Open AccessDigital signal processing of acoustic emission signals using power spectral density and counts statistic applied to single‐point dressing operation
Author(s) -
Nascimento Lopes Wenderson,
Isaac Ferreira Fabio,
Aparecido Alexandre Felipe,
Santos Ribeiro Danilo Marcus,
Conceição Junior Pedro de Oliveira,
Aguiar Paulo Roberto,
Bianchi Eduardo Carlos
Publication year - 2017
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2016.0317
Subject(s) - spectral density , acoustic emission , signal (programming language) , signal processing , acoustics , process (computing) , statistic , automation , grinding , computer science , digital signal processing , engineering , electronic engineering , mathematics , mechanical engineering , physics , statistics , telecommunications , programming language , operating system
Dressing is an important operation for the grinding process. Its goal is to recondition the wheel tool to re‐establish its cutting characteristics, owing to the wear produced after successive passes. Monitoring systems that use acoustic emission (AE) have been studied to correlate the signals with several tool conditions. This study brings a new approach of processing AE signals with the purpose of identifying the correct moment to stop the dressing, which is essential in an automatic control system. From the AE signals collected in dressing tests with aluminium oxide grinding wheel and single‐point dresser, spectral analysis was made through power spectral density, selecting frequencies bands that best characterise the process. The statistical parameter ‘counts’ was applied to the raw signal unfiltered and filtered in the selected bands in order to identify the tool condition and, in turn, towards a monitoring system implementation. Results showed an expressive relation between tool cutting conditions and processed signals in the selected bands. There was a great disparity of the filtered signals in the selected bands and signals unfiltered, reflecting that the filtered ones were more efficient in terms of process automation.