Abstract: A system for diagnosing a machine failure according to an embodiment of the inventive concept includes a pre-processing module that receives a primitive signal in a time-amplitude domain to generate a Mel spectrum image in a time-frequency domain, a feature extraction module that learns features of the Mel spectrum image to output an Euclidean distance between a latent variable and a centroid, a failure diagnosis module that performs classification into a preset number of classes on the basis of the Euclidean distance between the latent variable and the centroid, and a result output module that outputs whether a failure has occurred according to a class classified by the failure diagnosis module.

Abstract: Disclosed is a solar energy management method using deep reinforcement learning including receiving, by at least one processor, at least one or more pieces of energy information data for deep reinforcement learning of a deep neural network, calculating, by the at least one processor, a reward value for controlling a solar energy management algorithm constituting the deep neural network using the at least one or more pieces of energy information data and performing the deep reinforcement learning, and determining, by the at least one processor, a schedule for storing, discharging, consuming or selling solar energy by inputting the at least one or more pieces of energy information data as input data of the solar energy management algorithm.