【摘要】 对磨煤机振声信号进行了频谱和功率谱分析,分析表明:低频分量携带磨煤机的料位信息,而高频分量则是由高速电机旋转噪声、排风机噪声以及磨煤机筒体混响噪声引起的;低频料位信号与高频噪声信号是调制关系.利用希尔伯特变换对振声信号进行了解析化处理,分解出低频料位信息,并以振声解析信号的包络为对象,进行料位特征的提取.利用BP神经元网络,建立了磨煤机料位与振声信号的关系模型,从而实现磨煤机料位的自动识别.将模型的计算结果与实测值进行比较,结果表明,料位识别精度在±1.5%之内.更多还原

【Abstract】 Analyzes the frequency and power spectra of acoustic signal due to the vibration of coal that is pulverizing in a ball mill.The results show that the low-frequency signal contains the information on coal level in ball mill,while the high-frequency signal is caused by the noises of working high-speed motor,exhaust fan and rotating drum of ball mill.Both are in a modulating relation.The acoustic signal is analyzed by Hilbert transform,from which the low-frequency signal is picked out to show the corresponding coal level in rotating drum by an envelope of analyzed signal.The relationship model between coal level in ball mill and relevant envelope of acoustic signal due to vibration is therefore developed to recognize automatically the coal level.The comparison of calculated values from the model with measured values indicates that the recognition accuracy is within ±1.5%.更多还原