Condition monitoring and fault diagnosis are of significance to improve the safety and reliability of motors, given their widespread applications in virtually every branch of the industry. Sequential data modeling based on recurrent neural network (RNN) and its variants have drawn increasing attention because the temporal nature of motor signals can be well leveraged for motor analysis. One common drawback of prior research is that signals measured on motors are typically analyzed with a fixed time window, making it difficult to trade off between global state estimation and local feature extraction. This paper presents a deep learning-based model termed Multi-Resolution & multi-Sensor Fusion Network (MRSFN) for motor fault diagnosis, through multi-scale analysis of motor vibration and stator current signals. Specifically, vibration and current signals are first segmented by analysis windows of varying lengths to create a new data stream for the joint representation and temporal encoding of the original sensor signals, based on two network structures: convolutional neural network (CNN) and long short-term memory (LSTM). The advantage of the developed method is that it automatically learns the discriminative features through the network training process, without requiring manual feature selection as is typically the case in prior methods. By considering the temporal dependence of the signals being analyzed, the developed multi-resolution fusion technique not only improves the effectiveness of feature extraction but is also adaptive to varying motor speed. Two case studies demonstrate the advantages of the developed method.