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Article

Motion-Blurred Particle Image Restoration for On-Line Wear Monitoring

1
Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an 710049, China
2
School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, NSW 2052, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Sensors 2015, 15(4), 8173-8191; https://doi.org/10.3390/s150408173
Received: 23 February 2015 / Revised: 27 March 2015 / Accepted: 1 April 2015 / Published: 8 April 2015
(This article belongs to the Section Physical Sensors)
On-line images of wear debris contain important information for real-time condition monitoring, and a dynamic imaging technique can eliminate particle overlaps commonly found in static images, for instance, acquired using ferrography. However, dynamic wear debris images captured in a running machine are unavoidably blurred because the particles in lubricant are in motion. Hence, it is difficult to acquire reliable images of wear debris with an adequate resolution for particle feature extraction. In order to obtain sharp wear particle images, an image processing approach is proposed. Blurred particles were firstly separated from the static background by utilizing a background subtraction method. Second, the point spread function was estimated using power cepstrum to determine the blur direction and length. Then, the Wiener filter algorithm was adopted to perform image restoration to improve the image quality. Finally, experiments were conducted with a large number of dynamic particle images to validate the effectiveness of the proposed method and the performance of the approach was also evaluated. This study provides a new practical approach to acquire clear images for on-line wear monitoring. View Full-Text
Keywords: image restoration; particle separation; wear particle; on-line wear monitoring image restoration; particle separation; wear particle; on-line wear monitoring
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MDPI and ACS Style

Peng, Y.; Wu, T.; Wang, S.; Kwok, N.; Peng, Z. Motion-Blurred Particle Image Restoration for On-Line Wear Monitoring. Sensors 2015, 15, 8173-8191. https://doi.org/10.3390/s150408173

AMA Style

Peng Y, Wu T, Wang S, Kwok N, Peng Z. Motion-Blurred Particle Image Restoration for On-Line Wear Monitoring. Sensors. 2015; 15(4):8173-8191. https://doi.org/10.3390/s150408173

Chicago/Turabian Style

Peng, Yeping, Tonghai Wu, Shuo Wang, Ngaiming Kwok, and Zhongxiao Peng. 2015. "Motion-Blurred Particle Image Restoration for On-Line Wear Monitoring" Sensors 15, no. 4: 8173-8191. https://doi.org/10.3390/s150408173

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