Comparison of genetic and binary particle swarm optimization algorithms on system maintenance scheduling using prognostics information

Recent technical advances in condition-based maintenance technology have made it possible to not only diagnose existing failures, but also forecast future failures, which is called prognostics. A common method of maintenance scheduling in condition-based maintenance is to apply thresholds to prognostics information, which is not appropriate for systems consisting of multiple serially connected machinery. Maintenance scheduling is defined as a binary optimization problem and has been solved with a genetic algorithm. In this article, various binary particle swarm optimization methods are analysed and compared with each other and a genetic algorithm on a maintenance-scheduling problem for condition-based maintenance systems using prognostics information. The trade-off between maintenance and failure is quantified as the risk to be minimized. The forecasted failure probability of serially connected machinery is utilized in the analysis of the whole system. In addition to the comparison of a genetic algorithm and binary particle swarm optimization methods, a new binary particle swarm optimization that combines the good sides of two binary particle swarm optimizations is presented.