Hidden Markov model-based process monitoring system
Sensor signals produced in industrial manufacturing processes contain valuable information about the condition of operations. However, extracting the appropriate feature for effective fault diagnosis is difficult. Moreover, the adaptability and flexibility of current fault diagnosis systems are often found wanting in real-world applications. Unfortunately, it is essential to rebuild most fault diagnosis systems when new fault types emerge. This paper presents an intelligent fault diagnosis system based on a hidden Markov model. Introducing the concepts of time marginal energy and frequency marginal energy, the features of which can be acquired by the wavelet packet technique satisfy the requirements for fault diagnosis. By utilizing the best tree principle, this method not only extracts the feature automatically without a priori experience but also compresses the data; both of which ensure a system that is practical for real-time application. The new diagnosis system developed here is efficient and effective, as demonstrated by the model developed and applied to a real-time sheet metal stamping process. Based on tests conducted during two experiments (one based on simple blanking, the other on progressive operations) and related comparisons, the proposed method is substantially more effective than other approaches. In addition, the new method requires that only related models be created for new fault types, which results show are ideal for shop floor applications.
hidden markov model fault diagnosis marginal energy feature extraction wavelet packet decomposition sheet metal stamping operations