Fault detection and diagnosis in refrigeration systems using machine learning algorithms

The functionality of industrial refrigeration systems is important for environment-friendly companies and or-ganizations, 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 verifi-cation data is taken from different system configurations.

Automated summary

This study tested different machine learning classifiers to find the best solution for diagnosing twenty faults possibly encountered in industrial refrigeration systems. The results showed that the fault detection reliability of the algorithms highly depends on how well the training data covers the operation regime, and a well-trained SVM can classify twenty types of fault with 95% accuracy.