Summary
Although the application of neural networks in quality control and maintenance is growing quickly from last years, they are just an incipient technology in the Mining Industry. At the same time, maintenance of the shift is probably the most important matter in Mining, considering that the shift could be the only way out for people and material in a colliery.
The Area of Project Engineering of the University of Oviedo has designed a system to control the state of the wire ropes for extraction in coal shifts, based on the information supplied by three groups of electromagnetic sensors (Inductive and Hall-Effect) placed in a head around the rope when inspection is carried out.
The system involves the use of three parallel neural subnetworks which output is introduced in common final layers to be definitely classified. This system allows to detect internal broken wires and to prevent more serious defects before they occur, in such a way that the rope can be maintained in service during a longer period of time, with the necessary equilibrium between security, reliability and economy. If this system is placed permanently on the shift, the risk of unexpected failure of the wire rope should be decreased to the minimum.
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Ortega, F., Ordieres, J.B., Menéndez, C., González Nicieza, C. (1995). Development of a Neural-based Diagnostic System to Control the Ropes of Mining Shifts. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_30
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DOI: https://doi.org/10.1007/978-3-7091-7535-4_30
Publisher Name: Springer, Vienna
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