Xiao Han

Integrated predictive maintenance strategy for manufacturing systems by combining quality control and mission reliability analysis

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.

Health prognosis approach for manufacturing systems based on quality state task network

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.

Cost-oriented predictive maintenance based on mission reliability state for cyber manufacturing systems:

With the advent of Industry 4.0, maintenance strategy faces new demands to avoid the hysteresis of the conventional passive maintenance mode and the non-feasibility of the periodic preventive maintenance model. In view of the inherent polymorphism of manufacturing systems and with the objective of maximizing benefits, a novel cost-oriented predictive maintenance based on mission reliability state for manufacturing systems is proposed. First, the cyber-physical system is adopted to organize and analyze big data in the operational process of manufacturing systems in terms of predictive analytics in cyber manufacturing environment. Second, a new connotation of mission reliability is defined based on the big operational data to comprehensively characterize the dynamic state of the equipment health states and the qualified degree of the production task. Third, the predictive maintenance mode based on mission reliability state is quantified by the comprehensive cost, and the relationship between mission reliability and cost is established. Thereafter, cost-oriented dynamic predictive maintenance strategy is proposed. Finally, a case study on the maintenance decision-making problem of a cylinder head manufacturing system is presented. The final result shows that the comprehensive cost can be further reduced by the proposed method relative to the traditional periodic preventive maintenance strategy.

Product quality oriented predictive maintenance strategy for manufacturing systems

Maintenance, production and quality are strongly linked to each other. Predictive maintenance is an effective way to eliminate the potential failures and ensure the stable operation of the manufacturing system, and further improve the reliability of the manufacturing system and the quality of manufactured products. Regarding the intelligent manufacturing philosophy “prediction and manufacturing#x201D;, a novel predictive maintenance strategy is proposed, in which the product quality control is integrated into predictive maintenance decision-making. First, the key process variables that characterize equipment wear are identified and integrated into the modeling of the equipment failure rate. Second, quality deviation that characterizes product quality level is defined based on co-effect between manufacturing system component reliability and product quality (i.e., Q-R chain). Third, the optimal maintenance strategy is obtained by optimizing the quality cost, maintenance cost, and interruption cost simultaneously. Finally, a case study on the predictive maintenance strategy decision-making for a cylinder head manufacturing system is presented to illustrate the effectiveness of the proposed method. The final result shows that the proposed method can achieve a superior economic performance relative to the conventional preventive maintenance mode.

Mission reliability modeling for multi-station manufacturing system based on Quality State Task Network

Multi-state-oriented mission reliability modeling is the premise of intelligent scheduling and predictive maintenance for the multi-station manufacturing system. Previous studies on reliability modeling for manufacturing system could only provide a static reliability model based on the basic reliability of the components of manufacturing systems, which cannot support reliability-oriented production scheduling and preventive maintenance effectively. To resolve this dilemma, a multi-state-oriented mission reliability modeling for multi-station manufacturing system is proposed. First, the mapping relationship between the produced product reliability and mission reliability of the manufacturing system is proposed as the basis for modeling, and the connotation of mission reliability is elaborated by analyzing the polymorphisms of the multi-station manufacturing system. Second, a graphical representation to improve the state transparency named as Quality State Task Network is proposed based on production data by integrating the variability of task-demands propagation as well as the multi-state in material quality and machine performance. Third, the mission reliability modeling method based on the Quality State Task Network is proposed. Finally, a case study of cylinder-head manufacturing system has been applied to validate the proposed model.

Mission reliability evaluation based on operational quality data for multistate manufacturing systems

Modern and intelligent manufacturing systems have a prominent multistate feature. However, previous studies of reliability analysis of multistate manufacturing systems mostly focused on the basic reliability of manufacturing systems but disregarded their operating characteristics, which has hindered the development of Prognostics and Health Management technique for intelligent manufacturing systems. Therefore, an evaluation approach of mission reliability for multistate manufacturing systems based on operational quality data is proposed in this paper. First, from the systematic viewpoint of the composition and operational principle of the manufacturing system, the relationship among production task execution state, production equipment degradation state, and produced product quality state is expounded, and the connotation of the mission reliability of multistate manufacturing systems is defined. Second, an extended state task network (ESTN) is presented to organise operational quality data by considering the quality state of work in process (WIP). Third, a fusion model of operational quality data for manufacturing systems is established with the aid of the ESTN, and an operational quality data-oriented evaluation method of mission reliability is been put forward. Finally, a case study of a manufacturing system for a cylinder head is conducted to verify the proposed approach.

Comprehensive cost oriented predictive maintenance based on mission reliability for a manufacturing system

In this paper, a comprehensive cost oriented dynamic predictive maintenance policy based on mission reliability state is developed for a multi-state single-machine manufacturing system. In view of the inherent polymorphism of manufacturing systems (i.e., dynamic production scheduling and performance degradation), the connotation of mission reliability of equipment is defined and modeled based on the processing capacity distribution which integrates multiple fault data. Further, the relationship between mission reliability and performance of equipment is established by using the unavailability as the intermediary. The optimal predictive maintenance policy, the best mission reliability threshold for performing predictive maintenance action, is obtained by minimizing the comprehensive cost which includes processing capacity loss, corrective maintenance cost, predictive maintenance cost and indirect loss caused by failing to meet due dates over the planning period. This paper will also evaluate a manufacturing system of the cylinder head of an automotive engine as a case study to illustrate the effectiveness and advantages of the proposed method. The final result shows that a more significant economic benefit can be achieved by the proposed approach, which considers the mission reliability and comprehensive cost relative to the periodic preventive maintenance policy.

An integrated multi-objective production scheduling model considering the production quality state

The production quality state is the specific embodiment of the implementation effect of the production tasks, which comprehensively characterizes the machining precision of the raw materials in each manufacturing process. Therefore, the production quality state of the manufacturing process has a decisive impact on the reliability of the finished product. However, there are few studies on multi-objective job-shop optimization scheduling that take the production quality state as a constraint. In this paper, a method of deviation flow model to quantify the quality state of the manufacturing process is proposed firstly. Then, a multi-objective production scheduling model considering the production quality state, the makespan time and the performance state of manufacturing equipment is proposed. Thirdly, a scheduling decision on workstation based on heuristic algorithm is introduced. Finally, the model is verified by a case study of a car engine production task scheduling optimization.

Health state modeling for complex manufacturing system based on RQR chain and Hidden Markov Model

With the advent of Industry 4.0, the structure and function of the manufacturing system are becoming more and more complicated. Fault diagnosis and health management with predictive ability are the prerequisite for the effective and intelligent operation of the manufacturing system. Different from previous studies about the Prognostics and Health Management are always confined to the static modeling of the basic reliability of system components, a novel approach based on the big operational data and Hidden Markov Model is proposed in this paper. Firstly, the RQR chain is proposed to organize the big operational data, which includes the manufacturing system reliability (R) data, manufacturing process quality (Q) data and the produced product reliability (R) data. Secondly, a new concept of the health state of the manufacturing system is presented to highlight the operational performance of the production task. Thirdly, evolution of the key quality characteristics is adopted to fuse the big operational data, and the Hidden Markov Model (HMM) is used to model the health state of manufacturing systems. Finally, a case study of a manufacturing system for cylinder head is carried out to verify the effectiveness of the proposed approach. The final result shows that the proposed model has favorable dynamic modeling and prognostication capabilities.

Health estimation method of manufacturing systems based on multidimensional state prediction

Systematic and accurate health estimation for the running manufacturing system is the prerequisite to implement production scheduling and predictive maintenance. This enables remedial actions to be taken in advance and reschedule of production if necessary. However, existing studies pay more attention to the failure diagnosis of equipment, while ignoring the output and input characteristics of the manufacturing system. Therefore, this paper presents a novel method for health estimation of manufacturing systems from three dimensions of equipment performance, product quality and task execution Firstly, the equipment performance state is represented based on the theory of polymorphism. Secondly, the quality state is defined to describe the qualified degree of the output products according to the response model. Thirdly, a task execution state modeling method is proposed, and the correlation between sub-task execution states is considered based on Copula function. Then, an integrated model is built to prognosis the change trend of manufacturing system health by integrating the above three states. Finally, a case study conducted to illustrate the effectiveness of the proposed method.