Abstract
Aiming at the problems of poor self-adaptive ability in traditional feature extraction methods and weak generalization ability in single classifier under big data, an internal parameter-optimized Deep Belief Network (DBN) method based on grasshopper optimization algorithm (GOA) is proposed. First, the minimum Root Mean Square Error (RMSE) in the network training is taken as the fitness function, in which GOA is used to search for the optimal parameter combination of DBN. After that the learning rate and the number of batch learning in DBN which have great influence on the training error would be properly selected. At the same time, the optimal structure distribution of DBN is given through comparison. Then, FFT and linear normalization are introduced to process the original vibration signal of the gearbox, preprocess the data from multiple sensors and construct the input samples for DBN. Finally, combining with deep learning featured by powerful self-adaptive feature extraction and nonlinear mapping capabilities, the obtained samples are input into DBN for training, and the fault diagnosis model for gearbox based on DBN would be established. After several tests with the remaining samples, the diagnosis rate of the model could reach over 99.5%, which is far better than the traditional fault diagnosis method based on feature extraction and pattern recognition. The experimental results show that this method could effectively improve the self-adaptive feature extraction ability of the model as well as its accuracy of fault diagnosis, which has better generalization performance.
Highlights
As a key part of mechanical transmission system, the gearbox is widely used in wind turbine generators, coal mining, and military equipment
Sun et al [5] proposed a fault diagnosis method for the planetary gearbox based on parameter optimized VMD, determining the parameters of mode number and center frequency adaptively according to the extreme value of power spectral density
The preprocessed data is input into the network for training, and a fault diagnosis model for the gearbox based on Deep Belief Network (DBN) is constructed. rough experiments, it has been proved that the method proposed in this paper can effectively improve DBN’s self-adaptive fault feature extraction ability and identification accuracy effectively solving the shortcomings in traditional methods under big data
Summary
As a key part of mechanical transmission system, the gearbox is widely used in wind turbine generators, coal mining, and military equipment. Sun et al [5] proposed a fault diagnosis method for the planetary gearbox based on parameter optimized VMD, determining the parameters of mode number and center frequency adaptively according to the extreme value of power spectral density Such method can effectively extract fault feature frequency, making accurate diagnosis for crack faults in gears under strong background noises and subtle fault signals. Deep learning avoids the dependence on a large number of signal processing technologies and diagnostic experience, directly extracts fault features self-adaptively from signals in frequency domain, integrates feature extraction and pattern recognition methods in traditional fault diagnosis, and achieves self-adaptive extraction of fault features as well as intelligent diagnosis of health conditions under big data. The preprocessed data is input into the network for training, and a fault diagnosis model for the gearbox based on DBN is constructed. rough experiments, it has been proved that the method proposed in this paper can effectively improve DBN’s self-adaptive fault feature extraction ability and identification accuracy effectively solving the shortcomings in traditional methods under big data
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
