摘要
为了实现GB/T 4288—2018国标中将振动传感器贴在洗衣机箱体进行振动特性测试要求,对3D模拟加速度传感器拾取的洗衣机振动信号进行差分和放大从而提高信噪比,采用能量法对洗涤段振动信号进行自动分段及周期时域特征提取,分别用宽窗谱分析和窄窗谱分析进行频域特征提取,还采用谱减法消除了工频干扰。以某型波轮洗衣机为检测对象,实验测得洗涤阶段小段振动周期均值为2.7 s左右,离差较小;宽窗谱分析发现主要谱成分位于20~40、110~120、160~170和180 Hz频率段;窄窗谱分析获得的主要谱成分也在上述段内。实验结果表明方法效果显著。
The new national standard GB/T 4288 - 2018 requires that the vibration sensor be attached to the shell of washing machining for vibration characteristics test. This paper proposed a new method to solve the problem,it uses different circuit and amplifier to improve signal-to-noise ratio of the vibration signal of washing machine picked up by 3D analog acceleration sensor. Adopting energy method to segment the vibration signal automatically and extract the domain feature of the period of washing stage of impeller washing machine,using wide window spectrum analysis and narrow window spectrum analysis respectively to extract the frequency feature,and spectral subtraction method was used as well to weaken the power frequency interference. A type of impeller washing machine was tested to prove the method,the average period value of the small washing segments is about 2. 7 second with tiny deviation,and the principal components of frequency are in the same ranges of 20 ~ 40,110 ~ 120,160 ~ 170 and 180 Hz,for both wide window spectrum analysis and narrow window spectrum analysis. Experiments proved that the method has significant effect.
作者
陶金
高翠云
吴增元
常玉
陈思伟
Tao Jin;Gao Cuiyun;Wu Zengyuan;Chang Yu;Chen Siwei(Power Quality Analysis and Load Detection Technology Laboratory,Electronic and Information Engineering School,Anhui Jianzhu University,Hefei 230601,China;Washing Machine Performance Technology Department,Research and Development Center,Whirlpool China Co. ,Ltd. Hefei 230088,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2018年第12期149-156,共8页
Journal of Electronic Measurement and Instrumentation
基金
2018年度安徽省自然科学基金面上项目(1808085MF192)
2017年度高校学科(专业)拔尖人才(gxbjZD17)
2017年度省学术和技术带头人后备人选科研活动经费(2017H114)资助项目
关键词
特征提取
能量法
宽窗谱分析
窄窗谱分析
谱减法
feature extraction
energy method
wide window spectrum analysis
narrow window spectrum analysis
spectral subtraction method