In-profile monitoring on univariate profile with application to industrial busbar

Wei Zhang, Zhen He, Shuguang He, Niu Zhanwen, Yanfen Shang and Lisha Song

IISE Transactions, 2025, vol. 57, issue 11, 1295-1310

Abstract: In the current research on profile monitoring, most studies treat each profile as a whole to design the monitoring statistic. These monitoring methods can only detect whether there exist some anomalies in the process after a complete profile sample is collected. This leads to a lag between the occurrence of a shift and the signaling of an alarm, which hinders engineers from promptly intervening in the out-of-control process. To address this limitation, an in-profile monitoring scheme is proposed in this article, in which the dynamic influence mechanism of covariates on the response variable is considered. In phase-I, a random varying-coefficient model is utilized to model the dynamic time-varying relationship between covariates and the response variable, and the model parameters are estimated. In Phase-II, for the sequential observations generated by the monitored process, a new monitoring scheme based on the generalized likelihood ratio test is designed. This scheme can adapt to within-profile autocorrelation and arbitrary design points. To enhance online computational efficiency, recursive formulas for calculating the charting statistic are developed. Numerical studies demonstrate that the proposed scheme exhibits a satisfactory and robust monitoring performance. Finally, an application to industrial busbar running process monitoring is given to demonstrate the implementation of the scheme.

Date: 2025
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DOI: 10.1080/24725854.2024.2415976

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