Hidden Markov model based fault diagnosis for stamping processes

Metal stamping process plays a very important role in the modern manufacturing industry. Owing to an ever-increasing demand for better quality at reduced cost, a practical on-line monitoring and diagnosis system is of much appeal. However, the stamping process is a complicated transient process involving a large number of variables. It is rather difficult to monitor and diagnose by classical methods such as statistical classification. In this paper, a new method for fault detecting the stamping process is developed. First, it uses a number of autoregressive (AR) models to model the monitoring signal in different time periods of a stamping operation and uses the residues as the features. Then, it uses a Hidden Markov Model (HMM) for classification. The experiment results indicate that the new method is effective with a success rate between 80% and 90%. (C) 2003 Elsevier Ltd. All rights reserved.