Real-time detection method of gear contact fatigue pitting based on machine vision

li, hai; Zeng, Chengkai; ZHAO, Peijie; QIN, Zhipeng; Yang, Young-Soo

doi:10.1364/ao.451861

Cited by 3 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DCGAN is applied to expand pitting samples. Li [21] proposed a real-time detection of gear tooth pitting based on machine vision. He used a principle of gear meshing and the shooting principle of a line-scan camera in order to obtain the optimal centrifugal shooting distance for further analysis.…”

Section: Introductionmentioning

confidence: 99%

Detection and Monitoring of Pitting Progression on Gear Tooth Flank Using Deep Learning

et al. 2022

View full text Add to dashboard Cite

Gears are essential machine elements that are exposed to heavy loads. In some cases, gearboxes are critical elements since they serve as machine drivers that must operate almost every day for a more extended period, such as years or even tens of years. Any interruption due to gear failures can cause significant losses, and therefore it is necessary to have a monitoring system that will ensure proper operation. Tooth surface damage is a common occurrence in operating gears. One of the most common types of damage to teeth surfaces is pitting. It is necessary for normal gear operations to regularly determine the occurrence and span of a damaged tooth surface caused by pitting. In this paper, we propose a machine vision system as part of the inspection process for detecting pitting and monitoring its progression. The implemented inspection system uses a faster R-CNN network to identify and position pitting on a specific tooth, which enables monitoring. Prediction confidence values of pitting damage detection are between 99.5–99.9%, while prediction confidence values for teeth recognized as crucial for monitoring are between 97–99%.

show abstract

Section: Introductionmentioning

confidence: 99%

Detection and Monitoring of Pitting Progression on Gear Tooth Flank Using Deep Learning

et al. 2022

View full text Add to dashboard Cite

show abstract

Sensor concepts for gear damage detection in wind power drives

Merkle,

Binanzer,

Häderle

et al. 2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Gearbox failure in offshore wind turbines can be caused by fatigue damages like tooth fank fracture (TFF) or pitting damage. For damage detection in current applications, simple threshold values for vibration amplitudes are used to initiate a shutdown of the wind turbine. At this time significant damage to the gear is already present and an external maintenance is inevitable. The objective of this experimental study is to determine a better sensor concept for detection of gear damage in wind power drives during operation. The damage needs to be detected at a very early stage before the health condition of the gears becomes critical. Evaluation criteria for comparison of sensor concepts include the earliest possible detection of damage, robust and reliable detection under varying operating conditions, as well as practicality and cost-effectiveness. The test results show, vibration measurement in radial direction and in high frequency ranges is most suitable and most robust for pitting detection during operation. With the best sensor concept in place, it is possible to detect gear damage like pitting at a very early stage of 0.3 % pitting size.

show abstract

Online detection technology of triangular-blade tool grinding precision based on machine vision

Hong,

Ji,

Wang

et al. 2024

Appl. Opt.

View full text Add to dashboard Cite

To address the problems of repeat positioning accuracy and secondary clamping caused by a triangular-blade tool during grinding, this paper proposes an online detection method for the machining accuracy of the triangular-blade tool based on machine vision. The method utilizes a weighted average approach for grayscale processing of the original image and an adaptive median filtering algorithm for filtering and noise reduction. The processed image is then binarized. The Canny and Zernike moment edge detection algorithms are utilized for pixel-level and sub-pixel-level edge positioning. A curvature-based feature extraction method is proposed to complete image stitching. Measurement software is designed and developed on the MATLAB app designer platform. Experimental results show that the relative error in tool grinding length is within 0.094%, the average error in cutting edge width is 1.105%, the relative error in thickness is 5.065%, and the relative error in symmetry is 6.044%. The accuracy of the proposed method is confirmed through a comparison between image and microscope measurement.

show abstract

Real-time detection method of gear contact fatigue pitting based on machine vision

Cited by 3 publications

References 6 publications

Detection and Monitoring of Pitting Progression on Gear Tooth Flank Using Deep Learning

Detection and Monitoring of Pitting Progression on Gear Tooth Flank Using Deep Learning

Sensor concepts for gear damage detection in wind power drives

Online detection technology of triangular-blade tool grinding precision based on machine vision

Contact Info

Product

Resources

About