Isolation of abnormal changes in process variables is an integral component of fault diagnosis, as it provides evidential information for determining the root cause of a detected abnormal event. This task is challenging when the approach to diagnosis does not incorporate knowledge of the process’ nominal behavior, but is instead established solely on historical process data. Though isolation of abnormal changes in variables may be facilitated by including historical process data for faults that have been previously diagnosed, inconclusive results will remain for unfamiliar faults. This paper presents a method for isolating abnormal changes in process variables with an autoencoder (AE) - a type of neural network configured for latent projection — and without prior knowledge of nominal process behavior or faults. The AE is optimized with nominal process data as well as a sparsity constraint to produce a sparse network. Probing into the sparse AE allows one to gain insight into the correlations that exist among the process variables during normal process operation. Movements in the AE’s reconstruction space are interrogated alongside the acquired knowledge to isolate the abnormal changes in process variables. The method is demonstrated with a simulation of a nonlinear triple tank process, and is shown to isolate abnormal changes in variables for both simple and complex faults.

隔离过程变量中的异常变化是故障诊断的组成部分，因为它为确定检测到的异常事件的根本原因提供了证据。当诊断方法未结合过程名义行为的知识，而是仅基于历史过程数据建立时，此任务将具有挑战性。尽管可以通过包括先前已诊断出的故障的历史过程数据来促进变量异常变化的隔离，但是对于不熟悉的故障仍将无法得出结论。本文提出了一种使用自动编码器（AE）隔离过程变量异常变化的方法，该方法是为潜在投影配置的一种神经网络，并且无需事先知道正常的过程行为或故障。利用标称过程数据以及稀疏性约束对AE进行优化，以生成稀疏网络。探究稀疏AE可以使人们深入了解正常过程操作期间过程变量之间存在的相关性。与获取的知识一道，对AE重建空间中的运动进行查询，以隔离过程变量的异常变化。该方法通过非线性三槽过程的仿真进行了演示，并被证明可以隔离简单故障和复杂故障的变量异常变化。与获取的知识一道，对AE重建空间中的运动进行查询，以隔离过程变量的异常变化。该方法通过非线性三槽过程的仿真进行了演示，并被证明可以隔离简单故障和复杂故障的变量异常变化。与获取的知识一道，对AE重建空间中的运动进行查询，以隔离过程变量的异常变化。该方法通过非线性三槽过程的仿真得到证明，并被证明可以隔离简单故障和复杂故障的变量异常变化。