Abstract
Considering the drawbacks of traditional wavelet neural network, such as low convergence speed and high sensitivity to initial parameters, an ant colony optimization- (ACO-) initialized wavelet neural network is proposed in this paper for vibration fault diagnosis of a hydroturbine generating unit. In this method, parameters of the wavelet neural network are initialized by the ACO algorithm, and then the wavelet neural network is trained by the gradient descent algorithm. Amplitudes of the frequency components of the hydroturbine generating unit vibration signals are used as feature vectors for wavelet neural network training to realize mapping relationship from vibration features to fault types. A real vibration fault diagnosis case result of a hydroturbine generating unit shows that the proposed method has faster convergence speed and stronger generalization ability than the traditional wavelet neural network and ACO wavelet neural network. Thus it can provide an effective solution for online vibration fault diagnosis of a hydroturbine generating unit.
Highlights
Nowadays, hydroturbine generating units are becoming larger, more complicated, and more integrated, which makes regulation and operation of the hydroturbine generating unit complicated and increases the probability of occurrence of faults
Fault diagnosis results show that, compared with the traditional wavelet neural network and Ant colony optimization (ACO) wavelet neural network, can the method proposed in this paper increase the speed of convergence but it has strong generalization ability
The results show that the ant colony optimization- (ACO-)initialized wavelet neural network has stronger generalization ability and faster convergence speed and is more suitable to diagnose the vibration faults of a hydroturbine generating unit online
Summary
Hydroturbine generating units are becoming larger, more complicated, and more integrated, which makes regulation and operation of the hydroturbine generating unit complicated and increases the probability of occurrence of faults. Vibration signals of a hydroturbine generating unit, a complicated and nonlinear system, are generally influenced by multiple hydraulic, mechanical, and electrical/electronic factors [2] These factors may interact with each other, which makes it difficult to construct by theoretical analysis a one-to-one relationship between the vibration feature and the cause of the fault. Neural network [7] has perfect self-organization, adaptive learning, and remembrance abilities It can realize complicated relationship mapping in nonlinear systems and has become a dominant method in the area of hydroturbine generating unit fault diagnosis. Fault diagnosis results show that, compared with the traditional wavelet neural network and ACO wavelet neural network, can the method proposed in this paper increase the speed of convergence but it has strong generalization ability
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
