Infrared Detection and Identification of Rotating Machinery Based on Faster R-CNN

WANG Yang; YANG Li

Volume 42 Issue 11

Nov. 2020

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WANG Yang, YANG Li. Infrared Detection and Identification of Rotating Machinery Based on Faster R-CNN[J]. Infrared Technology , 2020, 42(11): 1053-1060.

Citation:

WANG Yang, YANG Li. Infrared Detection and Identification of Rotating Machinery Based on Faster R-CNN[J]. Infrared Technology , 2020, 42(11): 1053-1060.

WANG Yang, YANG Li. Infrared Detection and Identification of Rotating Machinery Based on Faster R-CNN[J]. Infrared Technology , 2020, 42(11): 1053-1060.

Citation:

WANG Yang, YANG Li. Infrared Detection and Identification of Rotating Machinery Based on Faster R-CNN[J]. Infrared Technology , 2020, 42(11): 1053-1060.

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Infrared Detection and Identification of Rotating Machinery Based on Faster R-CNN

WANG Yang,
YANG Li

College of Power Engineering, Naval University of Engineering, Wuhan 430033, China

Received Date: 2020-03-07
Rev Recd Date: 2020-09-08
Publish Date: 2020-11-20

Abstract

Abstract

Rotating machinery is the core component of mechanical equipment and can thus cause a significant loss if it breaks down. Therefore, real-time monitoring and diagnosis of the rotating machinery is critical. Automated infrared intelligent monitoring and diagnosis is a recent development in fault diagnosis. To realize infrared intelligent monitoring and diagnosis, it is necessary to accurately identify rotating machinery components. In this study, an infrared thermal camera was used to monitor the running state of the rotating machinery and infrared images of the motor, coupling, bearing seat, gearbox, and other equipment. The Faster R-CNN algorithm was used to train the rotating-machinery infrared images and to identify the targets. The results showed that the algorithm can accurately identify rotating machinery components. The recognition effect of single-angle and rotating-angle infrared monitoring was studied. It was found that the detection effect of infrared gray images fortraining at the same angle is better than that of infrared pseudo-color images. The influence of four types of pre-training networks on infrared target recognition was compared. The average detection accuracy of the resnet50 pre-training network was 0.9345, and the recognition accuracy was higher.
- rotating machinery,
- infrared detection,
- object identification,
- Faster R-CNN

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References(25)

References

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