A multi-scale convolution neural network for featureless fault diagnosis

Abstract

Feature extraction is an important step in conventional vibration-based fault diagnosis methods. However, the features are usually empirically extracted, leading to inconsistent performance. This paper presents a new automatic and intelligent fault diagnosis method based on convolution neural network. Firstly, the vibration signal is processed by wavelet transform into a multi-scale spectrogram image to manifest the fault characteristics. Next, the spectrogram image is directly fed into convolution neural network to learn the invariant representation for vibration signal and recognize the fault status for fault diagnosis. During model construction, rectifier neural activation function and dropout layer are incorporated into convolution neural network to improve the computational efficiency and model generalization. Training data is input into traditional convolutional neural network, ReLU network, Dropout network and enhanced convolutional neural network. The classification results are reached by inputting training data and test data. Then, comparison is made on the analytical results of the four networks to conclude that the preciseness of the classification result of the enhanced convolutional neural network achieves as high as 96%, 8% higher than traditional convolutional neural network. Through adjusting p, the holding probability of Dropout, 3 kinds of sparse neural networks are trained and the classification results are compared. It finds, when p=0.4, the enhanced convolutional neural network achieves the best classification performance, 5% and 4% higher than ReLU network and Dropout network respectively.