方法用于多功能条件下的故障诊断，平均精度达到97％以上。

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