Acoustic emissions for particle sizing of powders through signal processing techniques
The present work introduces an innovative method for measuring particle size distribution of an airborne powder, based on the application of signal processing techniques to the acoustic emission signals produced by the impacts of the powder with specific metallic surfaces. The basic idea of the proposed methodology lies on the identification of the unknown relation between the acquired acoustic emission signals and the powder particle size distribution, by means of a multi-step procedure. In the first step, wavelet packet decomposition is used to extract useful features from the acoustic emission signals; the dimensionality of feature space is further reduced through multivariate data analysis techniques. As a final step, a neural network is properly trained to map the feature vector into the particle size distribution. The proposed solution has several advantages, such as low cost and low invasiveness which allow the system based on this technique to be easily integrated in pre-existing plants. It has been successfully applied to the PSD measurement of coal powder produced by grinding mills in a coal-fired power station, and the experimental results are reported in the paper. The measurement principle can also be applied to different particle sizing applications, whenever a solid powder is carried in air or in other gases.
acoustic emissions particle sizing multivariate data analysis neural networks system identification