In this paper, we consider a repairable system whose failures follow a non‐homogenous Poisson process with the power law intensity function. The system is subject to corrective and multiple types of preventive maintenance. A corrective maintenance has a minimal effect on the system; however, a preventive maintenance may reduce the system's age. We assume the effects of different preventive maintenance on the system are not identical and derive the likelihood function to estimate the parameters of the failure process as well as the effects of preventive maintenance. Moreover, we derive the conditional reliability and the expected number of failures between two consecutive preventive maintenance types. The proposed methods are applied to a real case study of four trucks used in a mining site in Canada. Copyright © 2016 John Wiley & Sons, Ltd.

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In this thesis, a maintenance evaluation and improvement methodology is presented, which makes use of maintenance data to determine failure characteristics of repairable systems and the effectiveness of maintenance policies being conducted on them. The objective is to provide a way in which maintenance data can be collected, organized, cleaned and formatted to provide information on component failures analytics, system availability and utilization so as to determine flaws in maintenance strategies. The methodology also provides context for the study of maintenance effectiveness, and synthesizes its importance within the grander scheme of maintenance optimization of repairable systems. We consider a repairable system whose failures follow a Non-Homogenous Poisson Process (NHPP) with the power law intensity function. The system is subject to corrective and multiple types of preventive maintenance. We assume the effects of different preventive maintenance on the system are not identical, and estimate the parameters of the failure process as well as the effects of preventive maintenance. Ultimately, the methodology serves to guide maintenance designers in measuring the effectiveness of current maintenance policies and providing granular analysis on current failure trends to arrive at data-driven options for maintenance improvement. The proposed methodology was applied to a real case study of four AC-powered dump trucks used at an underground mine in Sudbury, Canada.

In this thesis, a maintenance evaluation and improvement methodology is presented, which makes use of maintenance data to determine failure characteristics of repairable systems and the effectiveness of maintenance policies being conducted on them. The objective is to provide a way in which maintenance data can be collected, organized, cleaned and formatted to provide information on component failures analytics, system availability and utilization so as to determine flaws in maintenance strategies. The methodology also provides context for the study of maintenance effectiveness, and synthesizes its importance within the grander scheme of maintenance optimization of repairable systems. We consider a repairable system whose failures follow a Non-Homogenous Poisson Process (NHPP) with the power law intensity function. The system is subject to corrective and multiple types of preventive maintenance. We assume the effects of different preventive maintenance on the system are not identical, and estimate the parameters of the failure process as well as the effects of preventive maintenance. Ultimately, the methodology serves to guide maintenance designers in measuring the effectiveness of current maintenance policies and providing granular analysis on current failure trends to arrive at data-driven options for maintenance improvement. The proposed methodology was applied to a real case study of four AC-powered dump trucks used at an underground mine in Sudbury, Canada.