Abstract
In practical industrial application, the fault samples collected from rotating machinery are frequently unbalanced, which will create difficulties when it comes to diagnosis. Besides, the variation of working conditions and noise factors will further reduce the diagnosis’s accuracy and stability. Considering the above problems, we established a model based on deep Wasserstein generative adversarial network with gradient penalty (DWGANGP). In this model, the unbalanced fault data set will first be trained by the sample generation network to generate synthetic samples, which will be used to restore the balance. A one-dimensional convolutional neural network with a specific structure is then used as the fault diagnosis network to classify the reconstructed equilibrium samples. The experimental results show that the proposed sample generation network can generate high-quality synthetic samples under highly imbalanced data, and the diagnostic network has a fast training convergence. Compared to the combination methods of support vector machines, back propagation neural network and deep belief network, our method has a 74% average accuracy in all unbalanced experimental conditions, which has 64%, 69% and 87% averages leading, respectively.
Highlights
Rolling bearings are widely used in industrial machinery, of which the health state has a significant influence on the performance and service life of the mechanical equipment.Because of the complex working environment, rolling bearing is one of the most vulnerable components in machinery
Extensive research has shown that the K-means clustering, support vector machine (SVM), Bayesian network and multilayer perceptron (MLP) can be used in the field of mechanical fault diagnosis [3,4,5,6]
WGAN-GP used in conjunction with convolutional neural network (CNN) exhibits the finest generalization under load–variant and noise conditions, based on imbalance fault diagnosis
Summary
Rolling bearings are widely used in industrial machinery, of which the health state has a significant influence on the performance and service life of the mechanical equipment.Because of the complex working environment, rolling bearing is one of the most vulnerable components in machinery. Extensive research has shown that the K-means clustering, support vector machine (SVM), Bayesian network and multilayer perceptron (MLP) can be used in the field of mechanical fault diagnosis [3,4,5,6]. Such approaches, do not have sufficient ability to extract depth features of vibration signals, due to the limitation of their shallow network architectures. It is undeniable that the above scheme’s diagnostic effect is often better than that of the single machine learning algorithm It increases the complexity of the model, and the feature selection relies on manual labelling and expert knowledge, which is processing time-consuming
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