Refrigerated Showcase Fault Detection by a Correntropy Based Artificial Neural Network Using Modified Brain Storm Optimization

This paper proposes a fault detection (FD) method for refrigerated showcase by a Correntropy based Artificial Neural Network (CANN) using Modified Brain Storm Optimization (MBSO), which is an evolutionary computation technique. Since over 55,000 convenience stores exist in Japan, developing FD models for each showcase with various features is difficult for experts. For the purpose of solving this challenge, an automatic ANN parameter training method for a variety of showcase systems should be developed. The proposed CANN using MBSO is verified to be more accurate than the conventional least square error (LSE) based ANNs (LANNs) using stochastic gradient descent (SGD) and the conventional CANNs using fast brain storm optimization (FBSO) with actual measurement data of showcase.