Intelligent Diagnosis of Rotating Machinery Based on Optimized Adaptive Learning Dictionary and 1DCNN

Hongchao Wang,Chuang Liu,Shuangyuan Wang,Wenliao Du

doi:10.3390/app112311325

Hongchao Wang, Chuang Liu + Show 2 more

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https://doi.org/10.3390/app112311325

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Abstract

In the intelligent fault diagnosis of rotating machinery, it is difficult to extract early weak fault impact features of rotating machinery under the interference of strong background noise, which makes the accuracy of fault identification low. In order to effectively identify the early faults of rotating machinery, an intelligent fault diagnosis method of rotating machinery based on an optimized adaptive learning dictionary and one-dimensional convolution neural network (1DCNN) is proposed in this paper. First of all, based on the original signal, a redundant dictionary with impact components is constructed by K-singular value decomposition (K-SVD), and the sparse coefficients are solved by an optimized orthogonal matching pursuit (OMP) algorithm. The sparse representation of fault impact features is realized, and the reconstructed signal with a concise fault impact feature structure is obtained. Secondly, the reconstructed signal is normalized, and the experimental dataset is divided into samples. Finally, the training set is input into the 1DCNN model for model training, and the test set is input into the trained model for classification and detection to complete the intelligent fault classification diagnosis of rotating machinery. This method is applied to the fault diagnosis of bearing data of Case Western Reserve University and worm gear reducer data of Shanghai University of Technology. Compared with other methods and models, the results show that the diagnosis method proposed in this paper can achieve higher diagnosis accuracy and better generalization ability than other diagnosis models under different datasets.

Highlights

With the development of science and technology, the degree of automation and intelligence of mechanical equipment is getting higher and higher
Through the analysis of the accuracy and loss change curve, it can be seen that that when the model is trained to the fourth time, the accuracy reaches a large value, and accuracy fluctuates less in the training process, indicating that network when the model is trained tosubsequent the fourth time, theprocess, accuracy reachesthat a large value, and the the accuracy fluctuates less in the subsequent training indicating the the network model training has completed
The results show that the network model based on this method is applicable to the experimental dataset of the worm gear reducer

Summary

Introduction

With the development of science and technology, the degree of automation and intelligence of mechanical equipment is getting higher and higher. As a key component of mechanical equipment, rolling bearings are widely used in various rotating devices to support rotating objects and transfer torque and power in the transmission system [1]. In the actual operating conditions, there are usually complex factors such as non-uniformly distributed load, excessive load, and so on. Bearings are vulnerable to pitting, wear and other damage, resulting in loss of function and even system failure. Once the rolling bearing fails, it will affect the operation of machinery and equipment, reducing production efficiency, and cause huge economic losses and even casualties [2]. It is necessary to monitor the working status of bearing parts [3]

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Discussion

Conclusion