A Fault Diagnosis Scheme for Rotating Machinery Using Recurrence Plot and Scale Invariant Feature Transform

DOI

10.2991/icmeit-19.2019.108How to use a DOI?

Keywords

recurrence plot; scale invariant feature transform (SIFT); style; rotating machinery; fault diagnosis.

Abstract

Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accidents and guaranteeing sufficient maintenance. The traditional fault diagnosis methods usually need manually extracting the features from raw sensor data before classifying them with pattern recognition models. This paper presents a method based on image processing for fault diagnosis of rotating machinery, who can realize feature extraction automatically. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a recurrence plot utilizing recurrence quantification analysis technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, scale invariant feature transform (SIFT) is employed to automatically exact fault features from the transformed recurrence plot and finally form the feature vector. The case study results demonstrate the effectiveness of the proposed method, thus providing a highly effective means to fault diagnosis for rotating machinery.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yang Wang
AU  - Bo Zhou
AU  - Ming Cheng
AU  - Hongyong Fu
AU  - Dequan Yu
AU  - Wenbo Wu
PY  - 2019/04
DA  - 2019/04
TI  - A Fault Diagnosis Scheme for Rotating Machinery Using Recurrence Plot and Scale Invariant Feature Transform
BT  - Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)
PB  - Atlantis Press
SP  - 675
EP  - 681
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-19.2019.108
DO  - 10.2991/icmeit-19.2019.108
ID  - Wang2019/04
ER  -