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摘要:
针对非线性非高斯系统在实际工作环境中受强噪声干扰影响导致的故障诊断精度低的问题，提出了一种状态转移密度方差自适应更新的代价评估粒子滤波（CRPF）故障诊断方法。通过设计观测值与先验状态之间的相关性判别函数，根据噪声和误差的大小实时自适应调整状态转移密度方差，增强算法对强噪声干扰的适应能力；研究了残差自适应阈值的设计方法，通过引入滑动窗求区间均值代替基于参数置信区间自适应阈值的均值和方差，在保证故障诊断准确性的前提下减少计算时间。以160 MW燃油机组为例，通过对不同强噪声环境下的汽包水位传感器故障诊断实例分析，结果表明该方法在复杂噪声环境下故障诊断的准确性得到了明显提高，同时减少了计算时间。
Abstract:Aimed at the problem of low precision in fault diagnosis of nonlinear non-Gaussian system due to serious noise interference under the actual working condition, this paper puts forward a new fault diagnosis method, which can adaptively update the state transition density variance of a cost reference particle filter (CRPF). By designing the correlation discriminant function between the measurement value and the prior state, the variance of the state transition density was adjusted adaptively according to the magnitudes of noise and error, and the adaptability of the algorithm to strong noise interference is dramatically enhanced. Furthermore, the method for designing adaptive threshold of residual was studied, and the sliding window was also introduced to calculate the mean of interval instead of the mean and variance of the adaptive threshold based on parameter confidence interval, which was expected to reduce the calculation time under the premise of ensuring the accuracy of fault diagnosis. Taking 160 MW fuel unit as an example, drum level sensor fault diagnoses under different strong noise conditions were analyzed. From the results, it is found that the accuracy of fault diagnosis in the complex noise environment is obviously improved and the computation time is greatly reduced.
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图 1 伽马噪声背景下2种算法对x1和x2的跟踪误差对比
Figure 1. Tracking error comparison of x1 and x2 between two algorithms under condition of Gamma noise
图 2 高斯混合噪声背景下2种算法对x1和x2的跟踪误差对比
Figure 2. Tracking error comparison of x1 and x2 between two algorithms under condition of Gaussian mixture noise
图 4 伽马噪声背景下故障检测及隔离
Figure 4. Fault detection and isolation under condition of Gammanoise
图 5 高斯混合噪声背景下故障检测及隔离
Figure 5. Fault detection and isolation under condition of Gaussian mixture noise
表 1 不同噪声背景下x1、x2的平均绝对误差
Table 1. Mean absolute errors of x1 and x2 under different noise conditions
状态 伽马噪声 高斯混合噪声 改进前 改进后 改进前 改进后 x1 0.011 1 0.008 2 0.195 2 0.009 5 x2 3.575 0 1.707 1 4.365 9 0.886 0 
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