A fault diagnosis method for optical elements based on infrared and visible light videos
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摘要: 光学元件的健康状态是激光系统稳定运行的关键，如何在激光系统工作状态下实现光学元件的实时监测和故障诊断定位是该专业领域亟需解决的问题。针对该问题，提出了一种基于红外和可见光视频信息的光学元件故障诊断方法。首先，使用长波红外相机和可见光相机采集光学元件工作过程中的视频信息；然后，对采集的视频信息使用异常点检测算法进行处理；最后，结合光学元件温升特性对光学元件进行故障诊断及定位。试验结果表明：相同算法情况下，该方法相较于单独使用红外视频进行故障诊断的方法在故障诊断准确率、虚警率和漏警率3个指标上分别提升9.70%、3.60%和6.10%；该方法相较于单独使用可见光视频进行故障诊断的方法在故障诊断准确率、虚警率和漏警率3个指标上分别提升18.00%、16.00%和2.00%。Abstract: Optical elements are the core components of laser systems, and their health status is the key to the stable operation of laser systems. How to realize real-time monitoring and fault diagnosis of optical elements in the working status of laser systems is a problem that urgently needs to be solved in this professional field. To solve this problem, this paper proposes a fault diagnosis method for optical elements based on infrared and visible light videos. Firstly, a long-wave infrared camera and a visible light camera are used to collect video information during the working process of the optical element. Then, the collected video information is processed using anomaly point detection algorithms. Finally, the fault diagnosis and localization of the optical element are carried out in combination with the thermal rise characteristics of the optical element. The experimental results show that, under the same algorithm, the method proposed in this paper has improved the fault diagnosis precision rate, false alarm rate and missed alarm rate by 9.70%, 3.60% and 6.10%, respectively, compared with the method of fault diagnosis using infrared videos alone; the method proposed in this paper has improved the fault diagnosis precision rate, false alarm rate and missed alarm rate by 18.00%, 16.00% and 2.00%, respectively, compared with the method of fault diagnosis using visible light videos alone.
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Key words:
- optical element /
- health status /
- infrared video /
- visible light video /
- fault diagnosis
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图 5 不同二值化阈值下可见光故障定位时间
Figure 5. Visible light FLT under different binarization threshold
图 6 不同二值化阈值下可见光FDPR、FAR、MAR
Figure 6. Visible light FDPR, FAR and MAR under different binarization threshold
表 1 两种视频异常点检测情况说明表
Table 1. Description of two videos’ anomaly detection
infrared video visible light video process flow case1 normal normal normal process flow case2 abnormal normal next step, analyze temperature information case3 abnormal abnormal position matching，output fault information directly case4 normal abnormal impossible case5 abnormal abnormal position mismatching，impossible 
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表 2 三种方法的指标对比结果表
Table 2. Experimental results of indicators of three methods
No. FDPR /% FLT/ms FAR /% MAR /% method 1 88.80 228 4.60 6.60 method 2 80.50 97 17.00 2.50 method 3 98.50 135 1.00 0.50
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