Condition-Based Diagnostic Approach for Predicting the Maintenance Requirements of Machinery
- 1 Department of Mechanical Engineering, Rivers State University of Science and Technology, Port Harcourt, Nigeria
- 2 Department of Marine Engineering, Rivers State University of Science and Technology,Port Harcourt, Nigeria
- 3 Department of Mechanical Engineering, Rivers State University of Science and Technology, Port Harcourt, Nigeria
- 4 School of Engineering, Cranfield University, Bedfordshire, U K
- 5 School of Engineering, Cranfield University, Bedfordshire, U K
Abstract
Wise maintenance-procedures are essential for achieving high industrial productivities and low energy expenditure. A major part of the energy used in any production process is expended during the maintenance of the employed equipment. To ensure plant reliability and equipment availability, a condition-based maintenance policy has been developed in this investigation. In particular, this project explored the use of vibration parameters in the diagnosis of equipment failure. A computer-based diagnostic tool employing an artificial neural-network (ANN) was developed to analyse the ensuing machinery faults, their causes and consequences. For various categories of this type of machinery, a vibration-severity chart (ISO 12372 / BS 4675: 1971) appropriately colour coded according to defined mechanical faults, was used in training of the ANN. The model was validated using data obtained from a centrifugal pump on full load and fed into the program written in Visual Basic. The results revealed that, for centrifugal pumps within 15 to 300kw power range, vibration-velocity amplitude of between 0.9 and 2.7mm/s was within acceptable limits. When the values rose to between 2.8 and 7.0mm/s, closer monitoring and improved understanding of the equipment condition was needed. The evolved diagnostic and prognostic model is applicable for other rotary equipment that is used within the same power limits.
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