Blade fault diagnosis using empirical mode decomposition based feature extraction method

Blade fault diagnosis using empirical mode decomposition based feature extraction method

Blade fault diagnosis had become more significant and impactful for rotating machinery operators in the industry. Many works had been carried out using different signal processing techniques and artificial intelligence approaches for blade fault diagnosis. Frequency and wavelet based features are us...

Full description

Bibliographic Details
Main Authors: Tan, C. Y., Ngui, Wai Keng, Leong, Mohd Salman, Lim, M. H.
Format: Conference or Workshop Item
Language:English
English
Published: EDP Sciences 2019
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://umpir.ump.edu.my/id/eprint/24279/
http://umpir.ump.edu.my/id/eprint/24279/
http://umpir.ump.edu.my/id/eprint/24279/1/Blade%20fault%20diagnosis%20using%20empirical%20mode%20decomposition.pdf
http://umpir.ump.edu.my/id/eprint/24279/7/106.1%20Blade%20fault%20diagnosis%20using%20empirical%20mode%20decomposition.pdf
id ump-24279
recordtype eprints
spelling ump-242792019-06-10T07:26:49Z http://umpir.ump.edu.my/id/eprint/24279/ Blade fault diagnosis using empirical mode decomposition based feature extraction method Tan, C. Y. Ngui, Wai Keng Leong, Mohd Salman Lim, M. H. TJ Mechanical engineering and machinery Blade fault diagnosis had become more significant and impactful for rotating machinery operators in the industry. Many works had been carried out using different signal processing techniques and artificial intelligence approaches for blade fault diagnosis. Frequency and wavelet based features are usually used as the input to the artificial neural network for blade fault diagnosis. However, the application of others time-frequency based feature extraction technique and artificial intelligence approach for blade fault diagnosis is still lacking. In this study, a novel blade fault diagnosis method based on ensemble empirical mode decomposition and extreme learning machine was developed. Bandpass filtering was applied to the raw vibration signals and integrated with the high pass filter to obtain the velocity signal. Synchronous time averaging was then applied to the velocity signals. Three ensemble empirical mode decomposition based feature extraction methods were proposed: direct statistical parameters extraction, intrinsic mode functions averaging statistical parameters extraction and features averaging statistical parameters extraction. The effectiveness of different feature vector sets for blade fault diagnosis was examined. Feature vector set of intrinsic mode functions averaging statistical parameters extraction was found to be more effective for blade fault diagnosis. With the novel proposed method, blade fault diagnosis could be more accurate and precise. EDP Sciences 2019-01 Conference or Workshop Item PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/24279/1/Blade%20fault%20diagnosis%20using%20empirical%20mode%20decomposition.pdf pdf en http://umpir.ump.edu.my/id/eprint/24279/7/106.1%20Blade%20fault%20diagnosis%20using%20empirical%20mode%20decomposition.pdf Tan, C. Y. and Ngui, Wai Keng and Leong, Mohd Salman and Lim, M. H. (2019) Blade fault diagnosis using empirical mode decomposition based feature extraction method. In: Engineering Applications Of Artificial Intelligence Conference (EAAIC 2018), 3 - 5 Disember 2018 , Sabah, Malaysia. p. 1., 255. ISSN 2261-236X https://doi.org/10.1051/matecconf/201925506009
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
English
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Tan, C. Y.
Ngui, Wai Keng
Leong, Mohd Salman
Lim, M. H.
Blade fault diagnosis using empirical mode decomposition based feature extraction method
description Blade fault diagnosis had become more significant and impactful for rotating machinery operators in the industry. Many works had been carried out using different signal processing techniques and artificial intelligence approaches for blade fault diagnosis. Frequency and wavelet based features are usually used as the input to the artificial neural network for blade fault diagnosis. However, the application of others time-frequency based feature extraction technique and artificial intelligence approach for blade fault diagnosis is still lacking. In this study, a novel blade fault diagnosis method based on ensemble empirical mode decomposition and extreme learning machine was developed. Bandpass filtering was applied to the raw vibration signals and integrated with the high pass filter to obtain the velocity signal. Synchronous time averaging was then applied to the velocity signals. Three ensemble empirical mode decomposition based feature extraction methods were proposed: direct statistical parameters extraction, intrinsic mode functions averaging statistical parameters extraction and features averaging statistical parameters extraction. The effectiveness of different feature vector sets for blade fault diagnosis was examined. Feature vector set of intrinsic mode functions averaging statistical parameters extraction was found to be more effective for blade fault diagnosis. With the novel proposed method, blade fault diagnosis could be more accurate and precise.
format Conference or Workshop Item
author Tan, C. Y.
Ngui, Wai Keng
Leong, Mohd Salman
Lim, M. H.
author_facet Tan, C. Y.
Ngui, Wai Keng
Leong, Mohd Salman
Lim, M. H.
author_sort Tan, C. Y.
title Blade fault diagnosis using empirical mode decomposition based feature extraction method
title_short Blade fault diagnosis using empirical mode decomposition based feature extraction method
title_full Blade fault diagnosis using empirical mode decomposition based feature extraction method
title_fullStr Blade fault diagnosis using empirical mode decomposition based feature extraction method
title_full_unstemmed Blade fault diagnosis using empirical mode decomposition based feature extraction method
title_sort blade fault diagnosis using empirical mode decomposition based feature extraction method
publisher EDP Sciences
publishDate 2019
url http://umpir.ump.edu.my/id/eprint/24279/
http://umpir.ump.edu.my/id/eprint/24279/
http://umpir.ump.edu.my/id/eprint/24279/1/Blade%20fault%20diagnosis%20using%20empirical%20mode%20decomposition.pdf
http://umpir.ump.edu.my/id/eprint/24279/7/106.1%20Blade%20fault%20diagnosis%20using%20empirical%20mode%20decomposition.pdf
first_indexed 2023-09-18T22:36:38Z
last_indexed 2023-09-18T22:36:38Z
_version_ 1777416639386484736

Similar Items