Predictive maintenance (PdM) is an effective means to eliminate potential failures, ensure stable equipment operation and improve the mission reliability of manufacturing systems and the quality of products, which is the premise of intelligent manufacturing. Therefore, an integrated PdM strategy considering product quality level and mission reliability state is proposed regarding the intelligent manufacturing philosophy of ‘prediction and manufacturing’. First, the key process variables are identified and integrated into the evaluation of the equipment degradation state. Second, the quality deviation index is defined to describe the quality of the product quantitatively according to the co-effect of manufacturing system component reliability and product quality in the quality–reliability chain. Third, to achieve changeable production task demands, mission reliability is defined to characterise the equipment production states comprehensively. The optimal integrated PdM strategy, which combines quality control and mission reliability analysis, is obtained by minimising the total cost. Finally, a case study on decision-making with the integrated PdM strategy for a cylinder head manufacturing system is presented to validate the effectiveness of the proposed method. The final results shows that proposed method achieves approximately 26.02 and 20.54% cost improvement over periodic preventive maintenance and conventional condition-based maintenance respectively.

Due to the artificial division of a machine performance state and unpredictable external working conditions, the performance and state transition strength of multistate machines (that is, their fuzzy multistate characteristic) cannot be accurately identified. Moreover, existing research on the reliability evaluation of multistate systems does not consider the operating mechanism of a manufacturing system. Therefore, this article proposes a mission reliability evaluation method for fuzzy multistate manufacturing systems based on an extended stochastic flow network (ESFN). First, from the relationship between the production task execution state, production machine degradation state, and produced product quality state, the operation mechanism of a fuzzy multistate manufacturing system is proposed, and the connotation of mission reliability is further defined. Second, an ESFN model of the multistate manufacturing system based on the operating mechanism of a fuzzy multistate manufacturing system is established. Third, on the basis of a fuzzy Markov model, the mission reliability evaluation method of the fuzzy multistate manufacturing system is proposed. Finally, a case study of the manufacturing system for producing a ferrite phase shifting unit is presented to verify the proposed approach, and a sensitivity analysis is conducted to analyze the impacts of the parameters in the proposed model.

Reliability assurance is a series of activities that ensure that the product reliability requirements be realised in a product life cycle. However, as reliability assurance activities are usually only implemented in design and usage, the integration of reliability assurance with quality control in production has not attracted the attention it deserved, thereby hindering production performance improvement to satisfy increasingly stringent customer requirements. To this end, this paper proposes a reliability-oriented quality control framework to integrate quality control and reliability assurance in production according to the principles of total quality management (TQM). Firstly, from the integrated quality and reliability assurance perspective, the RQR chain is proposed to represent the bidirectional relationships among the three basic management objects in a production process, namely, manufacturing system reliability (R), manufacturing process quality (Q), and the produced product reliability (R). Secondly, a reliability-oriented quality control framework for the production process based on the RQR chain is presented to provide the control clue for a manufacturing enterprise. Thirdly, the validity of the proposed approach is verified in a vehicle engine manufacturing enterprise of China. The proposed RQR chain as a decision-support model for TQM is eventually proven effective in promoting the integration of quality control and reliability assurance in production.