基于粒子滤波的风电高速轴轴承健康预测方法

Sharaf Eddine Kramti, L. Saidi, Jaouhar Ben Ali, M. Sayadi, Eric Bechhoeferer
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引用次数: 3

摘要

风力发电机组的机械故障会导致停止发电、增加维修费用或风力发电机组的破坏等诸多问题。齿轮箱是风力发电机组的重要部件,它由轴承和齿轮组成,一般先出现轴承故障,再出现齿轮故障。基于贝叶斯方法的概率框架应用于振动轴承信号是一种适合于健康状态估计的工具。振动信号是非线性的非高斯过程,利用贝叶斯方法导出的粒子滤波是处理这类信号的一种合适的方法。将该方法应用于实际的高速轴轴承振动信号,取得了较好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Particle Filter Based Approach for Wind Turbine High-Speed Shaft Bearing Health Prognosis
Mechanical failures in wind turbine generators cause many problems of stopping electricity production, increase the price of maintenance or the destruction of the wind turbine. Gearbox is the important component in wind turbine it composed by bearing and gearings, generally failure appear in the bearing then in gearings.A probabilistic framework based on Bayesian methods applied to vibration bearing signals is a suitable tool to health state estimation. Vibration signals are nonlinear and non-Gaussian process, then the use of particle filter as derived from Bayesian approaches is a suitable method for this kind of signals.The proposed method is applied on a real high speed shaft bearing vibration signals, wish give a good results.
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