Kinetic Mechanical Engineering, 2022, 3(4); doi: 10.38007/KME.2022.030406.
Jiwu Tang, Xu Liang and Ming Huang
Dalian Jiaotong University, Dalian 116028, Liaoning, China
Beijing Information Science and Technology University, Beijing 100096, China
The research of fault diagnosis and prediction technology of construction machinery can not only improve the working efficiency of the staff, but also shorten the fault time. Therefore, the use of appropriate engineering machinery fault diagnosis and prediction methods, early prediction of the fault, failure, can quickly and accurately determine the nature and location of the fault, timely troubleshooting, to avoid economic losses caused by the fault. In this paper, a fault diagnosis model based on particle swarm optimization wavelet neural network is proposed. In order to effectively enhance the optimization ability of particle swarm optimization algorithm and solve the problem of slow convergence speed of particle swarm optimization, the optimization ability of improved particle swarm optimization algorithm is greatly improved through the improvement of inertia weight factor, learning factor and position iteration formula. Experimental results show that the proposed optimization algorithm can obtain higher classification accuracy, which verifies the effectiveness of PSO-WNN in identifying fault degree.
Particle Swarm Optimization Algorithm, Wavelet Theory, Construction Machinery, Troubleshooting
Jiwu Tang, Xu Liang and Ming Huang. Method for Troubleshooting Construction Machinery Based on Particle Swarm Optimization and Wavelet Theory. Kinetic Mechanical Engineering (2022), Vol. 3, Issue 4: 46-53. https://doi.org/10.38007/KME.2022.030406.
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