Maintenance Modeling and Control of Multi-unit Systems Subject to Condition Monitoring

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The proposed research will focus on the development of maintenance models and optimal maintenance policies for multi-unit systems with the special interest in widely appearing structures of real systems such as series systems and the systems consisting of N independently operating units. Examples of series systems include complex equipment and machinery. Examples of N-unit systems include energy systems such as wind or solar panel farms at different locations, oil drilling platforms in a particular geographical area, production systems consisting of several machines or production lines, and fleets of vehicles such as trucks, buses, trains or airplane fleets. In many situations the units themselves can be considered as series systems. Previous research has focused mainly on the development of maintenance models and optimal policies for single unit systems. The published research results for two-unit and multi-unit systems are very limited. Due to the high maintenance setup cost for systems considered in the proposed research which may include also the travel cost and the system downtime cost, there is an economic inter-dependence among the units, and the newly developed maintenance policies which will utilize opportunistic maintenance should bring substantial benefits. Other challenges in this research include the additional constraints associated with these systems such as energy or production demand satisfaction, scheduling of planned, periodic maintenance combined with the developed CBM policies for groups of the units to meet the transportation requirements, selection of the proper size of the fleets and the number of vehicles with different propulsion systems, and the subsequent replacement decisions. The proposed research provides several excellent challenging topics for Ph.D. and M.A.Sc. students and top journal publications are expected. The theories and methodologies which will be applied include CBM modeling and control using hidden Markov and semi-Markov modeling, Bayesian dynamic programming, semi-Markov decision process theory, optimal stopping, renewal and Markov renewal theory, theory of regenerative and semi-regenerative processes, proportional hazards modeling, EM algorithm and its extensions, phase-type modeling, group and opportunistic maintenance modeling, recently developed approximate stochastic dynamic programming methods and index policies, vector time series modeling and data dimensionality reduction methods, vibration data de-noising methods, and simulation. The results will be published in the front line journals and presented at major international conferences, they will contribute significantly to the further development of the discipline, will bring high economic benefits, and will be highly applicable and desirable in many industries.