Next Article in Journal
A Deep Learning Model for Fault Diagnosis with a Deep Neural Network and Feature Fusion on Multi-Channel Sensory Signals
Next Article in Special Issue
Fault Detection and Classification in MMC-HVDC Systems Using Learning Methods
Previous Article in Journal
Variability of Muscle Synergies in Hand Grasps: Analysis of Intra- and Inter-Session Data
Previous Article in Special Issue
Fault Diagnosis of Rotary Machines Using Deep Convolutional Neural Network with Wide Three Axis Vibration Signal Input
Open AccessArticle

Vision Measurement of Gear Pitting Under Different Scenes by Deep Mask R-CNN

State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(15), 4298; https://doi.org/10.3390/s20154298
Received: 9 June 2020 / Revised: 29 July 2020 / Accepted: 30 July 2020 / Published: 1 August 2020
To accurately and quantitatively detect the gear pitting of different levels on the actual site, this paper studies a new vision measurement approach based on a tunable vision detection platform and the mask region-based convolutional neural network (Mask R-CNN). The shooting angle can be properly set according to the specification of the target gear. With the obtained sample set of 1500 gear pitting images, an optimized deep Mask R-CNN was designed for the quantitative measurement of gear pitting. The effective tooth surface and pitting was firstly and simultaneously recognized, then they were segmented to calculate the pitting area ratio. Considering three situations of multi-level pitting, multi-illumination, and multi-angle, several indexes were used to evaluate detection and segmentation results of deep Mask R-CNN. Experimental results show that the proposed method has higher measurement accuracy than the traditional method based on image processing, thus it has significant practical potential. View Full-Text
Keywords: gear pitting; Mask R-CNN; tunable vision detection platform; machine vision; deep learning gear pitting; Mask R-CNN; tunable vision detection platform; machine vision; deep learning
Show Figures

Figure 1

MDPI and ACS Style

Xi, D.; Qin, Y.; Wang, Y. Vision Measurement of Gear Pitting Under Different Scenes by Deep Mask R-CNN. Sensors 2020, 20, 4298.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop