Abstract
Condition-based monitoring has been developed to reduce the maintenance cost and also to enhance the effective operational life of the machine. Prognosis and health monitoring is a two-pronged approach that enables us to achieve this objective by continuous monitoring of critical parameters affecting the operability of the machine and effective prediction of residual life of the critical components. Prognosis is a key factor for effective logistic supply chain management that will ensure optimum utilization of available resources. Models based on ARIMA and Fuzzy Logic are used in this chapter to forecast the degradation data of oil pressure of diesel engine of Main Battle Tank. The ultimate aim of the chapter is to suggest the better of the two models for forecasting based on experimental study using oil pressure degradation data.
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Saraswat, G., Maurya, S., Verma, N.K. (2019). Health Monitoring of Main Battle Tank Engine Using Mamdani-Type Fuzzy Model. In: Verma, N., Ghosh, A. (eds) Computational Intelligence: Theories, Applications and Future Directions - Volume I. Advances in Intelligent Systems and Computing, vol 798. Springer, Singapore. https://doi.org/10.1007/978-981-13-1132-1_31
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DOI: https://doi.org/10.1007/978-981-13-1132-1_31
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