Imperfect predictive maintenance model for multi-state systems with multiple failure modes and element failure dependency.

Abstract

The objective of this study is to develop a practical

statistical model for imperfect predictive maintenance

based scheduling of multi-state systems (MSS) with

reliability dependent elements and multiple failure modes.

The system is modeled using a Markov state diagram and

reliability analysis is performed using the Universal

Generating Function (UGF) technique. The model is

simulated for a case study of a power generation transmission

system. The various factors influencing the

predictive maintenance (PdM) policy such as maintenance

quality and user threshold demand are examined and the

impact of the variation of these factors on system

performance is quantitatively studied. The model is found

to be useful in determining downtime schedules and

estimating times to replacement of an MSS under the PdM

policy. The maintenance schedules are devised based on a

"system-perspective" where failure times are estimated by

analyzing the overall performance distribution of the

system. Simulation results of the model reveal that a slight

improvement in the "maintenance quality" can 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. Moreover, the studies reveal that in order to

reduce the frequency of maintenance actions, it is

necessary to lower the minimum user expectations from the

system, 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

industry's needs.