A Change Point Approach for Phase-I Analysis in Multivariate Profile Monitoring and Diagnosis

Process monitoring and fault diagnosis using profile data remains an important and challenging problem in statistical process control (SPC). Although the analysis of profile data has been extensively studied in the SPC literature, the challenges associated with monitoring and diagnosis of multichannel (multiple) nonlinear profiles are yet to be addressed. Motivated by an application in multi-operation forging processes, we propose a new modeling, monitoring and diagnosis framework for phase-I analysis of multichannel profiles. The proposed framework is developed under the assumption that different profile channels have similar structure so that we can gain strength by borrowing information from all channels. The multi-dimensional functional principal component analysis is incorporated into change-point models to construct monitoring statistics. Simulation results show that the proposed approach has good performance in identifying change-points in various situations compared with some existing methods. The codes for implementing the proposed procedure are available in the supplementary material.

在统计过程控制（Statistical Process Control, SPC）领域，基于轮廓数据的过程监控与故障诊断始终是兼具重要性与挑战性的研究课题。尽管SPC领域的现有文献已对轮廓数据分析开展了广泛研究，但多通道（多组）非线性轮廓的监控与诊断相关挑战仍有待解决。受多工序锻造工艺的实际应用启发，我们针对多通道轮廓的第一阶段分析，提出了一套全新的建模、监控与诊断框架。所提框架基于不同轮廓通道结构相似这一假设构建，借此可通过借用所有通道的信息来增强分析效力。我们将多维函数主成分分析（Multi-dimensional Functional Principal Component Analysis）融入变点模型，以构建监控统计量。仿真实验结果表明，相较于现有若干方法，所提方法在多种场景下的变点识别任务中均表现优异。实现所提流程的代码已在补充材料中公开。