Abstract:

In the general stochastic nonlinear and non-Gaussian systems, the sensor faults including biased and scaled readings caused by sudden calibration errors have adverse effect on the precise monitor and stable control of the system. To deal with this problem, a novel diagnosis and identification method is proposed. An adaptive particle filter is developed to calculate the difference between the measurements and the particle filter estimates, then the type and magnitude of sensor faults are determined through with maximum likelihood estimation, thus the fast and precise detection of sensor fault is realized, and the adverse effect caused by the faults can be compensated. Some simulations are carried out on a boiler model, and the results validate the effectiveness of the proposed method.

Key words: Sensor; fault detection and identification, adaptive particle filter, maximum likelihood estimation

摘要： 在随机非线性或非高斯系统中，因突然发生的校准型错误而引发的读数偏置或缩放等传感器故障，会对系统的准确检测与稳定控制带来不利影响。针对该问题，提出一种新型的故障诊断和识别方法。设计一种自适应粒子滤波器来计算测量值和粒子滤波器估计值之差，进而利用最大似然估计来确定故障的类型和幅值，从而精确地诊断传感器故障，并对故障造成的影响进行实时补偿。在锅炉模型中进行了相关仿真实验，实验结果证明了所提方法的有效性。

关键词: 传感器, 故障检测和识别, 自适应粒子滤波器, 最大似然估计