基于CVAE的时变工况轴承运行异常检测

Anomaly Detection Based on Conditional Variational Autoencoder for Bearing Operating Under Time Varying Conditions

数据驱动的异常检测技术被广泛应用于复杂机械设备状态监测中,工况(operating conditions,简称OCs)变化会导致监测数据的分布漂移,使传统数据驱动的异常检测方法的准确性受到极大干扰。为了解决时变工况下工况和健康状态之间的耦合问题,提出了一个新的特征解耦学习框架。首先,基于变分自动编码器(variation auto encoder,简称VAE)构建一个特征解耦条件变分自动编码器(conditional variation auto encoder,简称CVAE)网络,实现工况和健康状态的解耦;其次,对解耦后的健康状态相关特征进行降维处理,构建异常指标(anomaly indicator,简称ANI);然后,将ANI与统计异常阈值相结合,实现时变工况下轴承的异常检测;最后,通过基于时变转速退化的轴承加速疲劳退化实验,验证了该方法的有效性以及所构建的健康指标在消除时变工况干扰方面的优越性。

Data-driven anomaly detection techniques have been widely used in recent years for condition monitoring of complex mechanical equipment. For example, wind turbines and other equipment often operate under time-varying conditions due to random changes in wind speed, and the changes in conditions further lead to the drift of the distribution of monitoring data and thus the coupling of health status and condition changes. To this end, a new feature decoupling learning framework is proposed to solve the coupling problem between operating conditions and health states under time-varying operating conditions. Specifically, a feature decoupling conditional variation auto encoder(CVAE) network is constructed based on the variational self-encoder to achieve the decoupling of operating conditions(OCs)and health states. Anomaly indicators(ANI) are constructed by dimensionality reduction of the decoupled health-state-related features. Combined with statistical anomaly thresholds to achieve the anomaly detection of bearings under time-varying operating conditions. Finally, the experiments of accelerated fatigue degradation of bearings set at timevarying speed degradation verify the effectiveness of the proposed method, and further demonstrate the superiority of the constructed health indicators in eliminating the interference of time-varying working conditions.