Abstract
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.
Highlights
Gearbox is one of the key components in rotating machinery, and the failure rates of its components are respectively about: gear 60%, bearing 19%, shaft 10%, case 7%, tight solid 3%, oil seal 1% [1]
We address to study a methodology based on deep learning for quantitatively measuring gear pitting, which is achieved by target detection and instance segmentation
The gear pitting images were binarized and grayscaled, and they could be processed by the pitting foreground segmentation algorithm
Summary
Gearbox is one of the key components in rotating machinery, and the failure rates of its components are respectively about: gear 60%, bearing 19%, shaft 10%, case 7%, tight solid 3%, oil seal 1% [1]. Gears have high fault probabilities because of their complex structures and harsh operating conditions [2,3]. Gear pitting is one of the most common failure modes of gears. Under long-term load working conditions, due to the contact fatigue stress, the material on the gear meshing surface is peeled off, forming early pitting. If the early pitting failures are not detected in a timely manner, they deteriorate rapidly and even lead to broken teeth so as to cause catastrophic economic losses and casualties [4,5]. In order to prevent the gear pitting from deteriorating, it is necessary to detect the faults as early as possible
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