A novel data-driven approach to optimizing replacement policy

Parallel systems are a commonly used structure in reliability engineering. A common characteristic of such systems is that the failure of a component may not cause its system to fail. As such, the failure may not be immediately detected and the random (disruption) time at which the number of failed components reaches a certain predefined number may therefore be unknown. For such systems, scheduling maintenance policy is a difficult task, which is tackled in this paper. The paper assumes that times between inspections conform to a modulated Poisson process. This assumption allows the frequency of inspection responds to the variation of the disruption state. The paper then estimates the disruption time on the basis of inspection point process observations in the framework of filtering theorem. The paper develops a unified cost structure to jointly optimise inspection frequency and replacement time for the system when the lifetime distribution of a component follows the Pareto or exponential distribution. Numerical results are provided to show the application of the proposed model

Scheduling screening inspections for replaceable and non-replaceable systems

This dissertation focuses on developing inspection schedules to detect non-self- announcing events which can only detected by inspections. Failures of protective sys- tems ,such as electronic equipments, alarms and stand-by systems, incipient failures and the emergence of certain medical diseases are examples of such events. Inspec- tions are performed at pre-determined times to detect whether or not the event has occurred, and necessary actions are taken upon the detection. In this research, my interest is in developing effective inspection schedules to detect non-self-announcing events that balance system downtime and inspection effort. To evaluate the quality of an inspection schedule, I use the availability (for re- placeable) and the detection delay (for non-replaceable systems) as performance mea- sures. When the monetary cost of inspection and the cost of delay are difficult to determine, non-monetary performance measures become more meaningful. In this research, the focus is on maximizing availability or minimizing detection delay given a limited number of inspections or a limited inspection rate. I show that for replace- able and non-replaceable systems, it is possible to construct inspection schedules that perform better than periodic inspection with respect to our performance measures. The occurrence of the event I would like to detect may be influenced by certain individual characteristics. For instance, the risk of developing a certain type of dis- ease might be different for different subgroups within the population. In this case, because of the non-homogeneity in the population, benefits of performing screening tests may not be fully achieved for each sub-group by using an inspection strategy developed for the entire population. Thus, it may be of value for an individual to learn more information about his/her likehood to have the disease. To address this issue, I analyze the change in the expected delay if schedules are based on the whole population information or the individual information and provide numerical results

Optimal Maintenance Planning in Novel Settings

In this dissertation work, we focus on optimal planning of maintenance activities in several novel settings. First, we consider a maintenance optimization model for a system with periodic preventive maintenance (PM), and periodic imperfect inspections to detect hidden failures. Our stylized mathematical model is inspired by the increasingly popular remote monitoring practices. We describe, both analytically and numerically, important structural properties of the model, and propose a simple approach to find a globally optimal solution. In the second chapter, we investigate a maintenance planning scenario in which the implementation of PM is unpunctual. Under the assumption that the degree of the unpunctuality follows a known probability distribution, we formulate cost-rate minimizing models to study the impact of such deviations. We establish both analytical and numerical results for two specific types of maintenance policies common in practice, namely age replacement with and without minimal repair. Finally, we focus on "maintaining" the health status of a patient with a chronic disease by investigating an optimal medical treatment sequencing problem. We restrict our attention to the two treatment case, and simultaneously balance three tradeoffs inherent to these treatments, i.e., length of effectiveness delay, probability of effectiveness and cost/reward. We provide both theoretical conditions and numerical examples that indicate when, as a function of the model parameters, it is optimal to initiate treatment with one treatment versus the other

State and parameter estimation techniques for stochastic systems

This thesis documents research undertaken on state and parameter estimation techniques for stochastic systems in a maintenance context. Two individual problem scenarios are considered. For the first scenario, we are concerned with complex systems and the research involves an investigation into the ability to identify and quantify the occurrence of fault injection during routine preventive maintenance procedures. This is achieved using an appropriate delay time modelling specification and maximum-likelihood parameter estimation techniques. The delay time model of the failure process is parameterised using objective information on the failure times and the number of faults removed from the system during preventive maintenance. We apply the proposed modelling and estimation process to simulated data sets in an attempt to recapture specified parameters and the benefits of improving maintenance processes are demonstrated for the particular example. We then extend the modelling of the system in a predictive manner and combine it with a stochastic filtering approach to establish an adaptive decision model. The decision model can be used to schedule the subsequent maintenance intervention during the course of an operational cycle and can potentially provide an improvement on fixed interval maintenance policies. The second problem scenario considered is that of an individual component subject to condition monitoring such as, vibration analysis or oil-based contamination. The research involves an investigation into techniques that utilise condition information that we assume is related stochastically to the underlying state of the component, taken here to be the residual life. The techniques that we consider are the proportional hazards model and a probabilistic stochastic filtering approach. We investigate the residual life prediction capabilities of the two techniques and construct relevant replacement decision models. The research is then extended to consider multiple indicators of condition obtained simultaneously at monitoring points. We conclude with a brief investigation into the use of stochastic filtering techniques in specific scenarios involving limited computational power and variable underlying relationships between the monitored information and the residual life.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

State and parameter estimation techniques for stochastic systems

This thesis documents research undertaken on state and parameter estimationtechniques for stochastic systems in a maintenance context. Two individual problemscenarios are considered. For the first scenario, we are concerned with complexsystems and the research involves an investigation into the ability to identify andquantify the occurrence of fault injection during routine preventive maintenanceprocedures. This is achieved using an appropriate delay time modelling specificationand maximum-likelihood parameter estimation techniques. The delay time model ofthe failure process is parameterised using objective information on the failure timesand the number of faults removed from the system during preventive maintenance.We apply the proposed modelling and estimation process to simulated data sets in anattempt to recapture specified parameters and the benefits of improving maintenanceprocesses are demonstrated for the particular example. We then extend the modellingof the system in a predictive manner and combine it with a stochastic filteringapproach to establish an adaptive decision model. The decision model can be used toschedule the subsequent maintenance intervention during the course of anoperational cycle and can potentially provide an improvement on fixed intervalmaintenance policies.The second problem scenario considered is that of an individual component subjectto condition monitoring such as, vibration analysis or oil-based contamination. Theresearch involves an investigation into techniques that utilise condition informationthat we assume is related stochastically to the underlying state of the component,taken here to be the residual life. The techniques that we consider are theproportional hazards model and a probabilistic stochastic filtering approach. Weinvestigate the residual life prediction capabilities of the two techniques and construct relevant replacement decision models. The research is then extended toconsider multiple indicators of condition obtained simultaneously at monitoringpoints. We conclude with a brief investigation into the use of stochastic filteringtechniques in specific scenarios involving limited computational power and variableunderlying relationships between the monitored information and the residual life