Fault Detection of Supermarket Refrigeration Systems Using Convolutional Neural Network

Abstract

The functionality of supermarket refrigeration systems (SRS) has a significant impact on the quality of food products and potentially human health. Automatic fault detection and diagnosis of SRS is desired by manufacturers and customers as performance is improved, and energy consumption and cost is lowered. In this work, Convolutional Neural Networks (CNN) are applied for fault detection and diagnosis of SRS. The network is found to be able to classify the fault with 99% accuracy. The sensitivity of the designed model to data quality is also assessed. The results show that the model can classify faults at low sample rates if the training set is large enough. Moreover, the model displays low sensitivity to data quality such as noisy and perturbed validation data, and the frequency of false positives is satisfactorily low as well.

Robustness analysis of PCA-SVM model used for fault detection in supermarket refrigeration systems

“…This information can be used for expanded development of fault detection models. A Convolutional Neural Network was applied for fault detection of refrigeration systems in [4]. This algorithm achieved more than 99 % accuracy in fault classification.…”