摘要
采煤机实际运行过程受机械噪声和电磁噪声的影响,振动传感器获得的特征信号信噪比低,导致采煤机摇臂低频振动特征频率提取困难。为解决该问题,以实际工况下含噪信号为分析对象,基于最优小波基的去噪方法,利用频谱分析和小波包分析,获得振动信号的小波包分解层数,以噪声功率等参数为指标选定了最优小波基,实现了振动信号的高信噪比输出。通过控制变量对去噪方案的小波包分解层数和小波基函数进行评估,验证了二者选择的准确性。最后计算采煤机摇臂齿轮箱各轴特征频率,对比降噪前后振动信号的特征频谱,从去噪后信号明显提取出摇臂传动机构各级特征频率。结果证明该方法能有效克服噪声影响并提取出摇臂各轴齿轮振动特征频率。
The actual operation process of the shearer is affected by mechanical noise and electrical noise,and the collected target vibration signal is mixed with noise.Aiming at the problem that the characteristic frequency of low frequency vibration of shearer rocker arm is difficult to extract,taking the noise signal under actual working conditions as the analysis object,a denoising method based on the optimal wavelet basis is adopted,using spectrum analysis and wavelet packet analysis to determine The number of wavelet packet decomposition layers of the vibration signal is determined,and the optimal wavelet base is selected with parameters such as noise power as the index,and the effective noise reduction of the vibration signal is completed.The wavelet packet decomposition layers and wavelet basis functions of the denoising scheme are evaluated by controlling variables,and the accuracy of the two choices is verified.The characteristic frequency of each shaft of the shearer rocker gear box is calculated,and the characteristic frequency spectrum of the vibration signal before and after noise reduction is compared.The results show that the method can effectively overcome the influence of noise and extract the low-frequency vibration frequency of the rocker arm.
作者
许志鹏
刘振坚
庄德玉
XU Zhipeng;LIU Zhenjian;ZHUANG Deyu(China Coal Research Institue,Beijing 100013,China;CCTEG Shanghai Research Institute,Shanghai 200030,China)
出处
《煤炭技术》
CAS
北大核心
2023年第1期230-233,共4页
Coal Technology
基金
中国煤炭科工集团有限公司科技创新创业资金项目(21-TD-MS005)。
关键词
采煤机摇臂
振动信号
特征频率
最优小波基
shearer rocker arm
vibration signal
eigenfrequency
optimal wavelet basis
