Skip to Main Content
The purpose of this paper is to develop an expert system for predictive maintenance of mining excavators and its various forms. These excavators are finding increasing applications in mining operations. Although, extensive data base and knowledge pool are available regarding maintenance, its methodology and concerned feed back mechanism, but no suitable or custom built expert system is yet available for specific mining machinery including excavators. Research and Development of a custom built Expert System is the need of the day because of large capital, productivity and risk involved with the mining excavators in a high capital intensive industrial scenario with acute sensitivity in the performance of such machines. This paper discusses an expert system for Failure Detection and Predictive Maintenance (FDPM) of mine excavators. The FDPM includes an expert system engine, a knowledge base, mathematical and neural network model for various fault detection and maintenance of excavators and its component and various sub-components. The FDPM system identifies, detect and locate the faults by various historical maintenance database, statistical fault analysis method, Genetic Algorithm and Artificial Neural Network. If the source of the one of the components under observation by the FDPM system, it accesses the integrity of the system components and predicts maintenance needs.