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Functional failure diagnosis approach based on Bayesian network for manufacturing systems

The failure of traditional manufacturing systems mostly refers to the physical failure of the production equipment that constitutes the manufacturing systems. With the advancement of manufacturing technology and the improvement of the level of intelligence, the physical failure of the conventional equipment in the daily operation of the system is rare, but the quality of the work-in-progress (WIP) is unqualified frequently. Especially the hidden functional failures such as reliability degradation of the final product have become increasingly prominent. How to model and characterize the functional failure of manufacturing systems has become a bottleneck restricting the application and development of holistic PHM (Prognostic and Health Management) technology of manufacturing systems. Therefore, a novel functional failure modeling and diagnostic strategy for intelligent manufacturing systems based on RQR chain is proposed in this paper, which includes the manufacturing system reliability (R) data, manufacturing process quality (Q) data and the produced product reliability (R) data. Firstly, the definition of the functional degradation process and principle of manufacturing systems is clarified from the perspective of RQR chain. Secondly, based on the established RQR chain, the functional fault connotation of manufacturing systems is defined, and the KPCs (key product characteristics) in the Bayesian network of integrated manufacturing systems are utilized. Big data are analyzed to model and predict the functional fault state of the running manufacturing systems. Thirdly, based on the relationship of RQR chain from right to left, the holistic functional fault diagnosis strategy is given. Finally, a case study of a manufacturing system for cylinder head is presented to verify the proposed approach.