Fault detection and diagnosis in refrigeration systems using machine learning algorithms

Zahra Soltani*, Kresten Kjær Sørensen, John Leth, Jan Dimon Bendtsen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

8 Citations (Scopus)
173 Downloads (Pure)


The functionality of industrial refrigeration systems is important for environment-friendly companies and organizations, since faulty systems can impact human health by lowering food quality, cause pollution, and even lead to increased global warming. Therefore, in this industry, there is a high demand among manufacturers for early and automatic fault diagnosis. In this paper, different machine learning classifiers are tested to find the best solution for diagnosing twenty faults possibly encountered in such systems. All sensor faults and some relevant component faults are simulated in a high fidelity Matlab/Simscape model of the system, which has previously been used for controller development and verification. In this work, Convolutional Neural Networks, Support Vector Machines (SVM), Principal Components Analysis-SVM, Linear Discriminant Analysis-SVM, and Linear Discriminant Analysis classifiers are compared. The results indicate that the fault detection reliability of the algorithms highly depends on how well the training data covers the operation regime. Furthermore, it is found that a well-trained SVM can simultaneously classify twenty types of fault with 95% accuracy when the verification data is taken from different system configurations.

Original languageEnglish
JournalInternational Journal of Refrigeration
Pages (from-to)34-45
Number of pages12
Publication statusPublished - Dec 2022

Bibliographical note

Publisher Copyright:
© 2022 The Authors


  • Refrigeration
  • Fault detection
  • Machine learning
  • Dimensionality reduction
  • Sensor


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