Restoration of online video ferrography images for out-of-focus degradations

Xi, Wenkui; Wu, Tonghai; Yan, Ke; Yang, Xujun; Jiang, Xinyu; Kwok, Ngaiming

doi:10.1186/s13640-018-0270-1

Cited by 10 publications

(6 citation statements)

References 37 publications

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“…Ferrography technology is a technology to infer the health state of the machine by detecting the characteristics of wear particles, which is widely used in the wear debris of gearbox oil samples [24,25]. The morphology of wear particles observed by ferrography analysis can effectively re ect the wear degree in different characteristic periods.…”

Section: Ferrography Analysis Of Wear Particlesmentioning

confidence: 99%

Study on Wear Characteristics Evolution of Helical Gear With Linear Initial Defect

Zhu

Yuan

Guo

et al. 2021

Preprint

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The initial defects have greatly affected the gear transmission under harsh working conditions in the fields of wind power and ships. The influence of linear initial defects on the evolution of wear characteristics of helical gears was studied. The laser marking device was used to process the linear initial defect along the tooth width direction, and the gear without initial defect was used for comparison. It can be concluded that the linear initial defect changed the meshing state of the gear tooth, and greatly shortened the normal wear life of the gear, the normal wear life of the gear is shortened by about 45%, and the wear rate in the stable wear stage is increased by about 56%, a great deal of pitting corrosion and plastic flow on the tooth surface occurred in the pitch circle position of the defective gear. In addition, the lubrication condition deteriorated in the later period caused by lubricating oil pollution and the hard particles falling off the gearbox bearings entered the meshing surface and the emerged crack, which further accelerated the wear process of gear.

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Section: Ferrography Analysis Of Wear Particlesmentioning

confidence: 99%

Study on Wear Characteristics Evolution of Helical Gear With Linear Initial Defect

Zhu

Yuan

Guo

et al. 2021

Preprint

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“…Thus, in developing our EF, and similar to [25], we quantified the following qualities of a well-enhanced image as follows: A well enhanced image should have a higher number of edge pixels than the original image. Furthermore, an enhanced image is expected to have a higher measure of information than the original image.…”

Section: Evaluation Functionmentioning

confidence: 99%

A new evaluation function for face image enhancement in unconstrained environments using metaheuristic algorithms

Oloyede

Hancke

Myburgh

et al. 2019

J Image Video Proc.

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Image enhancement is an integral component of face recognition systems and other image processing tasks such as in medical and satellite imaging. Among a number of existing image enhancement methods, metaheuristic-based approaches have gained popularity owing to their highly effective performance rates. However, the need for improved evaluation functions is a major research concern in the study of metaheuristic-based image enhancement methods. Thus, in this paper, we present a new evaluation function for improving the performance of metaheuristic-based image enhancement methods. Essentially, we applied our new evaluation function in conjunction with metaheuristic-based optimization algorithms in order to select automatically the best enhanced face image based on a linear combination of different key quantitative measures. Furthermore, different from other existing evaluation functions, our evaluation function is finitely bounded to determine easily whether an image is either too dark or too bright. This makes it better suited to find optimal solutions (best enhanced images) during the search process. Our method was compared with existing metaheuristic-based methods and other state-of-the-art image enhancement techniques. Based on the qualitative and quantitative measures obtained, our approach is shown to enhance facial images in unconstrained environments significantly.

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Section: Introductionmentioning

confidence: 99%

“…A large number of practices have proved that computer image recognition technology is of great significance to improve the accuracy of ferrography analysis, shorten the analysis time, and reduce the workload of analysts. 11 With the continuous development of computer image processing technology and the intelligent development of ferrography analysis technology, the majority of scientific and technological workers have made certain achievements through unremitting research. The following is an in-depth analysis of the research progress and existing problems of abrasive particle identification technology at home and abroad.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis method of mechanical power system based on image processing technology

Qiu

Zhou³

2020

International Journal of Advanced Robotic Systems

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Fault detection and diagnosis become one of today’s hot spots, which describes that image information is an important form of fault information, it can quickly through the image processing technique, and can accurately extract the characteristic signal. This article selects the color of the particle image, the integrated use of digital image processing, pattern recognition theory, the characteristic parameters of tribology knowledge, as well as the extraction, optimization, and digital; verifies the feasibility of iron spectrum of abrasive fault recognition, and provides a new efficient ferrographic wear particle image recognition method. Firstly, the grindstone image of the original color diesel engine was preprocessed, and the grindstone image of the ferrograph was identified by directly selecting grindstone from the preprocessed ferrograph image and selecting the target grindstone. According to the two types of abrasive particles, the characteristic parameters were first classified, and then the values of the characteristic parameters were obtained through the training and learning of the sample abrasive particles. In view of the large number of characteristic parameters of ferro-spectrum abrasive particles, this article determined the characteristic parameters suitable for the identification of abrasive particles in this article through the feature optimization and proved the correctness of the identification of characteristic parameters of abrasive particles through the test.

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Restoration of online video ferrography images for out-of-focus degradations

Cited by 10 publications

References 37 publications

Study on Wear Characteristics Evolution of Helical Gear With Linear Initial Defect

Study on Wear Characteristics Evolution of Helical Gear With Linear Initial Defect

A new evaluation function for face image enhancement in unconstrained environments using metaheuristic algorithms

Fault diagnosis method of mechanical power system based on image processing technology

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