Notice of RetractionStudy on fault diagnosis of adaptive collaborative inertia weighted velocity particle swarm optimization

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

JZQ250 gear box is studied in order to make real-time monitoring and fault diagnostics for the gearbox in engineering. With dynamic maximum speed limit set in particle swarm optimization (PSO), a method of diagnosing the gearbox´s fault, i.e., the adaptive collaborative weighted velocity PSO (WVPSO) is suggested to train BP neural network. The fault diagnosis is made with the monitoring characteristic values as the gearbox´s condition monitoring values obtained by analyzing the time-domain parameters, and with fault feature vectors as the input vectors of neural network, the results of which are compared with those of the BP algorithm. The results show that the WVPSO algorithm has a faster convergence speed, and is quicker to converge to the optimal solution in the learning training of the neural network. Thus, this algorithm has higher recognition accuracy for gearbox faults, the neural network model established for fault diagnosis is somewhat universal, and the accuracy and efficiency for fault diagnosis are comparatively high.