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归一化全矢能量在随机工况下齿轮微弱故障识别中的应用
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新疆维吾尔族自治区自然科学基金资助项目(2018D01C043)


Application of normalized total vector energy method in identifying weak fault of gearbox of wind turbine under random condition
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    摘要:

    随机工况下,转速和负载的不同会改变振动信号的幅频调制特性,使得风电机组齿轮箱局部微弱故障的诊断难度急剧增大,针对于此,提出采用不同的恒定工况替代随机工况,以归一化全矢频带能量实现随机工况下齿轮箱微弱故障的诊断。方法首先将随机工况分解成不同的恒定工况的组合,降低工况的维数,针对各恒定工况,采用全矢理论将同源信号进行融合,以保证微弱信号源信息的完整性,再利用FIR滤波对全矢信号进行分解,消除因工况的不同所造成的模态混叠的影响。考虑到转频处的频带能量能定量区分不同的工况,频带能量的变化率能实现齿轮工作状态的区分,而信息熵能准确反映信号激励源和激励方式的区别,提取各频带能量熵之和、转频处的频带能量及频带能量的变化率作为区分齿轮工作状态的特征向量,消除工况变化所造成的诊断干扰的同时有利于实现各种工况下的故障模式识别,达到随机工况下齿轮微弱故障诊断的目的。最后采用高斯混合模型对风电机组齿轮箱随机工况下的150组振动信号进行特征描述,运用最大贝叶斯分类器实现故障识别,故障识别率表明该方法可有效的识别随机工况下的齿轮早期局部微弱故障。

    Abstract:

    In random working condition, the characteristic of amplitudefrequency modulation will change with different rotor speeds and load, which makes the weak fault diagnosis of Wind turbine gearbox extremely difficult. In view of this, we proposed to use different constant conditions instead of random conditions, and the normalized full vector band energy was used to diagnose the weak failure of gearbox under random conditions. In this algorithm, random conditions are decomposed into several constant conditions first to reduce its dimensions. Then, homologous signals of each constant condition are melded by full vector theory to ensure the integrity of the weak source information. In order to eliminate the modal aliasing effect caused by difference conditions, full vector signal is broken by using FIR filter. Given that band energy can quantitatively distinguish different operating mode, the rate of band energy can distinguish the working state and the information entropy reflects the difference between the motivational source and the motivation way. The sum of each band energy entropy, rotating frequency band energy and the rate of change of band energy are extracted as the distinct feature of gear working state. After the above steps, 150 groups of vibration signal from wind turbine gearboxes under random conditions were identified by Bayesian classification after GMM describe the signal character, and the accuracy rate shows that early local weak fault in random condition can be identified accurately.

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章翔峰,姜宏,冉祥锋.归一化全矢能量在随机工况下齿轮微弱故障识别中的应用[J].机床与液压,2019,47(18):77-84.
. Application of normalized total vector energy method in identifying weak fault of gearbox of wind turbine under random condition[J]. Machine Tool & Hydraulics,2019,47(18):77-84

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  • 在线发布日期: 2020-03-12
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