磁浮列车双冗余加速度传感器故障检测研究

磁浮列车双冗余加速度传感器外置于电磁铁之上，运行时受振动、电磁干扰、温湿度变化等因素影响，测量特性不稳定，体现为动态的测量噪声。现阶段工程中常采用测试对比法检测冗余传感器，动态噪声下易出现虚警漏警的问题，检测准确率低，因此提出了基于自适应多点广义似然比检验的传感器故障检测方法。采用多点判决形式，增强对离群点的鲁棒性，同时引入滑动窗方差估计，递推估计奇偶向量的方差，调整判决函数，实现对动态噪声的自适应。经小比例悬浮试验台验证，相比同类传统方法，算法在静态噪声实验中检测准确率提高15%,在动态噪声实验中检测准确率提高13%,且显著改善了虚警漏警抑制能力，对动态噪声具有良好的鲁棒性。

Research on fault detection of double redundant acceleration sensor for maglev train

The dual-redundant acceleration sensor of the maglev train is externally mounted on the electromagnet. It is affected by vibration, electromagnetic interference, temperature and humidity change during operation. Its measurement characteristics are unstable, which are reflected by dynamic measurement noise. At present, the comparison test method is often used to detect redundant sensors in engineering practice. The problem of false alarm and miss alarm is easy to occur under dynamic noise, and the detection accuracy is low. Thus, in this article, an adaptive multi point generalized likelihood ratio test algorithm is proposed for sensor fault detection. The multi point decision form is used to enhance the robustness to outliers. In addition, the sliding window variance estimation is introduced to recursively estimate the variance of parity vectors, and the decision function is adjusted to realize the adaptiveness to dynamic noise. The effectiveness of the proposed algorithm is evaluated by experiments on a small-scale suspension test-rig. Compared with similar traditional algorithms, the detection accuracy of the proposed algorithm increased by 15% in static noise experiments, and increased by 13% in dynamic noise experiments. The false alarm rate and missing alarm rate are significantly reduced with good robustness to dynamic noise.