processing methods and the fault classification using various machine learning techniques. Although advanced signal processing methods (fast Fourier transform (FFT), spectrum kurtosis (SK), wavelet transform (WT), sparse representation, etc.) and machine learning algorithms (k-nearest neighbor (KNN), artificial neural network (ANN), support vector machine (SVM), etc.) have been successfully applied to intelligent diagnosis and have made considerable progress, it remains a challenging problem about how to perform diagnosis precisely and efficiently. With the development of online condition monitoring and data analysis systems, increasingly different kinds of real-time data are transferred from operating machines and the massive data are gained in the cloud. Facing with these heterogeneous massive data, feature extraction methods and mapping abilities from signals to conditions that are designed and chosen by experts, to a great extent depending on prior knowledge, are time-consuming and empirical.

Deep learning (DL) as a booming data mining technique has swept many fields including computer vision (CV) [@krizhevsky2012imagenet; @farabet2012learning], natural language processing (NLP) [@hirschberg2015advances; @sun2017review; @young2018recent], etc. In 2006, the concept of DL was first introduced through proposing the deep belief network (DBN)[@hinton2006reducing]. In 2013, MIT Technology Review ranked the DL technology as the top ten breakthrough technologies [@MIT2013]. In 2015, a review [@lecun2015deep] 