Abstract

Data-based fault diagnosis is an important technology in modern manufacturing systems. However, most of these diagnosis methods assume that all the data should be identically distributed. In diagnosis tasks, this assumption means that these methods can only handle faults from the same working load. In real-world applications, the working load of the equipment varies for the different productions; if an unknown working load with no prior data available is given, then these traditional methods may be invalid. Zero-shot learning, using known data to diagnose the fault under unknown working loads, provides a transfer approach to solve this problem. In this paper, a zero-shot learning method based on contractive stacked autoencoders is proposed. The proposed method is only trained by the data from the known working load and can diagnose the fault from unknown but related working loads without prior data. The experimental results on the Case Western Reserve University dataset indicate that the proposed method performs better than the traditional methods under unknown working loads and has an accuracy of 97.82%. In addition, the analysis of the singular value and feature space also suggests that the proposed method is more robust and the feature representation is more contractive.

中文翻译：

---

零工作负荷下故障诊断的零散学习方法

摘要

基于数据的故障诊断是现代制造系统中的一项重要技术。但是，大多数这些诊断方法均假定所有数据应均等分布。在诊断任务中，这种假设意味着这些方法只能处理来自相同工作负载的故障。在实际应用中，设备的工作负荷因生产而异。如果给出了未知的工作负荷，没有可用的先前数据，则这些传统方法可能无效。零散学习，使用已知数据诊断未知工作负载下的故障，提供了一种转移方法来解决此问题。提出了一种基于压缩堆叠自动编码器的零弹学习方法。所提出的方法仅由来自已知工作负荷的数据训练，并且可以在没有先前数据的情况下根据未知但相关的工作负荷来诊断故障。在凯斯西储大学的数据集上的实验结果表明，该方法在未知工作负荷下的性能优于传统方法，准确率为97.82％。此外，对奇异值和特征空间的分析还表明，该方法具有更强的鲁棒性，并且特征表示更具收缩性。