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首页> 外文期刊>Journal of Mechanical Science and Technology >Adaptive parameter-matching method of SR algorithm for fault diagnosis of wind turbine bearing
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Adaptive parameter-matching method of SR algorithm for fault diagnosis of wind turbine bearing

机译：风力涡轮机轴承故障诊断SR算法的自适应参数匹配方法

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摘要

The fault diagnosis of wind turbine bearings is challenging because of the heavy background noise and changes in wind speed. Stochastic resonance (SR) is an effective method of detecting fault signal from noise. However, the benefit of SR is seriously limited by the system parameters and the frequency of the input signal. A novel fault diagnosis method for wind turbine bearings, combining an adaptive SR algorithm that is based on quantum particle swarm optimization (QPSO) and frequency conversion based on frequency information exchange (FIE), is proposed. First, the frequency information of the fault characteristic signal is exchanged with the reference frequency by FIE, which can eliminate the limitation of the frequency band. Then, the SR system parameters are optimized by QPSO to avoid blind parameter selection. The signal after FIE is processed by the optimized SR system. The results of case study show that under the same input signal, the proposed method can achieve better signal-to-noise ratio and response amplitude than can the traditional double-side band modulation method and an SR method that is combined only with an optimization algorithm.
机译：由于沉重的背景噪音和风速变化，风力涡轮机轴承的故障诊断是具有挑战性的。随机共振（SR）是检测来自噪声故障信号的有效方法。然而，SR的好处受系统参数和输入信号的频率的严重限制。提出了一种用于风力涡轮机轴承的新型故障诊断方法，组合基于量子粒子群优化（QPSO）的自适应SR算法和基于频率信息交换（FIE）的频率转换。首先，通过FIE将故障特征信号的频率信息与参考频率交换，这可以消除频带的限制。然后，SR系统参数由QPSO进行优化，以避免盲目参数选择。 FIE后的信号由优化的SR系统处理。案例研究结果表明，在相同的输入信号下，所提出的方法可以实现比传统的双侧带调制方法和SR方法达到更好的信噪比和响应幅度，该方法仅用优化算法组合。

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