Fault detection and diagnosis (FDD) has been an active research field during the past several decades. Methods based on deep neural networks have made some important breakthroughs recently. However, networks require a large number of fault data for training. A chemical process may have several modes during production. Since fault is a low possibility event, some modes may have few fault data in history. Furthermore, collecting and annotating industrial data are extremely expensive and time-consuming. With scarce or unlabeled fault data, networks cannot be effectively used for modeling. In this paper, we present a FDD method based on transfer learning for multimode chemical processes. To overcome the fault data rareness and no label issues in some modes, transfer learning transfers the knowledge from a source mode to a target mode for FDD. Tennessee Eastman (TE) process with five modes is utilized to verify the performance of our proposed method.

在过去的几十年中，故障检测与诊断（FDD）一直是活跃的研究领域。基于深度神经网络的方法最近取得了一些重要的突破。但是，网络需要大量的故障数据进行培训。在生产过程中，化学过程可能具有多种模式。由于故障是不太可能发生的事件，因此某些模式在历史上可能几乎没有故障数据。此外，收集和注释工业数据非常昂贵且耗时。由于缺少或未标记故障数据，因此无法有效地使用网络进行建模。在本文中，我们提出了一种基于转移学习的多模式化学过程FDD方法。为了克服故障数据稀少且在某些模式下没有标签问题的情况，转移学习将知识从FDD的源模式转移到目标模式。