Regional adaptive moving average for robust anomaly detection in wiring harness manufacturing

Wiring harnesses play a crucial role in automotive, railway, and electronic systems, with terminal crimping being a critical phase in their assembly. In this study, piezoelectric force sensors are used to monitor crimping waveforms generated during an industrial wiring-harness terminal crimping process. Recent studies have applied AI-based anomaly detection models that learn normal crimping patterns from early-stage production data. However, reliably detecting rare defects becomes increasingly challenging as waveform drift accumulates over long production runs, causing the distribution of newly acquired signals to diverge from that of the initial training data. Under these conditions, conventional AI-based monitoring and classical reference-updating