A real-time monitoring and diagnosis system for manufacturing automation

Condition monitoring and fault diagnosis in modern engineering practices is of great practical significance for improving the quality and productivity, preventing the machinery from damages. In general, this practice consists of two parts: extracting appropriate features from sensor signals and recognizing possible faulty patterns from the features. In order to cope with the complex manufacturing operations and develop a feasible system for real-time application, we proposed three approaches. By defining the marginal energy, a new feature representation emerged, while by real-time learning algorithms with support vector techniques and hidden Markov model representations, a modular software architecture and a new similarity measure were developed for comparison, monitoring, and diagnosis. A novel intelligent computer-based system has been developed and evaluated in over 30 factories and numerous metal stamping processes as an example of manufacturing operations. The real-time operation of this system demonstrated that the proposed system is able to detect abnormal conditions efficiently and effectively resulting in a low-cost, effective approach to real-time monitoring in manufacturing. The related technologies have been transferred to industry, presenting a tremendous impact in current automation practice in Asia and the world.