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基于AR能量比和SVDD的滚动轴承性能退化评估
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国家自然科学基金项目(51865010;51665013)


Evaluation of rolling bearing performance degradation using autoregressive model energy ratio and support vector data description
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    摘要:

    滚动轴承在长期工作过程中存在不同程度的性能退化。如果能够识别滚动轴承的退化状态,就可以采取维修措施。针对滚动轴承性能退化评估问题,提出了一种基于振动信号自回归模型(Autoregressive model,AR)能量比和支持向量数据描述(Support vector data description,SVDD)相结合的滚动轴承性能退化评估方法。首先用AR滤波得到轴承全寿命周期内振动信号的剩余分量,计算能量比作为轴承状态的特征向量;然后利用轴承正常状态下的特征向量对SVDD进行训练,得到正常状态下的超球面。轴承全寿命周期样本特征向量与超球面之间的相对距离作为轴承性能退化定量评估指标;最后设定早期故障报警阈值。结果表明,与常见的监测指标的性能退化评估方法相比,该方法的早期故障检测能力强,对轴承各阶段性能退化状态描述更为准确。

    Abstract:

    Rolling bearings have different degrees of degradation in performance during longterm work. If the degraded state of the rolling bearing can be identified, maintenance measures can be taken. Aiming at the performance degradation evaluation of rolling bearings, a method for evaluating the degradation of rolling bearing performance is proposed, which combines the vibration signal autoregressive model (AR) energy ratio and the support vector data description (SVDD). Firstly, the residual components of the vibration signal in the whole life cycle of the bearing are obtained by AR filtering, and the energy ratio is calculated as the feature vector of the bearing state. Then, SVDD is trained by using the feature vector of the bearing under normal state, and the hypersphere under normal state is obtained. The relative distance between the feature vector of bearing life cycle sample and the hypersphere is used as the quantitative evaluation index of bearing performance degradation. Finally, the early fault alarm threshold is set to determine the early fault point. The results show that compared with the performance degradation assessment methods of common monitoring indicators, the early fault detection capability of the proposed method is stronger, and the description of each stage of bearing performance degradation is more accurate.

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王发令,周建民,张臣臣,尹文豪,张龙.基于AR能量比和SVDD的滚动轴承性能退化评估[J].机床与液压,2020,48(12):101-111.
. Evaluation of rolling bearing performance degradation using autoregressive model energy ratio and support vector data description[J]. Machine Tool & Hydraulics,2020,48(12):101-111

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  • 在线发布日期: 2020-08-21
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