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

Abstract

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.

A New Model for Determining Factors Affecting Human Errors in Manual Assembly Processes Using Fuzzy Delphi and DEMATEL Methods

“…Iraj et al [25] reported that factors affecting HEs in a mining process design are caused by individual factors (lack of knowledge and experience), task factors (multitasking and workload), organizational factors (poor management and training), and environmental factors (inadequate lighting, noise, and poor air quality). Noman et al [26] have studied some factors affecting inspection and maintenance errors, such as unclear instructions and procedures, stress, task complexity, and lack of experience and training, in addition to other work environmental factors such as noise, lighting, etc., Yaniel et al [27] analyzed human errors in a complex manual assembly line and identified 31 factors that caused those errors.…”