Abstract
In the defect diagnosis of the gear-shaft-bearing system with compound defects, the generated vibration signals are complicated. In addition, the information acquired by a single sensor is easily affected by uncertain factors, and low diagnostic accuracy is caused when traditional defect diagnosis methods are used, which cannot meet the high-precision diagnosis requirements. Therefore, a method is developed to identify the defect types and defect degrees of the gear-shaft-bearing system efficiently. In this method, the vibration signals are collected using multiple sensors, the dual-tree complex wavelet and the optimal weighting factor (OWF) methods are used for the data layer fusion, and the preprocessing is realized through wavelet transform and FFT. A learning model based on two-stream CNN composed of 1D-CNN and 2D-CNN is established, and the obtained wavelet time-frequency map and FFT spectrum are used as the input. Then, the trained features from the output of the connected layer are classified by the SVM. Compared with the OWF-1DCNN and OWF-2DCNN models, the time consumption of the OWF-TSCNN model is increased by 14.5%–26.6%, and the convergence speed of the network is decreased. However, its accuracy reaches 100% and 99.83% in the training set and test set, and the loss entropy and over-fitting rate are also greatly reduced. The feature extraction ability and generalization ability of the OWF-TSCNN model are increased, reaching 100% diagnosis accuracy on different defect types and defect degrees, which is more suitable for defect diagnosis of the gear-shaft-bearing system.
Highlights
In the defect diagnosis of the gear-shaft-bearing system with compound defects, the generated vibration signals are complicated
It was concluded that this model had good performance in defect diagnosis of gearbox due to the strong generalization ability and robustness. e vibration timedomain signal of rolling bearing was transformed into a twodimensional time-frequency image by Verstraete et al [11], which was used as the input of the improved convolutional neural network (CNN), and the defect diagnosis with a high accuracy rate was realized by adaptively extracting features and performing feature classification
Multiple sensors are used to detect the running status of the system at the same time, and 1D-CNN and 2D-CNN models are combined to establish a two-stream CNN learning model. e vibration signals generated by the system with different defect types and different defect degrees are diagnosed, and the diagnosis results are as follows: (1) e reliability of the sensor system is enhanced by using the multisensor information fusion method, which enhances the sensor system’s ability to resist interference from other unfavorable factors such as noise, and makes the collected vibration signals more reliable
Summary
If the signal with noise is directly used as the input of the learning model, a learning model with a complex structure needs to be built to resist the interference of noise factors, which decreases the accuracy of the diagnosis system and takes a longer time for diagnosis [21, 22]. Frequency aliasing will be generated when the signal is decomposed and reconstructed, and the feature extraction will be disturbed in the step [24]. For this problem, the dual-tree complex wavelet transform (DT-CWT) is introduced, which retains the advantages of the complex wavelet transform and has the advantages of translation invariance and complete reconstruction [25]. E wavelet coefficients dIRj e(n) and scale coefficients cIRJ e(n) of the real part tree are as follows:. En, the wavelet coefficients dφj (n) and scale coefficients cφJ (n) of the dual-tree complex wavelet are as follows: dφj (n) dIRj e(n) + dIIjm(n), j 1, 2, 3 .
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
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
