Affiliations: [a]
School of Reliability and Systems Engineering, State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China
| [b]
Science & Technology on Reliability and Environmental Engineering Laboratory, Beihang University, Beijing, China
| [c]
Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China
Correspondence:
[*]
Corresponding author. Yang Wang, Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China. E-mail: [email protected].
Abstract: Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accidents and guaranteeing sufficient maintenance. This paper presents a method based on image processing for fault diagnosis of rotating machinery. Different from traditional methods of signal analysis in the one-dimensional space, this study employs computing methods in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum 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, histogram of oriented gradient (HOG), is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form the feature vector. In the case study, two typical rotating machineries, gearbox and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery.
