Abstract
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.
Highlights
Digital image processing technology is widely used in engineering applications, such as remote sensing [1,2], 3D modeling [3], object recognition [4,5], and machine condition monitoring [6,7].In particular, to meet the on-line monitoring requirement for fault detection and diagnosis, versatile oil monitoring sensors have been developed in the past decades
To evaluate the performance of the proposed motion-blurred image restoration method, dynamic wear debris images were captured from on-line monitoring lubrication samples of a mine scraper conveyor gearbox
In order to solve the motion-blurred problem in an on-line particle imaging system for wear debris analysis, an image restoration method was developed for improving the quality of dynamic particle images
Summary
Digital image processing technology is widely used in engineering applications, such as remote sensing [1,2], 3D modeling [3], object recognition [4,5], and machine condition monitoring [6,7].In particular, to meet the on-line monitoring requirement for fault detection and diagnosis, versatile oil monitoring sensors have been developed in the past decades. There are five major groups of existing sensors classified according to their physical principles as follows [8]. Photoelectric-based sensors can provide the contour of particle projection by using a photoelectric conversion device based on shading principle. Induction sensors take advantage of the physical phenomenon that a moving wear particle would disturb a pre-set electromagnetic field to obtain the volume information of particles. Electric sensors adopt a similar principle with inductive ones where the electromagnetic field is replaced with an electric field. Sensors based on ultrasonic principle, close to photoelectric one, employ the reflection of ultrasonic by solid particles to detect the object area. Imaging-based sensors are used to capture images to extract profound information of wear particles, including quantity, size, morphologies and color, for wear process and mechanism examinations
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