Fast Automatic Failure Detection System Preliminary for LED Lamps Based on Machine Vision Technology

Abstract:

Using machine vision technology to realize the real-time automatic collection of test parameters including light and color of LED lamps during the experiments. The failure mechanisms were researched and the reference failure mechanisms were given according to the parameters change situation of the failure lamps, in order to implement rapid and automatic detection of test conditions of LED lamps, which can accelerate the research processes of LED lamps and related products, and promote the industrialization process of LED. The fast automatic failure detection system for LED lamps based on machine vision technology proposed in this paper had great significance for LED lamps research organizations such as testing organizations, research institutes, as well as manufacturers, and it was worthy of learning for them.

• Periodical: Applied Mechanics and Materials (Volumes 511-512)
• Pages: 518-523
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.511-512.518
• Online since: February 2014
• Authors: Zhong Hong Shen, Qun Xing Liu
• Keywords: Failure, LED Lamps, Light and Color Parameter, Machine Vision Technology, Rapid and Automatic Detection

References

[1] Caristian Steger, Markus Ulrich, Christian Wiedemannn. Machine Vision Algorithms and Applications[M]. Yangshao Rong Wu Di Jing, Duan Tokuyama, translated, Beijing, Tsinghua University Press, (2008).

[2] The semiconductor light source Prospectus of Guangdong Province. The semiconductor light source Industry Association of Guangdong Province [OL]. http: /www. gdledia. cn/about. asp?cat_id=420.

[3] You Dan, Xu Nini, Yue Jianfeng. Auto-checking system of LED lamp image on electrical-bicycle metersets [J]. Tianjin Polytechnic University, 2012, 31 (1) : 78-80.

[4] Liang Wei, Tan Guang Wei . Detection Technology automatically detects line of CNC machine tools [J]. Machinery & Electronics, 2012, 35: 148, 157.

[5] Xujie Qiong. Vehicle detection and tracking method based on video image processing [D]. Qingdao : Ocean University of China , (2012).

[6] Gao Wei Xin, Hu Yuheng , Mu Xiangyang , etc. Real-time detection and segmentation of submerged-arc welding defects in X-ray radiography images [J]. Scientific Instrument, 2011, 32 ( 6 ) : 1215 -1224.

[7] Li Jinhui . Pavement crack diseases detecting by image prcessing algorithm [J]. Journal of Chang'an University , 2004, 24 ( 3 ) : 24 -29.

[8] Shenzhong Hong , Jiang Chunxu , Zhao Haozhi, et al. Lapse detection and light decay detection automatically of LED lamps based on machine vision [C]. The twentieth Proceedings of lighting technology and marketing seminars across the Taiwan Strait , 2013, 341-348.

[9] Chen Zhonglin , Weng Ji, et al. A study on measurement of luminance distribution by using digital camera [J]. ZhaoMingGongChengXuebao, 2005, 16 ( 3 ) : 11 -14.

[10] Wei Yongjian. The new methods for testing and design of sports lighting quality [D]. Tianjin: Tianjin University, 2005.

[11] Gu Bing, Zhan Qing Xuan, Zhu Zhiqiang . Research on measurement of luminance distribution using a digital cameras [J]. ZhaoMingGongChengXuebao, 2003, 14 ( 1 ) : 15 -18.

[12] Shenzhen Hongye Deko . LED packaging plastic [W]. Http: /baike. soso. com/h54230514. htm?sp=l54230515.

[13] Shi Ting, Chen Yuming . Ripple current of the light emitting diode (LED) properties [J]. Lamps and Lighting, 2012, 2: 1 -2.

[14] Ron Lenk, Carol Lenk. Practical Lighting Design with LEDs[M]. 2011, Wiley-IEEE Press.

DOI: 10.1002/9781118008218