Abstract
For a single-structure deep learning fault diagnosis model, its disadvantages are an insufficient feature extraction and weak fault classification capability. This paper proposes a multi-scale deep feature fusion intelligent fault diagnosis method based on information entropy. First, a normal autoencoder, denoising autoencoder, sparse autoencoder, and contractive autoencoder are used in parallel to construct a multi-scale deep neural network feature extraction structure. A deep feature fusion strategy based on information entropy is proposed to obtain low-dimensional features and ensure the robustness of the model and the quality of deep features. Finally, the advantage of the deep belief network probability model is used as the fault classifier to identify the faults. The effectiveness of the proposed method was verified by a gearbox test-bed. Experimental results show that, compared with traditional and existing intelligent fault diagnosis methods, the proposed method can obtain representative information and features from the raw data with higher classification accuracy.
Highlights
With the development of machine learning, including artificial neural networks (ANNs), support vector machines (SVMs), random forest (RF), and other algorithms, research on intelligent fault diagnosis that combines shallow learning with a fault diagnosis has gradually emerged
Step 2: normal autoencoder (NAE), denoising autoencoder (DAE), sparse autoencoder (SAE), and contractive autoencoder (CAE) are stacked to generate deep normal autoencoder (DNAE), deep denoising autoencoder (DDAE), deep sparse autoencoder (DSAE), and deep contractive autoencoder (DCAE), respectively, and construct a multi-scale feature extraction structure and a feature fusion strategy based on information entropy
2) The inputs of DNAE, DDAE, DSAE and DCAE belong to the same dataset as the input of the proposed method, and the input of the convolutional neural networks (CNNs) is 400-dimensional sample
Summary
With the development of machine learning, including artificial neural networks (ANNs), support vector machines (SVMs), random forest (RF), and other algorithms, research on intelligent fault diagnosis that combines shallow learning with a fault diagnosis has gradually emerged. This study uses different autoencoders and combines with information entropy to design a novel feature fusion strategy, and builds a multi-scale feature extraction structure, which enhances the feature learning ability of the raw signal and improves the accuracy of fault diagnosis and stability. 3 Proposed Method The primary content of this section is divided into three parts: the construction of the feature extraction structure of a multi-scale deep neural network, the design of the feature fusion strategy based on information entropy, and the implementation process of the proposed method. We utilize NAE, DAE, SAE, and CAE to create a deep normal autoencoder (DNAE), deep denoising autoencoder (DDAE), deep sparse autoencoder (DSAE), and deep contractive autoencoder (DCAE) in a stack form These deep neural networks are combined in parallel to construct a multi-scale deep feature extraction structure to achieve feature extraction
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
