Abstract: Agglomeration fault of fluidized bed reactor not only influences product quality,but also influences production seriously.In order to monitor fluidized bed reactor agglomeration fault,fault monitoring method based on low-frequency piezoelectric acoustic sensor and voiceprint extraction is proposed.Acoustic signals created by the polymer impacting on the inner bed wall of the fluidized bed reactor were monitored with pasting piezoelectric ceramic sensors on the outside wall of fluidized bed reactor,transmitting the charge with long shielded cables and audio sampling.The acoustic signals waveform in time-domain,their power spectrums and voiceprints of polymer under the conditions of normal and agglomeration fault have been analyzed and compared.The stability and distinction of voiceprints under normal and fault conditions have been especially compared.With voiceprint extraction and neural network model,agglomeration fault were monitored.The monitoring model has also been verified with voiceprints extracted from other acoustic signals sampled from the sensor mounted at different position(1 meter apart),the same diagnose results are achieved.It shows that the method proposed has high time-space domain robustness.The original signals were re-sampled with different signal extraction rates.Voiceprint extraction,training and verification of monitoring model respect to re-sampled data with different extraction rates have been implemented,results show that agglomeration fault monitoring result will not be influenced with appropriate reduced signal sampling rates.This provided a new system architecture and implementation method for fault monitoring of fluidized bed reactor agglomeration.

Key words: fluidized bed, agglomeration fault, low-frequency acoustic signal, charge transmission, voiceprint

摘要： 流化床反应器的物料结块故障不仅影响产品质量,严重的还会影响生产。为了监测流化床反应器的物料结块故障,提出了一种基于压电声波传感器和声纹特征提取的故障监测方法。在流化床外壁粘贴压电陶瓷声波传感器,采用长屏蔽电缆电荷传输和音频采样方式,监测流化床内物料撞击床壁的声波信号。分析了正常颗粒物料和物料结块情况下声波信号的时域波形、功率谱和声纹特征,重点比较了正常信号和故障信号声纹特征的稳定性和可区分度。通过提取声纹特征,运用神经网络模型实现了对物料结块故障的准确监测。用不同位置声波传感器的感测信号验证故障监测模型的结果验证了这种方法具有较高的时空域鲁棒性。用不同信号抽取率对原始信号进行了重采样,对重采样数据分别进行了声纹特征提取、监测模型的训练和检验,结果表明适当降低信号采样率不影响流化床物料结块的监测结果。为流化床物料结块故障监测问题提供了一种新的系统结构和实现方法。

关键词: 流化床, 结块故障, 低频声波, 电荷传输, 声纹特征