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A subspace analysis method combining cointegration analysis and dynamic latent variables for non-stationary process fault detection

A subspace analysis method combining cointegration analysis ...

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第35届中国过程控制会议

作者： Keyu Yao Yuguo Yang Hongbo Shi School of Information Science and Engineering East China University of Science and Technology

In the field of fault detection, changes in the normal state of certain parameters due to changes in actual operating conditions are very similar to those caused by faults. This results in certain normal process changes being misrecognized as process abnormalities, while certain process abnormalities that actually occur are masked by normal process changes. In order to solve the above problems, a subspace analysis method based on cointegration analysis and dynamic latent variables subspace analysis(CA-DLVSA) is proposed in this work. First, the process variables in the real industrial process are categorized into non-stationary and stationary variables using the ADF test. Second, the variables determined to be static are divided into the dynamic stationary subspace and static stationary subspace using the DLV model. For variables judged to be non-stationary, the non-stationary subspace is further divided into the dynamic stationary and static non-stationary subspace using an algorithm that combines CA and DLV. Then, the dynamic stationary spaces obtained from the stationary and non-stationary data are then combined to form the final dynamic stationary space and the static stationary portion of the stationary data is retained to form the final static stationary space. Finally, the corresponding statistics are constructed for process monitoring in both spaces. In three-phase flow process, the excellent performance of the proposed method in non-stationary process monitoring is verified by comparing the DLV, SFA, CVA, CA-DLVSA methods.

关键词： Cointegration analysis dynamic latent variables Process Monitoring Non-stationary process

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