Affiliation:
1. College of Information Science and Technology, Donghua University, Songjiang District, Shanghai 201600, China
Abstract
The development of sensor networks allows for easier time series data acquisition in industrial production. Due to the redundancy and rapidity of industrial time series data, accurate anomaly detection is a complex and important problem for the efficient production of the textile process. This paper proposed a semantic inference method for anomaly detection by constructing the formal specifications of anomaly data, which can effectively detect exceptions in process industrial operations. Furthermore, our method provides a semantic interpretation of exception data. Hybrid signal temporal logic (HSTL) was proposed to improve the insufficient expressive ability of signal temporal logic (STL) systems. The epistemic formal specifications of fault offline were determined, and a data-driven semantic anomaly detector (SeAD) was constructed, which can be used for online anomaly detection, helping people understand the causes and effects of anomalies. Our proposed method was applied to time-series data collected from a representative textile plant in Zhejiang Province, China. Comparative experimental results demonstrated the feasibility of the proposed method.
Funder
Ministry of Science and Technology
National Natural Science Foundation of China
Natural Science Foundation of Shanghai
Fundamental Research Funds for the Central Universities and Graduate Student Innovation Fund of Donghua University
Subject
Process Chemistry and Technology,Chemical Engineering (miscellaneous),Bioengineering