[21] used the impulse modeling continuous wavelet transform (IMCWT) model to diagnose bearing faults under multi-speed, where quasi-Newton optimization algorithms were applied to optimize IMCWT model so that fault information from vibration signals could be extracted more effectively. However, on one hand, the above studies need careful engineering and substantial domain expertise in selecting the useful features related to faults, and on the other hand, information of single domain, such as time-frequency domain, is mostly adopted for fault diagnosis and the time domain and frequency domain information are not considered. These have limited the further development of fault diagnosis especially with the growth of complexity of mechanical systems and computation requirements. In recent years, with the achievement in deep learning and computation ability, the data-driven methods accompanied with the 2D visualization technique [22; 23; 24] for feature selection are widely developed and show excellent performance in fault diagnosis. Deep Convolutional Neural Network is one of the most used deep learning structures that has been gaining more and more attention, particularly in the field of image processing [25], pattern analysis [26], and fault diagnosis [27; 28; 29]. For the DCNN-based fault diagnosis, data are primarily processed and transformed into 2D samples as the input of classifier, and the final features for decision-making are obtained by iterations of self-study and not dependent on manual selection. One effective method for primary data processing is to make transformations on the data in time domain, frequency domain, and time-frequency domain to obtain related features which are further used for sample construction. However, as stated above, features of single domain are adopted for fault diagnosis in many earlier studies, which only describe the specific aspect of vibration signals, and with the rich features of the raw signals it is not easy to figure out the domain(s) where features give the most contribution to the fault diagnosis. On this account, some works combine features from more than one domain for a better fault diagnosis performance. For example, [PERSON] et al. [30] carried out diagnosis on gear faults, where features are first extracted from the sensor measurements in frequency domain and time-frequency domain by corresponding tools such as FFT and WPT, and statistics are then calculated on the obtained results, after which all the features are fused in sequence as the input of diagnosis scheme where DCNN is used. [PERSON] et al. [31] extracted features in the time domain (statistical induces), frequency domain (spectral skewness and kurtosis), and time-frequency domain (wavelet energy) for reducing the dimension of raw signals. The obtained results are then used for machine health monitoring. For most of studies on DCNN-based fault diagnosis, a single network is used, which means there is only one input. This leads to the problem of features integration across different domains, in other words, a careful construction of sample is needed to contain useful information as much as possible. However, the analysis results of raw signals may have different dimensions in different domains, due to which features in different domains cannot be fused directly. For example, the transformation results of raw signal by FFT and WT are [frequency \(\times\) amplitude] and [time \(\times\) frequency \(\times\) amplitude], corresponding to frequency domain and time-frequency domain, respectively. Beside those mentioned above, in single DCNN-based fault diagnosis, plenty of work must be done with respect to structure designing and parameter tuning to obtain a satisfying performance (e.g., diagnosis accuracy). This is generally done by running multiple trials where each trial is a complete execution of training application with values for the chosen structure and hyperparameters and is time-consuming.