  Early fault diagnosis in complex mechanical systems such as gearbox has
always been a great challenge, even with the recent development in deep neural
networks. The performance of a classic fault diagnosis system predominantly
depends on the features extracted and the classifier subsequently applied.
Although a large number of attempts have been made regarding feature extraction
techniques, the methods require great human involvements are heavily depend on
domain expertise and may thus be non-representative and biased from application
to application. On the other hand, while the deep neural networks based
approaches feature adaptive feature extractions and inherent classifications,
they usually require a substantial set of training data and thus hinder their
usage for engineering applications with limited training data such as gearbox
fault diagnosis. This paper develops a deep convolutional neural network-based
transfer learning approach that not only entertains pre-processing free
adaptive feature extractions, but also requires only a small set of training
data. The proposed approach performs gear fault diagnosis using pre-processing
free raw accelerometer data and experiments with various sizes of training data
were conducted. The superiority of the proposed approach is revealed by
comparing the performance with other methods such as locally trained
convolution neural network and angle-frequency analysis based support vector
machine. The achieved accuracy indicates that the proposed approach is not only
viable and robust, but also has the potential to be readily applicable to other
fault diagnosis practices.
