Research of Particle Size Identification Based on Blind Source Separation

Research of Particle Size Identification Based on Blind Source Separation

Juan Wu Zhen Zhou Xu Yang Junlei Hu 

College of Measure-Control Technology and Communication Engineering, Harbin University of Science and Technology, Harbin 150040, China

Shanghai Institute of Measurement and Testing Technology, Shanghai, China

Page: 
32-43
|
DOI: 
https://doi.org/10.18280/mmc_a.900103
Received: 
15 March 2017
|
Accepted: 
15 April 2017
|
Published: 
31 March 2017
| Citation

OPEN ACCESS

Abstract: 

Aiming at effectively identifying the different particle sizes of single particles of the same kind and eliminating the interference of redundant and noise components in the light scattering signals, this paper puts forward a single particle size detection method by processing the light scattering signals of particles with blind source separation. As shown in the test results, the research object, a single-spherical PTFE pellet (4μm in particle size), contains noise and redundant angular intensity scattering signals. The scattering signals are decomposed into by empirical mode 8 components, which respectively serve as the virtual multi-source signals for blind source separation. The redundant noises in different channels are removed by the ICA algorithm to produce the noiseless and non-redundant light scattering signals. Such signals bear the information on the type of particles, and provide a reliable signal source for further, accurate measurement of particle size. It is demonstrated in the simulation experiment and the engineering example that the blind source separation method not only applies to identification of signal source, but also reduces the noise, improves the signal-to-noise ratio (SNR) of the signal, and suppresses the interference in the light scattering signals from redundant and nose components.

Keywords: 

light scattering, blind source separation, particle size

1. Introduction
2. Light Scattering Signal Processing Based on Blind Source Separation
3. Light Scattering Signal Simulation Based on Blind Source Separation
4. Experimental Verification
5. Conclusion
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