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针对高精度传感器硬件冗余成本巨大的问题,提出了不同精度的冗余传感器故障诊断方法.该方法采用动态模型不确定性影响最小化而故障影响最大化的原则,对低精度传感器数据进行预处理,轮流使用一个传感器作为输入,另一个作为输出建立卡尔曼滤波方程组,并通过所得新息进行故障诊断.实验表明,所提出方法能有效抑制低精度传感器的噪声干扰,降低成本以及系统建模复杂性,在传感器故障诊断的工程应用中具有较好的实用性.
Aiming at the huge redundancy cost of high-precision sensor hardware, a new fault diagnosis method of redundant sensors with different accuracy is proposed.The principle of minimizing the influence of the uncertainty of the dynamic model and the influence of the fault is proposed in this method, Processing, using one sensor in turn as input and the other as output to establish Kalman filter equations, and diagnosing the fault through the new information obtained.Experiments show that the proposed method can effectively reduce the noise interference of low-precision sensors, reduce the cost and the system construction The complexity of the model has good practicability in engineering application of sensor fault diagnosis.