[1]LIU Jinping,KUANG Yabin,ZHAO Shuangshuang,et al.Transition process identification based on the long and short sliding windowed slow feature analysis and time series association rule mining[J].CAAI Transactions on Intelligent Systems,2023,18(3):589-603.[doi:10.11992/tis.202205048]
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Transition process identification based on the long and short sliding windowed slow feature analysis and time series association rule mining

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 3 Page number: 589-603 Column: 学术论文—知识工程 Public date: 2023-07-05
Title:
Transition process identification based on the long and short sliding windowed slow feature analysis and time series association rule mining
Author(s):
LIU Jinping1;  KUANG Yabin1;  ZHAO Shuangshuang1;  YANG Guangyi2
1. College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China;
2. Information Center, Hunan Institute of Metrology and Test, Changsha 410014, China
Keywords:
process monitoring; identification of transition process; slow feature analysis; synchronize frequent tree; temporal association rule mining; steady state condition; long and short sliding window; multimode condition
CLC:
TP29
DOI:
10.11992/tis.202205048
Abstract:
The transition process of working condition has similar data characteristics with process anomalies or faults. Thus, it is prone to cause frequent false alarms in the process monitoring if the transition process cannot be identified, which may lead to inappropriate manual operations and consequently destroy production stability. This paper proposes a transition process identification method based on the long and short sliding windowed slow feature analysis (SFA) and time series association rules mining approach. First, a multi-mode process modeling method based on the SFA associated with long and short sliding window processing is proposed according to the time-span difference between steady and transition conditions. The working condition state is divided into multiple sub-stages of steady or transition states, and then corresponding offline SFA models are established, respectively. Then, a synchronous frequent tree construction method with multi-time series and multi-time intervals is proposed, which can mine the association rules of each transition state in multi-time series and multi-time intervals, so as to accurately identify transition processes. Based on the Tennessee Eastman (TE) process, a set of process data, including all modal transitions, is established to verify the proposed method. The results show that the proposed method can effectively identify the transition process with frequent transitions, reducing the false alarm rate and improving the level of process monitoring.
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