Health prognosis approach for manufacturing systems based on quality state task network. (April 2019)

• Record Type: Journal Article
• Authors: He, Yihai; Cui, Jiaming; Gu, Changchao; Han, Xiao; Chen, Zhaoxiang; Zhao, Yixiao
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 233:Number 5(2019)
• Page Start: 1573
• Page End: 1587
• Publication Date: 2019-04
• Subjects: Manufacturing system -- health prognosis -- big operational data -- quality state task network -- mission reliability; Mechanical engineering -- Periodicals; Engineering -- Management -- Periodicals; Manufacturing processes -- Periodicals; 629.8
• DOI: 10.1177/0954405418780174
• Languages: English
• ISSNs: 0954-4054

Abstract

Previous studies on health prognosis are exceedingly dependence on the failure data and sensor data of a single component of manufacturing systems, and the holistic health prognosis techniques applicable to whole manufacturing systems still remain a challenge due to its increasingly physical and functional complexity. Therefore, a generalized health prognosis method is presented based on the deep fusion of quality-oriented big data of operational process of manufacturing systems. First, the generalized connotation of manufacturing system health is explained from the aspects of the physical composition and functional characteristics of manufacturing systems, and the quality state task network is proposed to organize quality-oriented big operational data, which improve the state transparency of the manufacturing system and lay the foundation of holistic health prognosis. Second, key characterization parameters in quality state task network are defined. Specifically, the performance state is analyzed based on multistate characteristics by considering the effects of stochastic degradation processes; the product quality state is quantified by using a process model that is established based on monitoring and inspection data; and the task execution state is quantitatively described by analyzing the evolution of task demand among machines. Third, an integrated model is built by integrating the three above-mentioned states as two key indicators, namely, qualified degree and mission reliability, for the comprehensive prognosis of the health of manufacturing systems. Finally, the effectiveness of the proposed approach is verified with a case study on the health prognosis of a cylinder head manufacturing system.