A New Integrated Risk-Assessment Model for Minimizing Human-Machine Error Consequences in a Preventive Maintenance System

In this paper, a risk-assessment model for minimizing human-machine error consequences during the implementation of preventive maintenance tasks has developed. The developed model aimed to find the optimal preventive maintenance interval (PMI) and corresponding minimum risk consequences costs per unit of time. Based on expert judgment, a human error probability (HEP) model was developed using the success likelihood index methodology (SLIM). In addition, we developed a system failure probability (SFP) model based on the failure and repair data of the system. The HEP model was integrated with an SFP model to develop a human-machine error model. Then, the risk-assessment model was developed based on the consequences of system failure and human error. The effectiveness of the developed human-machine error model was shown by applying a numerical example for a multi-component series system. The optimization toolbox from MATLAB R2022b was applied to solve the developed human-machine model. The results showed that the optimal PMI can be implemented after 24 working hours to 105 hours under the acceptable limit of risk in the system which is 100 SR/h. Finally, the developed model is an effective method that can be applied to a wide range of manufacturing systems to minimize financial risks related to system inspection and maintenance while meeting safety and availability requirements.