Anomaly detection of industrial state quantity time-Series data based on correlation and long short-term memory. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Tang, Mingxin; Chen, Wei; Yang, Wen
• Is Part Of: Connection science. Volume 34:Issue 1(2022)
• Page Start: 2048
• Page End: 2065
• Publication Date: 2022-12-31
• Subjects: Anomaly detection -- long short-term memory -- machine learning -- model based -- time series of state quantity; Neural computers -- Periodicals; Artificial intelligence -- Periodicals; Cognitive science -- Periodicals; Connectionism -- Periodicals; 006.3
• DOI: 10.1080/09540091.2022.2092594
• Languages: English
• ISSNs: 0954-0091

Abstract

ABSTRACT: Anomaly detection of multi-dimensional time-series data is a key research area, and the analysis of control, switching, and other state signals (i.e., industrial state quantity time series) is of particular importance to the operational sciences. When only the limited values of industrial state quantities are taken in the discrete set, there is no continuous change trend, making it difficult to achieve good results when applying analogue anomaly detection methods directly. In this study, assuming a correlation between the time series of state and analogue quantities in industrial systems, a model for anomaly detection in state quantity time-series data is built through a correlation supported by long short-term memory, and the model is verified using real physical process data. These results demonstrate that the proposed method is superior to extant industrial time-series models. Thus far, no studies focusing on the anomaly detection of two-state quantity time-series outliers have been performed. We believe that the research problem addressed herein and the proposed method contribute an interesting design methodology for the anomaly detection of time-series data in IIoT.