A framework to practical predictive maintenance modeling for multi-state systems

Abstract A simple practical framework for predictive maintenance (PdM)-based scheduling of multi-state systems (MSS) is developed. The maintenance schedules are derived from a system-perspective using the failure times of the overall system as estimated from its performance degradation trends. The system analyzed in this work is a flow transmission water pipe system. The various factors influencing PdM-based scheduling are identified and their impact on the system reliability and performance are quantitatively studied. The estimated times to replacement of the MSS may also be derived from the developed model. The results of the model simulation demonstrate the significant impact of maintenance quality and the criteria for the call for maintenance (user demand) on the system reliability and mean performance characteristics. A slight improvement in maintenance quality is found to postpone the system replacement time by manifold. The consistency in the quality of maintenance work with minimal variance is also identified as a very important factor that enhances the system's future operational and downtime event predictability. The studies also reveal that in order to reduce the frequency of maintenance actions, it is necessary to lower the minimum user demand from the system if possible, ensuring at the same time that the system still performs its intended function effectively. The model proposed can be utilized to implement a PdM program in the industry with a few modifications to suit the individual industrial systems’ needs.

[1]  Marcello Braglia,et al.  The analytic hierarchy process applied to maintenance strategy selection , 2000, Reliab. Eng. Syst. Saf..

[2]  Toshio Nakagawa,et al.  A cumulative damage shock model with imperfect preventive maintenance , 1991 .

[3]  Antoine Grall,et al.  A condition-based maintenance policy for stochastically deteriorating systems , 2002, Reliab. Eng. Syst. Saf..

[4]  Y. X. Zhao,et al.  On preventive maintenance policy of a critical reliability level for system subject to degradation , 2003, Reliab. Eng. Syst. Saf..

[5]  Charles E Ebeling,et al.  An Introduction to Reliability and Maintainability Engineering , 1996 .

[6]  D. Elmakis,et al.  Redundancy optimization for series-parallel multi-state systems , 1998 .

[7]  J.-K. Chan,et al.  Modeling repairable systems with failure rates that depend on age and maintenance , 1993 .

[8]  Toshio Nakagawa,et al.  Optimum Policies When Preventive Maintenance is Imperfect , 1979, IEEE Transactions on Reliability.

[9]  Hoang Pham,et al.  A quasi renewal process and its applications in imperfect maintenance , 1996, Int. J. Syst. Sci..

[10]  M. M. O'Kane,et al.  Intelligent motors moving to the forefront of predictive maintenance , 1999 .

[11]  R. Drenick.  THE FAILURE LAW OF COMPLEX EQUIPMENT , 1960 .

[12]  T. H. Savits,et al.  Age Dependent Minimal Repair. , 1985 .

[13]  Masaaki Kijima,et al.  Periodical replacement problem without assuming minimal repair , 1988 .

[14]  E. C. Fitch.  1 – Maintenance Technology , 1992 .

[15]  Hoang Pham,et al.  OPTIMAL AGE-DEPENDENT PREVENTIVE MAINTENANCE POLICIES WITH IMPERFECT MAINTENANCE , 1996 .

[16]  Wendai Wang,et al.  Reliability quantification of induction motors-accelerated degradation testing approach , 2002, Annual Reliability and Maintainability Symposium. 2002 Proceedings (Cat. No.02CH37318).

[17]  Antoine Grall,et al.  Continuous-time predictive-maintenance scheduling for a deteriorating system , 2002, IEEE Trans. Reliab..

[18]  M. Kijima.  SOME RESULTS FOR REPAIRABLE SYSTEMS WITH GENERAL REPAIR , 1989 .

[19]  A. Grall,et al.  Continuous time predictive maintenance scheduling for a deteriorating system , 2001, Annual Reliability and Maintainability Symposium. 2001 Proceedings. International Symposium on Product Quality and Integrity (Cat. No.01CH37179).

[20]  Toshio Nakagawa,et al.  Replacement policies of a shock model with imperfect preventive maintenance , 1992 .

[21]  Pradip Kumar Sadhu,et al.  Deterministic and stochastic approach for safety and reliability optimization of captive power plant maintenance scheduling using GA/SA-based hybrid techniques: A comparison of results , 2007, Reliab. Eng. Syst. Saf..

[22]  Andrew K. S. Jardine,et al.  Optimal Replacement Policy for a General Model with Imperfect Repair , 1992 .

[23]  Gregory Levitin,et al.  Optimization of imperfect preventive maintenance for multi-state systems , 2000, Reliab. Eng. Syst. Saf..

[24]  M.Carmen Carnero Moya.  The control of the setting up of a predictive maintenance programme using a system of indicators , 2004 .

[25]  Sebastian Martorell,et al.  Age-dependent reliability model considering effects of maintenance and working conditions , 1999 .

[26]  Mazhar Ali Khan Malik,et al.  Reliable Preventive Maintenance Scheduling , 1979 .

[27]  Hongzhou Wang,et al.  A survey of maintenance policies of deteriorating systems , 2002, Eur. J. Oper. Res..

[28]  Hoang Pham,et al.  Optimal maintenance policies for several imperfect repair models , 1996, Int. J. Syst. Sci..

[29]  Gregory Levitin,et al.  Multi-State System Reliability - Assessment, Optimization and Applications , 2003, Series on Quality, Reliability and Engineering Statistics.

[30]  Henry W. Block,et al.  A general age replacement model with minimal repair , 1988, Naval Research Logistics (NRL).

[31]  Toshio Nakagawa,et al.  Imperfect Preventive-Maintenance , 1979, IEEE Transactions on Reliability.