Predictive maintenance scheduling with failure rate described by truncated normal distribution

The method of risk assessment and planning of technical inspections of machines and optimization of production tasks is the main focus of this study. Any unpredicted failure resulted in the production plans no longer being valid, production processes needing to be rescheduled, costs of unused machine production capacity and losses due to the production of poor-quality products increase, as well as additional costs of human resources, equipment, and materials used during the maintenance. The method reflects the operation of the production system and the nature of the disturbances, allowing for the estimation of unknown parameters related to machine reliability. The machine failure frequency was described with the normal distribution truncated to the positive half of the axis. In production practice, this distribution is commonly used to describe the phenomenon of irregularities. The presented method was an extension of the Six Sigma concept for monitoring and continuous control in order to eliminate and prevent various inconsistencies in processes and resulting products. Reliability characteristics were used to develop predictive schedules. Schedules were assessed using the criteria of solution and quality robustness. Estimation methods of parameters describing disturbances were compared for different job shop scheduling problems. The estimation method based on a maximum likelihood approach allowed for more accurate prediction of scheduling problems. The paper presents a practical example of the application of the proposed method for electric steering gears.