一种无监督的轴承健康指标及早期故障检测方法

An Unsupervised Bearing Health Indicator and Early Fault Detection Method

提出了一种无监督的轴承健康指标及早期故障检测方法。设计了一种可以有效提取轴承状态特征的深度可分离卷积自编码器模型,以编码器的输出作为轴承状态特征表示,使用Bray-Curtis距离计算退化状态特征和健康状态特征之间的距离作为轴承状态的健康指标(BC-HI)。基于健康指标BC-HI提出了一种结合Savitzky-Golay滤波的早期故障检测方法,根据健康指标的趋势获取异常阈值,判断早期故障的发生。为验证所提方法的有效性及泛化能力,在轴承加速寿命试验数据集上进行试验,试验结果表明提出的健康指标可以反映轴承的退化趋势,并且对早期故障较为敏感,具有较强的泛化能力,与孤立森林、支持向量机等方法相比,首次故障检测时间更加提前,误报警率更低,具有一定的应用价值。

A method of unsupervised bearing health indicator and early fault detection was proposed.A deep separable convolutional auto-encoder model was designed to effectively extract bearing state features,where the outputs of the encoder were used as bearing state features.Then,the Bray-Curtis distance was used to calculate the distance between the degenerated state features and the healthy state features as the bearing state health indicator(BC-HI).An early fault detection method combined with Savitzky-Golay filter was proposed based on BC-HI.The abnormal threshold was obtained according to the trend of health indicators to judge the occurrence of early faults.In order to verify the effectiveness and generalization ability of the proposed method,the experiments were carried out on datasets of bearing accelerated life tests.The experimental results show that the health indicator proposed may reflect the degradation trend of bearings,and is sensitive to early faults,and has strong generalization ability.Compared with the methods such as isolated forest and support vector machine,the first fault detection time is earlier and the false alarm rate is lower,so it has certain application values.