An Intelligent Online Monitoring and Diagnostic System for Manufacturing Automation

Condition monitoring and fault diagnosis in modern manufacturing automation is of great practical significance. It improves quality and productivity, and prevents damage to machinery. In general, this practice consists of two parts: 1) extracting appropriate features from sensor signals and 2) recognizing possible faulty patterns from the features. Through introducing the concept of marginal energy in signal processing, a new feature representation is developed in this paper. In order to cope with the complex manufacturing operations, three approaches are proposed to develop a feasible system for online applications. This paper develops intelligent learning algorithms using hidden Markov models and the newly developed support vector techniques to model manufacturing operations. The algorithms have been coded in modular architecture and hierarchical architecture for the recognition of multiple faulty conditions. We define a novel similarity measure criterion for the comparison of signal...

• Type: Journal
• Year: 2008
• Where: TASE
• Authors: Ming Ge, Yangsheng Xu, Ruxu Du
• Keywords: Hidden Markov Models, Manufacturing Operations, Similarity Measure Criterion, Software Engineering, TASE 2008