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Deep-learning-based methods used for fault diagnosis show remarkable performance, and these methods primarily learn features based on the time or frequency domain. Generally, time- and frequency-domain features are important for identifying faults. They can reflect the type and severity of faults, especially, frequency-domain features can reveal highly distinct patterns related to fault types. Thus, learning time- and frequency-domain features helps obtain comprehensive fault information and realize high accuracy for fault diagnosis. In addition, multitask learning (MTL) can learn features from many related tasks simultaneously. Therefore, in this study, MTL is employed for learning time- and frequency-domain features from two tasks (learning features from the time-domain and the frequency-domain). A fault diagnosis method using a bitask-based time- and frequency-domain feature learning network (TF-FLN) is …