Life Cycle Cost Analysis of Pumping System through Machine Learning and Hidden Markov Model

The pumping system is a critical component in various industries and consumes 20% of the world's energy demand, with 25-50% of that energy used in industrial operations. The primary goal for users of pumping systems is to minimise maintenance costs and energy consumption. Life cycle cost (LCC) analysis is a valuable tool for achieving this goal while improving energy efficiency and minimising waste. This paper aims to compare the LCC of pumping systems in both healthy and faulty conditions at different flow rates, and to determine the best AI-based machine learning algorithm for minimising costs after fault detection. The novelty of this research is that it will evaluate the performance of different machine learning algorithms, such as the hybrid model support vector machine (SVM) and the hidden Markov model (HMM), based on prediction speed, training time, and accuracy rate. The results of the study indicate that the hybrid SVM-HMM model can predict faults in the early stages more effectively than other algorithms, leading to significant reductions in energy costs.

Automated summary

This study aims to compare the lifecycle costs (LCC) of pumping systems in different flow rates under healthy and faulty conditions, and determine the best AI-based machine learning algorithm for cost reduction after fault detection. The results indicate that the hybrid SVM-HMM model can effectively predict early-stage faults, leading to significant reductions in energy costs.