Fault diagnosis is an essential component in process supervision. Indeed, it determines which kind of fault has occurred, given that it has been previously detected, allowing for appropriate intervention. Automatic fault diagnosis systems use machine learning for predicting the fault type from sensor readings. Nonetheless, these models are sensible to changes in the data distributions, which may be caused by changes in the monitored process, such as changes in the mode of operation. This scenario is known as Cross-Domain Fault Diagnosis (CDFD). We provide an extensive comparison of single and multi-source unsupervised domain adaptation (SSDA and MSDA respectively) algorithms for CDFD. We study these methods in the context of the Tennessee-Eastmann Process, a widely used benchmark in the chemical industry. We show that using multiple domains during training has a positive effect, even when no adaptation is employed. As such, the MSDA baseline improves over the SSDA baseline classification accuracy by 23% on average. In addition, under the multiple-sources scenario, we improve classification accuracy of the no adaptation setting by 8.4% on average.

翻译：故障诊断是过程监控中的关键组成部分。它能够在故障被检测后确定故障类型，从而采取适当干预措施。自动故障诊断系统利用机器学习从传感器读数中预测故障类型。然而，这些模型对数据分布的变化较为敏感，这种变化可能由被监测过程的变化（如操作模式的变更）引发。该场景被称为跨域故障诊断（CDFD）。我们针对CDFD问题，对单源与多源无监督域适应（分别为SSDA和MSDA）算法进行了全面比较。这些方法在化学工业广泛使用的田纳西-伊斯特曼过程基准平台上进行验证。研究表明，即使不采用适应机制，使用多个训练域也能产生积极效果。相较于SSDA基线，MSDA基线的平均分类准确率提升了23%。此外，在多源场景下，相较于无适应设置，分类准确率平均提升了8.4%。