Background:
The effective diagnosis of wind turbine gearbox fault is an important means to
ensure the normal and stable operation and avoid unexpected accidents.
Methods:
To accurately identify the fault modes of the wind turbine gearbox, an intelligent diagnosis
technology based on BP neural network trained by the Improved Quantum Particle Swarm Optimization
Algorithm (IQPSOBP) is proposed. In IQPSO approach, the random adjustment scheme of contractionexpansion
coefficient and the restarting strategy are employed, and the performance evaluation is executed
on a set of benchmark test functions. Subsequently, the fault diagnosis model of the wind turbine
gearbox is built by using IQPSO algorithm and BP neural network.
Results:
According to the evaluation results, IQPSO is superior to PSO and QPSO algorithms. Also,
compared with BP network, BP network trained by Particle Swarm Optimization (PSOBP) and BP network
trained by Quantum Particle Swarm Optimization (QPSOBP), IQPSOBP has the highest diagnostic
accuracy.
Conclusion:
The presented method provides a new reference for the fault diagnosis of wind turbine
gearbox.
Fault diagnosis of motor rolling bearing based on IMF sample entropy and particle swarm optimization SVM