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Application of PSO Algorithm to Gearbox Fault Diagnosis

机译:PSO算法在齿轮箱故障诊断中的应用

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摘要

After an introduction of the principle of particle swarm optimization (PSO) algorithm based on swarm intelligence, and of the modified version of the PSO algorithm, a neural network system for gearbox fault diagnosis has been established. After being trained by PSO algorithm, the neural network system is applied to fault diagnosis. Comparison of the diagnostic results between the PSO-based algorithm and the BP-based algorithm indicates that the network system based on PSO algorithm is of better training performance, and of less overall output error compared with those of the BP algorithm. It has been proved that the neural network system for fault diagnosis based on PSO algorithm is of higher probability of identifying multi-fault symptoms,and of rapid convergence and higher diagnosis accuracy, and that the neural network system for fault diagnosis expects a wide application in the field of mechanic fault diagnosis because of its higher searching efficiency.
机译:在介绍了基于群智能的粒子群优化(PSO)算法的原理以及PSO算法的改进版本之后,建立了用于变速箱故障诊断的神经网络系统。经过PSO算法训练后,将神经网络系统应用于故障诊断。对基于PSO算法和基于BP算法的诊断结果进行比较表明,与PBP算法相比,基于PSO算法的网络系统具有更好的训练性能,总体输出误差较小。实践证明,基于PSO算法的故障诊断神经网络系统具有较高的识别多故障症状的可能性,具有快速收敛性和较高的诊断精度,并且故障诊断神经网络系统有望在神经网络中得到广泛的应用。机械故障诊断领域,因为其较高的搜索效率。

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