Maintenance integration in manufacturing systems

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). 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 is based on Artificial Neural Networks (ANNs) which emulate brain action. The NMMS would monitor the system and suggest the most appropriate maintenance actions. The main characteristics of the system are; storing maintenance history, tracking components and measuring 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 a data base system or EXCEL 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 has been implemented. Simulation and analysis of the available historical data helped to find the root of the dominant faults and find the suitable solutions to get better maintenance actions. Furthermore, it revealed the performance level of the maintenance strategy and the activity of the maintenance staff.