ONLINE CONDITION MONITORING OF ROTATING EQUIPMENT USING NEURAL NETWORKS

This paper reports on research Aquila Mining Systems Ltd. conducted by J.H. Burrows Electronics Inc. in relation to the vibration condition monitoring of rotating equipment used in the mining and petrochemical industries. Using historical and real time vibration data monitored fr om compressors, pumps and electric motors, various approaches were des igned and evaluated to extract and identify useful patterns and trends in the vibration signals of the rotating components of these machines . Efforts were focused on establishing whether the observed trends cou ld be classified into distinct categories which would be indicative of the mechanical state of the equipment. Subsequent work will examine t he feasibility of on-line prediction of component wear that could lead to preventive maintenance in advance of complete and catastrophic fai lure. Towards this end, data experimentation with neural networks will be undertaken to examine their applicability in accurately and reliab ly predicting the mechanical state of a machine and its various compon ents.