A novel decentralized detection framework for quality-related faults in manufacturing industrial processes

Quality-related fault detection is the key subject under process monitoring and fault diagnosis (PM-FD) framework, which is an effective mean to guarantee safety production and obtain reliable product quality, and thus, has recently become active areas of process control fields. In this paper, a novel decentralized detection method for quality-related faults is proposed for manufacturing industrial processes, which will help field engineers to purposefully and real-time observe the operation state of the production process. Specifically, the main innovations are: 1) the linear and nonlinear dynamic interdependencies between process and quality variables are revealed and quantified by the minimal redundancy maximal relevance (mRMR) algorithm, and the most representative variables are selected in each subprocess; 2) a new mixed kernel function based dynamic mixed kernel principal component analysis (DMKPCA) model is designed for enhancing the generalization ability and maintaining the data characteristics of original sample space; 3) unified monitoring statistics are constructed in the subprocess level, and Bayesian fusion is implemented for forming monitoring decisions from the plant-wide level. Finally, a case study on an actual hot rolling mill process (HSMP) is finally given to validate the effectiveness of the proposed scheme, and several competitive methods are applied to carry out the quality-related fault detection process.