Developing a Dynamic Risk Assessment Model for Workshop Equipment Using Predictive Maintenance and a Non Linear Safety Index in Skill Based Training Centers

Background: Vocational training workshops are essential for workforce development, yet intensive equipment use accelerates deterioration and failures. Equipment malfunctions constitute a primary source of occupational hazards and workplace accidents, posing serious threats to user health and safety.



Objective: This study develops a comprehensive model to optimize maintenance strategies, aiming to systematically enhance workshop safety and reduce lifecycle costs.



Methodology: Using System Dynamics (SD) modeling in Vensim software, key variables influencing equipment performance and safety were identified, and a causal-loop diagram was constructed. A stock-and-flow model was simulated under three maintenance scenarios: corrective, preventive, and predictive maintenance. Performance indicators included lifecycle costs, reliability, availability, and downtime as a proxy for safety risks.



Results: The results showed that the predictive maintenance strategy (based on the Internet of Things and big data analytics) is significantly more efficient compared to other methods. This strategy resulted in a reduction in failure rates to 10% in the ideal predictive scenario and 18% in the realistic scenario, and equipment availability to 98% in the predictive scenario and 95% in the realistic scenario. These improvements directly translate into reduced downtime, reduced employee exposure to unsafe equipment, and, as a result, a significant reduction in the risk of accidents and occupational hazards in the workshop environment.



Conclusion: Investment in advanced maintenance strategies is a strategic intervention for risk management, occupational safety enhancement, and sustainability of training facilities. The proposed model serves as an evidence-based decision-support tool for managers to mitigate operational hazards and improve workshop working conditions.