Development of mathematical models and methods for optimal condition based maintenance, especially within energy production and energy-intensive industry

The purpose of the project is to introduce, study, solve, and apply a generic model for the optimization of maintenance activities within power production and energy-intensive industry. The modeling framework is sufficiently general to include classic maintenance principles and related activities such as inspection and corrective, preventive, opportunistic andreliability-centered maintenance.

Start date 01/01/2011
End date The project is closed: 31/12/2014

​The development of maintenance principles and methods still has not reached a decision-modeling perspective that allows for the best alternative maintenance decisions to emerge automatically from an optimization. This project is built on previous research on the modeling and optimization of maintenance activities in the aircraft industry and in nuclear and offshore wind power production. Its purpose is to study a generic model for the optimization of maintenance activities. The model will be a mixed-integer multi-stage stochastic programming model that incorporates the uncertainty of the remaining useful lives of components, in the form of conditional distributions such as derived from condition-based monitoring and statistical analysis. The modeling framework is sufficiently general to include corrective, preventive, opportunistic and reliability-centered maintenance. A main focus lies on the construction of an efficient solution methodology; it will be based on an analysis of the polyhedral properties of the convex hull of feasible solutions and of the Markov properties of the activities. Cases will be studied particularly in the wind power industry.

Keywords: Preventive maintenance at opportunities; policy; reliability

Project leader and contact for communications
​Michael Patriksson, mipat@chalmers.se

Funded by

  • Swedish Energy Agency (Public, Sweden)
​Energy, Transport

Published: Wed 08 Oct 2014. Modified: Thu 31 May 2018