Fault diagnosis is vital for normal operation of the rotating motor. An effective and reliable deep learning method
known as stacked denoising autoencoder (SDAE) is investigated in this paper, which can extract the features from the pending
signals with disturbances. Deep adaptive networks are designed to extract features automatically from time domain data and
frequency domain data of motor vibration signal, respectively. Then, the network parameters of the SDAE are trained to
reconstruct the signal features, and clustering results are investigated. Finally, a classification layer is added to the top layer of the
SDAE network for the fault isolation. It is shown that, the diagnosis accuracy with input of vibratory frequency signal is higher
than that of time domain signal. The features extracted by SDAE can represent complex mapping relationships between signal
and various running status, and the accuracy is improved comparing with traditional fault diagnosis methods.