Novel multivariate q-sigma rule focusing on process variation for incipient fault detection in dynamic processes

• Published in: Chemometrics and intelligent laboratory systems Vol. 206; p. 104149
• Main Authors: Chen, Bo, Luo, Xiong-Lin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.11.2020
• Subjects: Minimum control limit; Multivariate q-sigma; Process incipient fault detection; Process monitoring and fault diagnosis; Process variation; Multivariate q-sigma; Process monitoring and fault diagnosis; Process variation; Process incipient fault detection; Minimum control limit
• ISSN: 0169-7439; 1873-3239
• DOI: 10.1016/j.chemolab.2020.104149

When incipient faults occur in chemical processes, some variables will slightly deviate from original trajectories, and process residuals will gradually be continuously biased toward one side of their mean values, i.e., process variation will occur. Traditional indices are inadequately sensitive to this situation or achieve it at the cost of a high false alarm rate (FAR). To address this situation and explore methods with low FAR in dynamic processes, canonical variate residuals (CVRs) are generated. Then, a novel multivariate q-sigma (Mq-sigma) rule is proposed to monitor CVRs. It considers the process variation mentioned above in a window and sets the control limit for each variable. When tested on a simulated process, the Mq-sigma is highly sensitive to process variations and can detect incipient faults earlier than other methods, i.e., it has the lowest detection delay and FAR. •A novel multivariate q-sigma rule for incipient fault detection in dynamic process.•The upper and lower control limit is considered for each variable individually.•Process variation in a window is observed to monitor incipient fault.•The distribution of incipient and serious faults is discussed.