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摘要:
针对交互式多模型（IMM）故障诊断方法固定模型转移概率导致的诊断准确性、速度下降和估计精度损失问题，提出了一种基于模型转移概率和模型概率修正的故障诊断方法，并与粒子滤波（PF）结合实现了风机变桨系统传感器的多故障诊断。在非模式切换阶段，采用后验模型概率梯度信息设计模型转移概率的修正函数，以抑制噪声对IMM估计精度的影响；在模式切换阶段，采用模型概率反转的策略快速切换模型，弥补模型软切换导致的诊断延迟和错误诊断。通过仿真实验证明所提方法的准确性、模型切换速度以及状态估计精度都得到了较好的提升。
Abstract:Aimed at the diagnostic accuracy reduction, speed drop and estimation accuracy loss caused by the fixed model transition probability of Interactive Multi-Model (IMM) fault diagnosis method, this paper proposes a fault diagnosis method based on model transition probability and model probability modification, which is combined with the Particle Filter (PF) to achieve multi-fault diagnosis of wind turbine pitch sensor. In the non-mode-switching phase, the posterior model probability gradient information is used to design the modification function of the model transition probability to suppress the influence of noise on the accuracy of IMM estimation. In the mode-switching phase, the model probability inversion strategy is used to quickly switch models to compensate for diagnostic delay and error diagnosis caused by model soft handoff. The simulation results show that the fault diagnosis accuracy, model switching speed and state estimation accuracy of the proposed method are improved.
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表 1 传感器故障模型
Table 1. Sensor failure models
模型 传感器故障类型 故障建模 M1 正常 fs, k=0 M2 恒增益 fs, k=βbias M3 恒偏差 fs, k=－h(·)+kgainh(·) M4 卡死 fs, k=－h(·)+βfixed－vk 注：βbias、kgain和βfixed分别为桨距角测量偏差值、增益系数和固定值。 
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表 2 MIMM-PF与标准IMM-PF性能对比
Table 2. Performance comparison between MIMM-PF and standard IMM-PF
指标 IMM-PF MIMM-PF CDID 564.54(94.09%) 582.76(97.13%) Delay 6.36ΔT 3.6833ΔT RMSE 1.2438 0.5434 注：()内为CDID的百分比形式。
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