APFC: Adaptive Particle Filter for Change Point Detection of Profile Data in Manufacturing Systems

Change point detection is critical in quality inspection and assessment in manufacturing systems. As one of the most popular Bayesian inference techniques, particle filter algorithm has been successfully applied to estimate the change points of profile data in various manufacturing processes. However, particle filter is computationally expensive, which hinders its wide application for online change point detection. To overcome this challenge, we propose an adaptive particle filter algorithm (APFC) for online change point detection in this paper. With the full consideration of change mechanism, the particle sizes are adaptively selected for parameter estimation as time evolves. The proposed method is validated through extensive simulation studies and two real cases of pipe tightening process and nano manufacturing process.