Citation: | Zheng Zhaoyang, Zhang Tianshu, Dong Yunsheng, et al. Identification of hardware fault data of particle LiDAR[J]. Opto-Electronic Engineering, 2019, 46(7): 190100. doi: 10.12086/oee.2019.190100 |
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Overview: Particle LiDAR is a high-precision instrument with laser as the emitter. It continuously monitors the temporal and spatial evolution and characteristics of aerosol, boundary layer, cloud height and multi-layer cloud structure, thus obtaining the three-dimensional structure of atmospheric aerosol distribution with detailed changes, which has strong ability and high degree of automation. The particulate matter LiDAR is fully covered in the area to achieve high-temporal resolution air pollution monitoring, combined with the application of informational big data to achieve pollution source tracking, early warning, forecasting functions, etc., to provide more timely and effective decision support for environmental pollution prevention and control. However, when the hardware of the radar's transmitting unit, receiving unit, etc. fails, there will often be abnormal echo data generated, which will directly affect the subsequent inversion results and have a great influence on the accuracy of the above applications. As a long-term, high-intensity, continuous operation high-precision equipment, atmospheric particulate matter monitoring LiDAR affected by factors such as working environment and accessory quality, and hardware failure is difficult to avoid.
The hardware fault of the LiDAR will make the quality of the echo data worse. However, there is still a lack of effective identification methods for the error data caused by the hardware failure. Analysis of echo characteristics of atmospheric particulate matter monitoring when LiDAR has hardware failure, according to the echo signal information of the echo shape and intensity of the radar, the fuzzy logic algorithm is used to identify the fault data. The hardware fault data of the atmospheric particulate radar is identified and tested. At the same time, in order to reduce the false positive rate of data without hardware failures, the mean values of extinction coefficient and signal-to-noise ratio (SNR) at the height of 300 meters to 500 meters were compared between the data of hardware failures and the data was misjudged, reducing the false positive rate by setting the signal to noise ratio threshold. The experimental results show that this method is used to identify the hardware fault data of the LiDAR monitoring of the external field, the recognition rate is 94.6%, and the false positive rate is only 1.5%. This method has a good recognition effect on hardware fault data.
The method adopted in this paper can also realize the real-time monitoring of the LiDAR operating state and achieve real-time warning of the LiDAR running state, which provides a reference for us to find faults in time and ensures the normal operation of the equipment.
Statistical distribution of Z/Z′ and r probabilities without hardware failure data. (a) Z/Z′; (b) r
Statistical distribution of Z/Z′ and r probabilities with hardware failure data. (a) Z/Z′; (b) r
Z/Z′ and r corresponding membership function. (a) Z/Z′; (b) r
Echo waveform of misjudged data. (a) Cloud; (b) Haze; (c) Dust
Cluster analysis of the mean value of SNR and extinction coefficient from 300 meters to 500 meters