  Early and accurately detecting faults in rotating machinery is crucial for
operation safety of the modern manufacturing system. In this paper, we proposed
a novel Deep fault diagnosis (DFD) method for rotating machinery with scarce
labeled samples. DFD tackles the challenging problem by transferring knowledge
from shallow models, which is based on the idea that shallow models trained
with different hand-crafted features can reveal the latent prior knowledge and
diagnostic expertise and have good generalization ability even with scarce
labeled samples. DFD can be divided into three phases. First, a spectrogram of
the raw vibration signal is calculated by applying a Short-time Fourier
transform (STFT). From those spectrograms, discriminative time-frequency domain
features can be extracted and used to form a feature pool. Then, several
candidate Support vector machine (SVM) models are trained with different
combinations of features in the feature pool with scarce labeled samples. By
evaluating the pretrained SVM models on the validation set, the most
discriminative features and best-performed SVM models can be selected, which
are used to make predictions on the unlabeled samples. The predicted labels
reserve the expert knowledge originally carried by the SVM model. They are
combined together with the scarce fine labeled samples to form an Augmented
training set (ATS). Finally, a novel 2D deep Convolutional neural network (CNN)
model is trained on the ATS to learn more discriminative features and a better
classifier. Experimental results on two fault diagnosis datasets demonstrate
the effectiveness of the proposed DFD, which achieves better performance than
SVM models and the vanilla deep CNN model trained on scarce labeled samples.
Moreover, it is computationally efficient and is promising for real-time
rotating machinery fault diagnosis.
