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.