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Diagnostics Expert System for Mine Hydraulic Excavators
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Maintenance problems in mining equipments are considered as ill-structured problems for which effective algorithmic results are not possible due to lack of unknown nature of failures and mine conditions.
Most of the maintenance models focus on equipment failure in terms of sudden stoppage. Majority of the maintenance optimization models are, in general, considers a fixed value of the cost of breakdown maintenance. But, the cost of breakdown maintenance not only includes down time losses and repair/replacement cost, but may also include various indirect cost. Early detection of failure modes represents the most effective way to reduce the chances of equipment failure but the existing Indian scenario in terms of machine maintenance reveals the predominance of breakdown maintenance culture in the coal mining industries in particular and industries involving heavy duty earth moving machinery in general.
Various expert systems have been used in coal mining industries to support engineering design and decision making. Its availability can be found in various mining parameters such as geological condition, mining condition, dig-ability assessment. Its availability can also be found in the area of material handling equipments to hydro electric generator. It has been used as a trouble-shooter in various industrial as well as mining applications. Besides, it has been used as an optimization tool for equipment selection in mining. Many researchers worked in the area of cost optimization in mining operation through artificial intelligence technique. Advanced fault diagnosis methods have also been used in various research works such as model-based approaches, knowledge based approaches, qualitative simulation, neural network, genetic algorithm and classical multivariate statistical techniques.
But, very few models focus on the investigation of preventive replacement or a perfect planned maintenance policy or total productive maintenance policy that restores the equipment to an as-good-as-new state.
Most of the maintenance models focus on equipment failure in terms of sudden stoppage. Majority of the maintenance optimization models are, in general, considers a fixed value of the cost of breakdown maintenance. But, the cost of breakdown maintenance not only includes down time losses and repair/replacement cost, but may also include various indirect cost. Early detection of failure modes represents the most effective way to reduce the chances of equipment failure but the existing Indian scenario in terms of machine maintenance reveals the predominance of breakdown maintenance culture in the coal mining industries in particular and industries involving heavy duty earth moving machinery in general.
Various expert systems have been used in coal mining industries to support engineering design and decision making. Its availability can be found in various mining parameters such as geological condition, mining condition, dig-ability assessment. Its availability can also be found in the area of material handling equipments to hydro electric generator. It has been used as a trouble-shooter in various industrial as well as mining applications. Besides, it has been used as an optimization tool for equipment selection in mining. Many researchers worked in the area of cost optimization in mining operation through artificial intelligence technique. Advanced fault diagnosis methods have also been used in various research works such as model-based approaches, knowledge based approaches, qualitative simulation, neural network, genetic algorithm and classical multivariate statistical techniques.
But, very few models focus on the investigation of preventive replacement or a perfect planned maintenance policy or total productive maintenance policy that restores the equipment to an as-good-as-new state.
Keywords
Expert System, Failure and Maintenance Optimization Model, Mine Excavator.
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