Predictive Maintenance Optimization Based on Genetic Algorithms for Future Industrial Systems

Predictive maintenance (PdM) is a crucial technology for the industry’s future. It involves making maintenance decisions based on the prediction of the system’s performance in the future. It helps to reduce maintenance costs and ensures operational efficiency and flexibility of the future industrial systems (FIS) under different dynamic and uncertain conditions. However, applying traditional PdM optimization approaches to FIS is challenging due to the complex interactions and interconnections among the FIS components and the uncertainties of future events. This chapter presents a promising PdM optimization approach based on Genetic Algorithms (GA) for FIS that considers all the above challenges. Then, we illustrate the application of this approach for maintenance optimization of a 16-component system in both normal and dynamic situations where the system structure is changed due to reconfiguration.