Progressive stamping processes have been applied to fabricate an extended range of products from centimeter-scale parts to meter-scale parts. The quality of stamped products may vary and be out of specification due to various anomalies during manufacturing process. Therefore, an effective online health monitoring and fault diagnosis technique is of great practical significance. This paper develops a two-stage systematic approach to enhance the fault detection and fault identification capability for the progressive stamping process with aggregated system-level tonnage signals. The first stage uses a combined Haar transform and power spectrum analysis to map features extracted from aggregated signals to individual operations. The second stage develops a two-step control chart strategy for anomaly detection and identification. The proposed method can improve the monitoring effectiveness and the quality assessment of individual operations based on an aggregated tonnage signal especially when single working range of different operations in the multi-station system are highly overlapped. The results show the method efficacy of quick and accurate anomaly detection and identification in real time.

This content is only available via PDF.
You do not currently have access to this content.