降雨量的测量对气象预报及防汛减灾具有重要作用,因此当降雨量传感器发生故障时需要能快速修复.而目前应用最广泛的翻斗式降雨量传感器其故障类型多样,致使故障诊断难度大.为此,本文提出一种基于故障树理论与灰色关联法的降雨量传感器故障诊断新方法,采用故障树理论及灰色关联分析法建立了专家系统,通过故障知识获取、故障树建立、灰色关联度获取、知识库构建和推理机推理设计,实现了降雨量传感器的故障诊断.试验验证表明,该方法故障诊断快速准确,实现系统自动诊断替代人工检测与排查,准确率可达到90％以上,提高了降雨量传感器的诊断与维修效率.

A rainfall sensor which adopts the ratio of water volume as the measurement parameter was designed in this study. The ratio of water volume is unaffected by the platform movement on the sea. The sensor uses a corresponding relationship model of the ratio of water volume and electric capacitance established by experiments and regression analysis. With this corresponding relationship model, the rainfall data is obtained from the measured electric capacitance. According to the subsequent verification test of a prototype, the maximum measurement error of the prototype was 0.24mm, which is able to meet the accuracy requirements of marine rainfall measurement in specifications for oceanographic survey.

从船舶真风解算的模型出发,依据误差传播定律,分析了相对风测量误差和航速误差对真风解算的影响.视风的测量误差会直接传递给真风;航速小于15 kn时,航速误差传递给真风解算的误差可忽略不计,航速大于15 kn时,真风误差会随航速的增大而增大.真风解算模型忽略了海流因素,当水流速度不可忽略时,船舶的运动轨迹受水流影响,分析了海流引起的误差模型,给出了选用不同航向航速源下的真风误差修正量.

The capacitance signal processing is one of key technologies of capacitive precipitation gauge.A capacitance meas-uring circuit based on dual 555 timers was designed.The measured capacitance was put in the oscillation circuit produced by the dual 555 timers, and the oscillation frequency could change when the capacitance changed.The oscillation frequency could subse-quently be transformed into voltage signal by phase distinguish, integral and amplified circuit.The components and parameters of circuit were chosen by calculation so that the relationship between input capacitance and output voltage could be linear.The linear relationship and the feasibility of the circuit are confirmed by experiments.

In order to overcome the disadvantages of existing rain gauges including the obvious inaccuracy while rainfall intensity is strong,and their measurement range is small,a novel automatic rainfall gauge based on the sensitive pressure sensing component has been developed.The hardware of the developed gauge includes a rainfall signal transformer based on a sensitive pressure sensor,a electrical signal processing unit built up with differential amplifier and linear transform circuit,and a computation and procession centre based on the higher speed and lower power consumption embedded processor ARM.The generalized regression neural network(GRNN)is applied in the software of the developed gauge to realize the approximation of function,and a accuracy measurement model has been built by training the GRNN with small data sample.By measuring the pressure and the duration response generated by the rainfall on the sensitive pressure sensor,and calculating with the trained GRNN model,the accuracy rainfall could be obtained.The test results show that the developed rainfall gauge not only could be applied to measure the heavy rain,but also has the merits of wider measurement range and more precision.