Developing Smart Systems to Predict Faults and Perform Preventive Maintenance of Industrial Machines Using MATLAB Simulations

الملخص

Effective maintenance of industrial machinery is critical for minimizing downtime, reducing operational costs, and ensuring peak performance. Traditional maintenance strategies, such as corrective maintenance (CM) and preventive maintenance (PM), often lead to inefficiencies such as excessive downtime or unnecessary repairs. Predictive maintenance (PdM), utilizing machine learning (ML) techniques, offers a solution by enabling early fault detection and predicting equipment failures, thereby reducing unplanned downtime and optimizing maintenance expenditures. This paper presents a robust framework for predictive maintenance of industrial machines that leverages MATLAB simulations for fault detection, Remaining Useful Life (RUL) prediction, and maintenance scheduling. The system incorporates machine learning models, including Random Forest (RF) for fault detection, Long Short-Term Memory (LSTM) networks for RUL prediction, and Genetic Algorithms (GA) for optimizing maintenance schedules. Results from simulations highlight the potential of the system to enhance machine reliability, minimize downtime, and reduce maintenance costs.