Machine Learning–Based Fault Detection and Diagnosis of Organic Rankine Cycle System for Waste-Heat Recovery
Publication: Journal of Energy Engineering
Volume 147, Issue 4
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.
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Data Availability Statement
All data and code generated or used during the study are proprietary or confidential in nature and the models may only be provided with restrictions.
Acknowledgments
The authors gratefully acknowledge the financial support by the National Natural Science Foundation of China (Grant No. 51976147).
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Published In
Journal of Energy Engineering
Volume 147 • Issue 4 • August 2021
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© 2021 American Society of Civil Engineers.
History
Received: Dec 3, 2020
Accepted: Feb 14, 2021
Published online: Apr 29, 2021
Published in print: Aug 1, 2021
Discussion open until: Sep 29, 2021
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