Research On Methods For Condition Based Equipment Health Prognosis And Integrated Maintenance Model

With the development of modern industrial technologies, the health management,and maintenance directly affect the production operation and the economic benefits ofenterprises. The reliability and maintenance status of equipment ensure the normaloperation of the system and are the important survival condition of enterprises. Thus,the equipment maintenance has increasingly prominent role and status in theproduction and operation of enterprises, and is the basis to reduce production costs andensure the production efficiency. Some maintenance strategies are studyed, but theyignore the influence of health prognosis.Based on the available research on equipment health prognosis and maintenance,it studies the equipment operational state, describes the equipment degradation andobtains the equipment health prognosis, which provides the making decision basis forthe integrated dynamic maintenance planning. The resources and maintenance risk canbe integrated into the dynamic maintenance model. The single-sensor online healthprognosis is studied first. Hidden semi-Markov model (HSMM) is applied into theonline health states recognition and prognosis of equipment. Then, based on the singlecondition information online health prognosis, the multi-sensor health prognosis isstudied, and the residual useful lifetime prediction model can be proposed. Based onthe online health prognosis, the integrated dynamic maintenance model can beproposed, and the maintenance area can be improved. Finally, based on the integratedmaintenance model, the maintenance of multi-component equipment is described.Based on the above previous research and literature, the disadvantage and advantage of this research can be analyzed. This paper studies the health prognosis andmaintenance strategy. The following parts can be focused.(1) The single-senor online health prognostic model can be proposedThis paper addresses prognostic methods based on HSMM by using sequentialMonte Carlo (SMC) method. HSMM is applied to obtain the transition probabilitiesamong health states and the state durations. The SMC method is adopted to describe theprobability relationships between health states and the monitored observations ofequipment. This paper proposes a novel multi-step-ahead health recognition algorithmbased on joint probability distribution to recognize the health states of equipment andits health state change point. A new online health prognostic method is also developedto estimate the residual useful lifetime (RUL) values of equipment. At the end of thepaper, a real case study is used to demonstrate the performance and potentialapplications of the proposed methods for online health prognosis of equipment.(2) The multi-sensor health prognostic model can be proposedFirst, based on the single-senor online health prognostic model, the basicalgorithms of HSMM are modified in order for decreasing computation and spacecomplexity, and the modified HSMM with multi-sensor information is applied, inwhich the hidden degradation process can be seen as the system state. Then, theadaptive HSMM (AHSMM) is proposed for hidden degradation state identification,while the maximum likelihood linear regression transformations method is used totrain the output and duration distributions to re-estimate all unknown parameters. TheAHSMM can also be used to obtain the transition probabilities among health states andhealth state durations of a complex system. Finally, the main results are verified by acase study, and the results show that the AHSMM with multi-sensor information has abetter performance for health prognosis than HSMM.(3) Based on online health prognosis, the integrated dynamic maintenancemodel can be proposedThe integrated dynamic maintenance includes the deterioration and aginginformation into the maintenance for improving the overall decisions. This paperpresents an integrated decision model which considers both health prognosis and theresource planning. Based on online health prognosis, the system multi-failure states canbe classified and the transition probabilities among the multi-failure states can be generated. The upper triangular transition probability matrix is used to describe thesystem deterioration and the changing of transition probability is used to denote thesystem aging process. And the resource planning is integrated into the maintenancemodel for different failure states. Finally, a bi-level dynamic programmingmaintenance model is proposed to obtain the optimal maintenance strategy and therisks of maintenance actions are analyzed.Based on the integrated maintenance, the multi-component maintenance model isestablished, which includes degradation, maintenance cost and maintenance action. Forthe degradation, the failure changing trend can be obtained by the diagnostic andprognostic information. The minor maintenance action, the imperfect maintenanceaction and the replacement maintenance action are defined, and the impaction on thefailure is described. For maintenance cost, the failure cost, maintenance cost, resourcecost and downtime cost can be considered. Based on the cost model of eachmaintenance activity, the multi-stage total cost rate model can be proposed.A case study is used to demonstrate the implementation and potential applicationsof the proposed methods.The three parts of research contact closely with each other, and forms a systematicframework for equipment maintenance scheduling policy. Based on the online healthprognosis, this paper develops the integrated dynamic maintenance strategy consideringthe degradation and maintenance resources. The research can improve the maintenancelevel and reliability, reduce the maintenance cost, raise utilization, and ultimatelyenhance the competitiveness for enterprises. It can also expand the field of maintenancemanagement of manufacturing systems, and provide effective decision support andscientific guidance for system maintenance strategies of manufacturing enterprises.