||The management of maintenance activities extremely affects the useful life of the equipments, product quality, direct costs of maintenance and consequently production costs. Thus, a reliable maintenance system is critical to keep acceptable level of profit and competition. This work presents a Neural Management maintenance System (NMMS) considering
safety and environmental issues. It combines methods applied at present to have a benefit of the vast literature in maintenance of manufacturing systems. It integrates Corrective Maintenance (CM), adaptive Preventive Maintenance (PM) and Condition Based Maintenance (CBM) with suitable maintenance strategy addressed for each component/subsystem.
The NMMS would monitor the system and suggest the most appropriate
maintenance actions. The main characteristics of the system includes;
integration of expert opinion in a knowledge base, storing maintenance history and tracking components, alarming predetermined maintenance activities, alerting for spare parts and materials, updating schedules, considering limitation of resources, and measure the effectiveness of the maintenance system. The scheme has been designed and simulated. The easiness and intelligence of the proposed NMMS depends on keeping the maintenance data in EXEL spreadsheets and linking it to MATLAB which in turn update the models and makes the decisions. A case study application in a fluorescent lamps factory is implemented.Simulation and analysis of the available historical data should help the management to find the root of the dominant faults and find the suitable solutions to optimize the maintenance actions. Furthermore, it revealed the performance level of the maintenance strategy and the activity of the maintenance staff.