MATEC Web Conf.
Volume 100, 201713th Global Congress on Manufacturing and Management (GCMM 2016)
|Number of page(s)||7|
|Section||Part 2: Internet +, Big data and Flexible manufacturing|
|Published online||08 March 2017|
Oil metal particles Detection Algorithm Based on Wavelet Transform
AVIC Beijing Changcheng Aeronautic Measurement and Control Technology Research Institute, Beijing, China
In order to observe the real-time abrasion status of the aero-engine, we need to monitor the lubrication system online. As the aero-engine operating time and running state changes, the concentration, composition, size and other parameters of the metal debris can show different changes. They can be used as an important indicator to reflect the state of the aero-engine fault. However, due to the influence of electromagnetic, vibration disturbance and random noise signal introduced by the processing unit itself, the metal particles signal tend to comprise noise. Oil metal particles detection algorithm based on wavelet transform, utilizes the optimized localized nature in time domain and frequency domain of wavelet transform and the characteristics of multi-resolution analysis, combined with the signal characteristics in actual aero-engine condition to realize noise reduction and detection, while validating the algorithm using real experimental data. The result shows that noise can be effectively decreased and signal characteristics can be detected correctly.
Key words: Sensor / metal debris / wavelet transform / variance iterative estimation / feature extraction
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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