Refrigerated Showcase Fault Detection by an Autoencoder with the Adaptive Kernel Size Tuning using Maximum Correntropy Criterion

Abstract

This paper proposes refrigerated showcase fault detection by an autoencoder with the adaptive kernel size tuning (AKST) using Maximum Correntropy Criterion (MCC) and Stochastic Gradient Descent with Momentum. Refrigerated showcase data may include outliers that store values differing from actual values because of various reasons such as incorrect sensor settings and radio frequency interference. The conventional fault detection methods by an autoencoder using Least Square Error (LSE) as a loss function are affected by the outliers when the outliers are included in learning data. On the contrary, the conventional fault detection methods by an Artificial Neural Network (ANN) using MCC as a loss function can ignore influence of the outliers. Moreover, faults rarely occur in refrigerated showcases. The methods by an ANN using MCC require fault data for learning. A fault detection method by an autoencoder using the MCC that is a combination of the above two methods can ignore influence of the outliers and doesn't require fault data. However, there is a parameter named kernel size in the MCC. It is required to tune the parameter properly and the parameter tuning requires engineering. The proposed AKST method reduces the engineering of the kernel size tuning. Effectiveness of the proposed method is confirmed by comparison with comparative methods by an autoencoder using LSE and Stochastic Gradient Descent (SGD), an autoencoder with fixed kernel size using the MCC and SGD, an autoencoder with the AKST using the MCC and Adam, and an autoencoder with the AKST using the MCC and AdaGrad.