Int. J. Reliability and Safety, Vol. 11, Nos. 1/2, 2017 23 Copyright © 2017 Inderscience Enterprises Ltd. Modelling composite performance variable of deteriorating systems using empirical evidence and artificial neural network P.A. Ozor* Department of Mechanical Engineering, Faculty of Engineering, University of Nigeria Nsukka, Enugu State, Nigeria and Department of Quality and Operations Management, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa Email: pozor@uj.ac.za Email: paul.ozor@unn.edu.ng *Corresponding author S.O. Onyegegbu Department of Mechanical Engineering, Faculty of Engineering, University of Nigeria Nsukka, Enugu State, Nigeria Email: samuel_onyegegbu@yahoo.co.uk J.C. Agunwamba Department of Civil Engineering, Faculty of Engineering, University of Nigeria Nsukka, Enugu State, Nigeria Email: jonah.agunwamba@unn.edu.ng Abstract: The use of operational and environmental conditions combined with Artificial Neural Networks (ANNs) to model the composite performance of deteriorating repairable systems is presented. The proposed variable is obtained by combination of reliability, availability, maintainability and profitability (RAMP). Probability distributions and empirical evidence observed on an example system, namely centrifugal pumps at the gas plant of an energy company, were relied upon to model the operation process. The results show that the input variables, preventive maintenance, spare parts availability, efficiency of operating personnel and efficiency of maintenance personnel, with cumulative performance enhancement of 56.1%, 39.97%, 30.8% and 30.6%, respectively, improve RAMP appreciably. The results also show that proper assessment and control of the input variables, administrative delays,