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针对风力机桨距系统故障，提出一种基于观测器的多新息随机梯度辨识算法的故障诊断方法。多新息随机梯度辨识算法通过扩展新息长度能够改进随机梯度辨识算法的估计精度，根据系统的规范状态空间模型，结合状态观测器可以实现系统状态和参数的交互估计。将桨距系统模型转换为可辨识的状态空间模型，依据桨距系统故障会引起系统参数变化的特点，采用所提出的算法对系统状态和参数进行估计，将桨距系统故障诊断问题转化为系统状态和参数估计问题。仿真结果表明，所提出的方法能够有效诊断桨距系统故障。“,”We propose a fault diagnosis method based on the observer-based multi-innovation stochastic gradient al-gorithm in consideration of the pitch system faults of wind turbines.The proposed algorithm can improve the parameter estimation accuracy by extending the innovation length.With regards the observer canonical state space systems model,the multi-innovation stochastic gradient algorithm combined with the state observer is a-ble to achieve the interactive estimation between the system states and the parameters.Here,the pitch system model is further transformed into the identification model by converting it into a canonical state space model. On the basis of the pitch system faults leading to the change of system parameters,the algorithm is adopted to estimate the systemstate and parameters.Then,the pitch system fault diagnosis problem is transformed into the parameter estimation issue.The simulation results demonstrate that the proposed method is capable of ef-fectively diagnosing the pitch system faults.