摘要
准确监测滑油液中磨损微粒的大小和分布信息是评估机械设备服役状态和预测剩余生命的重要手段。然而在实际应用中,感应式磨粒检测传感器输出信号常常伴随着各种噪声和干扰,导致微弱的磨粒信号特征难以准确辨识。为此,本文提出了一种自适应感应电压特征辨识方法。首先对检测信号进行多尺度滤波,利用多组不同截止频率滤波结果之间的稳定性进行目标信号的定位和分割。然后,根据信号的数学模型提取数值特征并进行感应电压辨识,从而实现磨损微粒的精确计数和特征分析。实验结果表明,新方法能较为完整地保留磨粒信号的形态特征,并成功提取出直径70μm磨损颗粒所产生的感应电压信号,对传感器检测精度的提高以及磨损状态准确评估提供了基础。
The accurate sensing of the size and distribution of wear debris in lubricating oil is an important method for evaluating service condition and remaining using life prediction of mechanical equipment.However,in practical application,the output of inductive debris detection sensor is often contaminated by a variety of noise and interference,which makes a challenge to identify the characteristics of debris signals.Therefore,an adaptive method for induced voltage feature identification is proposed in this article.Firstly,the detection signal is multi-filtered by low-pass filter with different cut-off frequencies.Based on the significant difference between multidimensional filtered data,the target signals are located and segmented.Finally,according to the established mathematical model,the signal numerical features are extracted to realize the identification,counting,as well as quantitative analysis of wear debris.Experimental results show that the proposed strategy successfully extract the induced voltage generated by a 70μm ferromagnetic debris with little distortion of morphological characteristics,which provides a basis for improving the detection performance of the sensor and accurately evaluating the wear state.
作者
罗久飞
郑睿
王鑫宇
陈平
冯松
Luo Jiufei;Zheng Rui;Wang Xinyu;Chen Ping;Feng Song(Advanced Manufacturing Engineering School,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;College of Mechanical and Vehicle Engineering,Chongqing University,Chongqing 400044,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2022年第8期173-181,共9页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(51705057)项目资助
关键词
磨粒检测
特征提取
稳定性分析
信号识别
debris detection
feature extraction
stability analysis
signal identification
作者简介
罗久飞,分在2009年和2012年于重庆理工大学获得学士和硕士学位,2015年于重庆大学获得博士学位,现为重庆邮电大学先进制造工程学院副教授,主要研究方向为智能故障诊断和信号处理技术。E-mail:luojf@cqupt.edu.cn;通信作者:冯松,2010年于重庆大学获得学士学位,2016年于西安交通大学获得博士学位,现为重庆邮电大学先进制造工程学院副教授,主要研究方向为油液实时监测技术。E-mail:fengsong@cqupt.edu.cn
