Abstract
Aiming at the problem of weak signal signature recognition of gear faults, a gear fault diagnosis method based on lifting wavelet packet and combined optimization BP neural network is proposed. The initial non-sampling prediction and update operators are calculated by Lagrange interpolation subdivision based on the principle of lifting wavelet, and the adaptive redundancy lifting wavelet packet decomposition and reconstruction algorithm is constructed. The network parameters of the number of hidden layers and the quantity of nodes, initial weights and thresholds of BP neural network are optimized by genetic algorithm (GA). The Levenberg-Marquardt (LM) algorithm is used to improve the search space of the network. Through experimental analysis, the results show that the gear fault diagnosis method proposed in this paper not only has high diagnostic accuracy, but also increase efficiency.
Highlights
The gear is the most important connection and transmission component in the mechanical equipment
In order to overcome the problems of slow convergence and falling into local minimum of traditional BP-NN, Wu [1] uses Genetic Algorithm (GA) to optimize the initial weight of BP-NN, and uses GA global search ability to effectively avoid BP- NN is caught in a local minimum problem; Zhang [2] proposed a fault diagnosis of the fan gearbox based on genetic algorithm optimization BP neural network, which effectively diagnoses the gearbox fault diagnosis
Because GA only optimizes the initial weight of BP-NN to speed up the determination of the search space, the basic BP algorithm is still used in the local optimization process in the search space
Summary
The gear is the most important connection and transmission component in the mechanical equipment. If the fault of the gear in the transmission process is found in time, the maintenance and repair time of the equipment can be arranged economically and reasonably to avoid accidents. In order to overcome the problems of slow convergence and falling into local minimum of traditional BP-NN, Wu [1] uses Genetic Algorithm (GA) to optimize the initial weight of BP-NN, and uses GA global search ability to effectively avoid BP- NN is caught in a local minimum problem; Zhang [2] proposed a fault diagnosis of the fan gearbox based on genetic algorithm optimization BP neural network, which effectively diagnoses the gearbox fault diagnosis. Because GA only optimizes the initial weight of BP-NN to speed up the determination of the search space, the basic BP algorithm is still used in the local optimization process in the search space. It still fails to change the slow convergence of BP-NN
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
