Abstract
Aiming at the problem of poor robustness of the intelligent diagnostic model, a fault diagnosis model of rolling bearing based on a multi-dimension input convolutional neural network (MDI-CNN) is proposed. Compared with the traditional convolution neural network, the proposed model has multiple input layers. Therefore, it can fuse the original signal and processed signal—making full use of advantages of the convolutional neural networks to learn the original signal characteristics automatically, and also improving recognition accuracy and anti-jamming ability. The feasibility and validity of the proposed MDI-CNN are verified, and its advantages are proved by comparison with the other related models. Moreover, the robustness of the model is tested by adding the noise to the test set. Finally, the stability of the model is verified by two experiments. The experimental results show that the proposed model improves the recognition rate, robustness and convergence performance of the traditional convolution model and has good generalization ability.
Highlights
Rotating machinery is the most critical component in mechanical equipment, and it is widely used in industrial production equipment [1]
The multi-dimension input convolutional neural network (MDI-convolutional Neural Network (CNN)) was implemented by using MATLAB 2018a programming program, and it was run on a personal computer with a 2.6GHz CPU, inter-core i5-3230m and 8GB RAM
The results show that the fault recognition rate of the MDI-CNN model reached 99.96% after training, which proved that the proposed model was feasible and effective, and had high accuracy
Summary
Rotating machinery is the most critical component in mechanical equipment, and it is widely used in industrial production equipment [1]. Since rolling bearings are one of the most common structures in rotating machinery, the slight defects of rolling bearings may lead to the failure of the whole system and cause severe financial losses [2]. It is of great significance to study the fault diagnosis of rolling bearings [3,4,5,6,7]. In view of bearing fault diagnosis, after a long period of exploration and research, many scholars have proposed various model-based methods [8]. Feature extraction and recognition [9] are the two most important processes in a diagnostic model. The extracted features are entered into classification algorithms, such as support vector machines (SVM) [17] and artificial neural networks (ANN) [18]
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