Nonparametric, real-time detection of process deteriorations in manufacturing with parsimonious smoothing

Machine faults and systematic failures are resulted from manufacturing process deterioration. With early recognition of patterns closely related to process deterioration, e.g., trends, preventative maintenance can be conducted to avoid severe loss of productivity. Change-point detection identifies the time when abnormal patterns occur, thus it is ideal for this purpose. However, trend detection is not extensively explored in existing studies about change-point detection–the widely adopted approaches mainly target abrupt mean shifts and offline monitoring. Practical considerations in manufacturing cast additional challenges to the methodology development: data complexity and real-time detection. Data complexity in manufacturing restricts the utilization of parametric statistical modeling; the industrial demand for online decision-making requires real-time detection. In this article, we develop an innovative change-point detection method based on Parsimonious Smoothing that targets trend detection in nonparametric, online settings. The proposed method is demonstrated to outperform benchmark approaches in capturing trends within complex data. A case study validates the feasibility and performance of the proposed method on real data from automotive manufacturing.