The present invention relates to a failure diagnosis technology, and more particularly, by using various wavelet techniques for time-dependent frequency analysis, a plurality of time/frequency images are obtained from sensor data of an object, and the plurality of time/frequency images are based on deep learning. It relates to an artificial intelligence-based failure diagnosis apparatus and method for performing failure diagnosis of an object by analyzing it with a model. To this end, the artificial intelligence-based failure diagnosis apparatus according to the present invention includes a conversion unit that receives sensing data for an object and generates at least two time/frequency maps composed of time and frequency components, and at least the at least one generated by the conversion unit. It includes a combining unit for generating a time/frequency combination map by combining two or more time-frequency maps, and a classification unit for outputting a failure diagnosis result of an object by analyzing the time/frequency combination map through a deep learning-based model.
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