Fault isolation is an essential procedure in multivariate statistical process monitoring, which is used to locate the detected fault. Fault isolation identifies the crucial variables responsible for the detected fault. Accurate isolation results are useful for process engineers in diagnosing the root cause of the fault. Recent studies have revealed the equivalence between the fault isolation task and the variable selection problem in discriminant analysis. Inspired by this idea, a nonnegative garrote-based fault isolation strategy is developed to identify the criticality of each process variable to the detected fault, which is further revised to a more robust version by adopting a robust nonnegative garrote. The critical variables can be identified even when the historical process data are contaminated by outliers using the method proposed in this study. The Tennessee Eastman process was used to illustrate the validity of the proposed method.

故障隔离是多变量统计过程监视中的基本过程，用于定位检测到的故障。故障隔离可识别导致检测到的故障的关键变量。准确的隔离结果对于过程工程师诊断故障的根本原因很有用。最近的研究揭示了判别分析中故障隔离任务和变量选择问题之间的等效性。受此想法启发，开发了一种基于非负Garrote的故障隔离策略，以识别每个过程变量对检测到的故障的严重程度，并通过采用鲁棒的非负Garrote将其进一步修订为更可靠的版本。使用本研究提出的方法，即使历史过程数据被异常值污染，也可以识别关键变量。田纳西州伊士曼过程用于说明该方法的有效性。