Abstract
In the research field of mechanical equipment fault diagnosis, usually only the existing fault types are identified, and the new emerge class of the fault is ignored, however, the new emerge fault class may also occur actually. In order to solve the problem, a novel fault diagnosis model based on deep convolution variational autoencoder network and adaptive label propagation (DCVAN-ALP) is proposed. Firstly, the initial high dimensional features are constructed by using the double tree complex wavelet packet method as the input of the model. Secondly, the convolutional neural network architecture is applied to construct the variational autoencoder, and the local and non-local characteristics of samples are embedded into the loss function for training, which is considered to improve the identification of hidden layer features of the neural network. Finally, t-SNE and the improved label propagation algorithm are adopted to process the hidden features of the neural network, which can achieve the purpose of diagnosing the existing fault class and especially new emerge fault class. Experimental results show that the proposed model can effectively extract the fault characteristics of the vibration signal, and it also has a significantly higher recognition accuracy rate than other typical deep learning methods and traditional classifiers in diagnosing new emerge fault class.
Highlights
In industrial process and military fields, mechanical equipment is widely used, once the equipment is damaged, the system reliability will be reduced, which may cause serious impact
Wang et al [13] converted time-domain vibration signal to 3-D images based on erosion operation, and AlexNet convolutional neural network was used to diagnose the faults of coal washing machine
For various deep neural networks, the neurons in deep auto-encoder (DAE) and deep belief network (DBN) models generally adopt full connection, while convolutional neural network (CNN) network adopts local connection and weight sharing, which can greatly reduce the training of network parameters and improve the efficiency [18]-[20], in addition, variational autoencoder (VAE) inherits the advantages of auto-encoder in unsupervised learning, while the characteristics of hidden layer features can better represent the input data than autoencoder [21]
Summary
In industrial process and military fields, mechanical equipment is widely used, once the equipment is damaged, the system reliability will be reduced, which may cause serious impact. For various deep neural networks, the neurons in DAE and DBN models generally adopt full connection, while CNN network adopts local connection and weight sharing, which can greatly reduce the training of network parameters and improve the efficiency [18]-[20], in addition, VAE inherits the advantages of auto-encoder in unsupervised learning, while the characteristics of hidden layer features can better represent the input data than autoencoder [21]. A novel diagnosis model is proposed based on deep convolution variational autoencoder network with adaptive label propagation algorithm. In order to solve the problem, the reparameterization technique [26] is proposed to make variables z reparameterized, let z , with N (0,1) With this trick, a differentiable estimator of the variational lower bound can be calculated and the gradient descent algorithm can be applied to train VAE
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