Automated Maintenance Approach for Industrial Machineries by Soft Computing Techniques at Offline Monitoring Process

Fault diagnosis of industrial machineries become very much important for improving the quality of the manufacturing as well as for reducing the cost for product testing. in modern manufacturing scenario, a fast and reliable diagnosis system has turned into a challenging issue in the complex industrial environment. in this work, the diagnosis of gearbox is considered as a mean of health monitoring system by used lubricant. the proposed methodology has been performed on the basis of wear particle analysis in gearbox at offline stage. possible wear characterization has been done by image vision system to interpret into soft computing techniques like fuzzy inference and neural network mechanisms. basically, the maintenance policy has been taken with the help of fuzzy expert system, which has been described in the present work