DocumentCode
616881
Title
Detecting the presence of large biomass particles in pneumatic conveying pipelines using an acoustic sensor
Author
Duo Sun ; Yong Yan ; Carter, Robert M. ; Gang Lu ; Riley, G. ; Wood, Michael
Author_Institution
Sch. of Eng. & Digital Arts, Univ. of Kent, Canterbury, UK
fYear
2013
fDate
6-9 May 2013
Firstpage
1487
Lastpage
1490
Abstract
This paper proposes a novel approach to online automatic detection of the presence of large biomass particles in a pneumatic conveying pipeline using an acoustic emission sensor and time-frequency analysis techniques. The acoustic sensor is used to capture the sound emitted from the collisions between biomass particles and pipe wall. Time-frequency analysis technique is used to eliminate environmental noise from the acoustic signal, extract the revealing information about the collisions, and identify the large particles. The acoustic sensor together with its signal conditioning unit is integrated into a compact enclosure, which can be easily attached to the outer face of a pneumatic pipeline. Experimental results obtained from an industrial pneumatic conveyor demonstrate the method works well and results are promising.
Keywords
acoustic emission; acoustic signal processing; conveyors; pipelines; pneumatic systems; renewable materials; sensors; time-frequency analysis; acoustic emission sensor; acoustic signal; environmental noise; industrial pneumatic conveyor; large biomass particles; online automatic detection; pneumatic conveying pipelines; signal conditioning unit; time-frequency analysis techniques; Acoustic sensors; Acoustics; Biomass; Noise; Pipelines; Time-frequency analysis; acoustic sensor; biomass; fuel handling; large particle detection; time-frequency analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Instrumentation and Measurement Technology Conference (I2MTC), 2013 IEEE International
Conference_Location
Minneapolis, MN
ISSN
1091-5281
Print_ISBN
978-1-4673-4621-4
Type
conf
DOI
10.1109/I2MTC.2013.6555661
Filename
6555661
Link To Document