Refrigerated Showcase Fault Detection by a Pasting based Artificial Neural Networks using Parallel Multi-population Modified Brain Storm optimization and Correntropy

This paper proposes a fault detection method for refrigerated showcase by Pasting based Artificial Neural Networks (PANNs) using Parallel Multi-population Modified Brain Storm optimization (PMP-MBSO) and Correntropy. PMP-MBSO is an evolutionary computation technique and Correntropy is one of loss functions (LFs) for ANN. At present, in Japan, the number of convenience stores is about 50,000, and refrigerated showcases placed in each store have various characteristics. Since it is difficult to adjust fault detection methods for all different refrigerated showcases, an automatic parameter adjustment method for refrigerated showcase systems is required. The proposed PANNs using PMP-MBSO and Correntropy can realize the automatic adjustment. Using actual operation data of showcases, detection accuracy of the proposed method is verified to be higher than those of the ANNs using least square error (LSE) and stochastic gradient descent (SGD), and the ANNs using Correntropy and MBSO. Effectiveness of the proposed method is verified by applying Friedman test. It is verified to speed up the proposed method as well.