Title: Operational risk modeling based on operational data fusion for multi-state manufacturing systems.

Fulltext:https://journals.sagepub.com/eprint/SEB5DXKXPCR46EXZMTKY/full
Authors:Yixiao Zhao, Yihai He, Fengdi Liu, Xiao Han, Anqi Zhang, Di Zhou, Yao Li.
Journal :Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability.
SCI Impact factor:1.313(Q3) .
Published date:September 25,2019.

Abstract:
Predictive health analysis and diagnostics are essential functional elements of “Smart Manufacturing,” but existing studies are mostly limited to discrete manufacturing equipment without a holistic analysis on the entire system health state. In consideration of the enhancement of risk control in production quality management advocated in ISO 9001:2015, modeling and monitoring the risk during operation are the core aspects and bottlenecks for achieving prognostics. Therefore, a novel holistic operational risk modeling approach for discrete manufacturing systems is proposed by fusing the operational data to quantify the risk-oriented operational performance systematically. First, the connotation of operational risk is expounded and associated with system operational mechanism and mission reliability connotation. Second, a modeling foundation is proposed on the basis of quantifying machine, task execution and work-in-process quality from mission reliability framework in accordance with the operational data characteristics and properties. Third, operational risk evaluation is conducted using the above indicators, where reasonable aggregation of three indicators is performed by fuzzy evidence theory and the risk levels are determined. Finally, a case study is conducted to illustrate the effectiveness and advantages of the proposed approach.