Fuzzy logic-based fault diagnosis for mining equipment failures

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Virginia Tech

Equipment availability is the most significant factor in the productivity of many mines and processing plants. Machine breakdowns are not only expensive in terms of production losses but also important in meeting production schedules. In a complex piece of machinery like a shearer or a powered support system in a highly automated longwall face, such breakdowns can be due to one of the large number of possible faults. A large proportion, up to 80% of the down time is spent in locating the fault. For this reason, a need for an automated diagnostic method to assist the operator in the diagnosis process is felt. In this study, a diagnostic system is developed by modeling the partially known or imprecise relations and poorly defined variables found in a diagnostic environment. Logic of fuzzy sets and systems theory finds an interesting application in this area. This study presents a diagnostic algorithm, which relates the possible causes of failure to their respective symptoms through fuzzy logic paths. Applications of the diagnostic method are illustrated through examples of a compressor and a shearer.