An intelligent fault diagnosis framework dealing with arbitrary length inputs under different working conditions

As fault diagnosis for motor drive systems enters the era of big data, intelligent fault diagnosis methods exhibit excellent performance because of their learning capabilities. However, the existing methods are strict with the signal size, which reduces the performance of these methods for modern condition monitoring. Besides, most existing intelligent methods have a great limitation: the training data and testing data are under the same working conditions. To overcome these limitations, we propose a novel three-layer model inspired by a recurrent neural network (RNN) and transfer learning, which has the ability to process variable size sequences under different working conditions. In the first layer, the input unit is extended to ensure that there is adequate dimension to store the information of sequences. In the second layer, the main information of the whole sequence is stored, transmitted, transformed and output by gates. In the final layer, softmax is employed to classify the health conditions based on the output of the RNN with a long short-term memory cell. The classification loss based on the whole framework and the domain loss using kernel method are proposed to train the model. Furthermore, a bearing dataset is adopted to verify the effectiveness of the proposed method. The experimental results show that the proposed method can not only break the limitations of existing methods, but also achieve a superior performance compared with related methods.