A Fault Diagnosis Scheme for Rotating Machinery Using Recurrence Plot and Scale Invariant Feature Transform
Yang Wang, Bo Zhou, Ming Cheng, Hongyong Fu, Dequan Yu, Wenbo Wu
Available Online April 2019.
- https://doi.org/10.2991/icmeit-19.2019.108How to use a DOI?
- recurrence plot; scale invariant feature transform (SIFT); style; rotating machinery; fault diagnosis.
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 PB - Atlantis Press SP - 675 EP - 681 SN - 2352-538X UR - https://doi.org/10.2991/icmeit-19.2019.108 DO - https://doi.org/10.2991/icmeit-19.2019.108 ID - Wang2019/04 ER -