Refrigerated Showcase Fault Detection by an Autoencoder with Coin Betting and Maximum Correntropy Criterion

This paper proposes refrigerated showcase fault detection by an autoencoder with coin betting and Maximum Correntropy Criterion (MCC). In actual situations, showcase data may include outliers which are incorrectly stored data. Radio frequency interference or incorrect sensor setting cause the outliers. When the outliers are included in learning data, the conventional autoencoders using least square error (LSE) may be influenced by the outliers. On the other hand, even when the outliers are included in learning data, autoencoders using MCC can reduce influence from the outliers. Moreover, the conventional artificial neural networks utilize various learning algorithms such as stochastic gradient descent with momentum (SGDM), adaptive moment estimation (Adam), adaptive gradient algorithm (AdaGrad). These methods have hyperparameters related to a learning rate. Since the hyperparameters affect learning strongly, it is required to tune the hyperparameters appropriately and the tuning requires engineering costs. On the other hand, coin betting can automatically tune a learning rate appropriately while learning. Therefore, the coin betting is expected to reduce the engineering costs for parameter tuning. Practicability of the proposed method is verified by comparison with an autoencoder with SGD and LSE, an autoencoder with SGDM and MCC, an autoencoder with Adam and MCC, and an autoencoder with AdaGrad and MCC. The results are verified by the Friedman test, a post hoc test using the Wilcoxson signed-rank sum test with the Holm correction, and parameter sensitivity analysis.