特种车辆油气弹簧漏气故障的识别与预测

针对特种车辆实现基于状态的维修和自主保障需求,首次对特种车辆悬挂系统的核心部件油气弹簧进行了基于数据驱动方法的状态识别和故障预测研究。对油气弹簧的主要故障模式和机理进行了分析,针对发生频率最高的漏气故障,提出了一种适用于不同工况下的基于相同位移下气压变化的特征提取方法。提出了一种基于支持向量分类机和回归机的故障识别和预测框架,采用该框架能适应实车应用环境的要求,无需增加额外传感器,只利用车辆现有的测试环境就能实现油气弹簧漏气故障的实时监测和预防性维护,与其他基于振动信号的方法相比更具有实用性。在试验室内进行了模拟状态退化试验,利用采集的数据验证了方法的可行性,可用于特种车辆油气悬架的在线状态监控和预测。

Gas leakage fault recognition and prognostics of special vehicle hydro pneumatic spring

Aiming at the demands of condition based maintenance and self maintenance for special vehicle, the condition recognition and prognostics technology based on data driven method were firstly studied for the hydro pneumatic spring that is the core element of special vehicle suspension system. The main failure mode and mechanism of the hydro pneumatic spring were analyzed. Aiming at the gas leakage fault occurring most frequently, a new feature extraction method based on the gas pressure change under the same displacement suitable for different working conditions was proposed. A fault recognition and prognostics architecture based on SVM and SVR was proposed. This architecture is suitable for the real vehicle application environment, can achieve the real time monitoring and preventive maintenance of the gas leakage fault of the hydro pneumatic spring only using the existing test environment of the vehicle without adding extra transducers. Compared with other methods based on vibration signals, the proposed method has stronger practicality. State degradation simulation test was conducted in the laboratory. The data collected in the test verify the feasibility of the proposed method, which can be used in the online condition monitoring of the hydro pneumatic suspension in special vehicle.