Supervised linear dynamic system model for quality related fault detection in dynamic processes. (August 2016) - Ge, Zhiqiang; Chen, Xinru
Highlights: Linear dynamic system (LDS) is introduced to handle dynamic and stochastic features of the process data. A supervised form of the LDS model has been developed. An on-line quality-related fault detection algorithm is designed. Superiority of the developed method is evaluated through TE benchmark process. Abstract: Dynamic and uncertainty are two main features of industrial processes data which should be paid attentions when carrying out process monitoring and fault diagnosis. As a typical dynamic Bayesian network model, linear dynamic system (LDS) can efficiently deal with both dynamic and uncertain features of the process data. However, the quality information has been ignored by the LDS model, which could serve as a supervised term for information extraction and fault detection. In this paper, a supervised form of the LDS model is developed, which can successfully incorporate the information of quality variables. With this additional data information, the new supervised LDS model can provide a quality related fault detection scheme for dynamic processes. A detailed industrial case study on the Tennessee Eastman benchmark process is carried out for performance evaluation of the developed method.