摘要
掌握润滑油中磨粒的尺寸和浓度信息可以获知设备的润滑状态,从而可以采取针对性的维护措施,预防设备过早失效。本研究以自制的在线润滑油磨粒检测传感器为平台,将油液中铁磁性磨粒经过时产生的电磁扰动转换为相对应的电压信号,并设计硬件电路将此电压信号转化成真有效值信号;同时提出一种基于局部加权回归的平滑滤波算法(LOWESS)对信号进行降噪和特征提取,以在温漂、零漂、电磁干扰等噪声环境下实时获取润滑油中磨粒的尺寸和浓度信息;最后将检测结果与MetalSCAN3110磨粒传感器的结果对比,证明了该方法的可行性。
The oil debris size and density information can reflect the lubrication condition of the rotating equipments, which can help to take appropriate measures to prevent premature failure. The disturbance generated when the ferromagnetic debris passed through is converted to corresponding voltage signals based on the electromagnetic oil debris sensor and then is transformed into true RMS signal by a hardware circuit. An oil debris online identification algorithm is proposed based on a locally weighted regression scatter plot smoother（LOWESS） to noise reduction and feature extraction in order to obtain the real-time information of the size and density of the ferromagnetic particles in the temperature drift, zero drift, electromagnetic interference and other noise environments. Finally, the feasibility and effectiveness of the proposed method is proved by the contrast between the detected results of the MetalSCAN3110 sensor and our method.
出处
《失效分析与预防》
2014年第1期6-10,共5页
Failure Analysis and Prevention
基金
国家自然科学基金(60905046)
无损检测技术教育部重点实验室开放基金(ZD201329007)
关键词
润滑油磨粒监测
故障诊断
特征提取
信号处理
oil debris monitoring
fault diagnosis
feature extraction
signal processing
