Machine Learning–Based Fault Detection and Diagnosis of Organic Rankine Cycle System for Waste-Heat Recovery

Abstract

Utilizing the organic Rankine cycle (ORC) for waste heat recovery is an important energy conversion method. Some faults may occur in the ORC in actual operation, but few studies have focused on the fault detection and diagnosis of the whole ORC system. Fault detection detects whether a fault occurs in the system and fault diagnosis diagnoses where the fault is. This paper investigated a fault detection and diagnosis scheme of the ORC system for waste heat recovery based on machine learning. First, a thermodynamic ORC model was established. Three kinds of faults (expander fault, pump fault, and heat exchanger fault) and three kinds of algorithms [logistic regression, softmax regression, and support vector machines (SVMs)] were described. The data of four major important faults (fouling fault of the evaporator and of the condenser, looseness of the mechanical moving parts in the expander, and blocking of the pump) were generated from the thermodynamic ORC model and used to train the fault detection and diagnosis schemes. To evaluate the accuracy of the fault detection and diagnosis schemes, a set of experimental data was employed to test the schemes. The accuracy scores of fault detection using logistic regression and support vector machines were 77.42% and 96.77%, respectively. The accuracy scores of fault diagnosis using softmax regression and SVM were 91.78% and 94.52%, respectively. The test times of fault diagnosis using softmax regression and SVM were 0.0099 and 0.0085 s, respectively. The results demonstrated that machine learning–based fault detection and diagnosis schemes for the ORC have high accuracy and immediacy. Therefore, the proposed schemes are promising tools for fault detection and diagnosis of the ORC system for waste heat recovery.