Study Of Pressure Fluctuations On The Cold Test Fluidized Bed

Currently the boiler pressure measuring points of the circulating fluidized bed are all installed in the furnace walls. Extensive recycling of materials, and frequent frication and clash of measuring points make the plug wear problem at the measuring points very serious, resulting in inaccurate measurements or damage of the measuring points. To solve the problem, in this thesis I arrange pressure measuring points on the walls at the entrance of the wind chamber hood when setting up cold bubbling fluidized bed test rig, then the collected pressure signals are transformed into digital signals with pressure signal acquisition system,and the digital signals are stored in the computer. During the test, I collect pressure fluctuation signals in normal operating conditions and disturbance conditions by adjusting and changing the test's control conditions and carry out in-depth analysis and research of the signals to probe pressure fluctuation conditions in fluidized bed.Based on Matlab programming and using autocorrelation function of the Fourier transform method, the time domain signals are transformed into frequency domain signals. From the received signal spectrum we can clearly see that the pressure fluctuation at the hood inlet is closely related to some parameters such as static bed height of fluidized bed and superficial gas velocity, in addition pressure wave energy is mainly manifested by low-frequency pressure fluctuations, which show that monitoring signals collected at hood entrance can reflect the status of pressure fluctuation in fluidized bed. Then I denoise the signals,by comparative analysis of project filter method and wavelet denoising method,wavelet denoising method is proved better than project filter method. Further by selective comparison of different wavelet bases for denoising, it is concluded that db2 wavelet three-layer decomposition denosing method can be used to achieve the best denosing effects.During the test, I stimulate some disturbance conditions such as failure of monitoring pressure signals in fluidized bed scene, disturbance generated by placing materials and hood blocking. By using time domain analysis, frequency domain analysis and wavelet packet analysis, I reach different results in normal conditions and disturbance conditions and establish the fault signals'diagnosis and recognition method. I use time domain analysis, by using method of setting confidence interval, to determine whether pressure signals go wrong. I use frequency domain analysis, by comparing different spectrums in normal conditions and placing materials disturbance conditions, to serve spectrum as feature vector and achieve fault diagnosis recognition. By using wavelet packet analysis, carrying out three-layer decomposition of the pressure signals and reconstructing them, and comparing different node powers at each node in normal and disturbance conditions, it is concluded that the signal power spectrum at p30 can serve as feature vector to determine whether hood block arise in circulating fluidized bed, achieve fault diagnosis and provide a theoretical reference and basis for field application.