With the development of the wind energy industry, condition monitoring (CM) has become one of the important ways to improve the reliability of wind turbines (WTs). A data driven WTCM method based on a novel multivariate state estimation technique (MSET) is proposed, which can realize the fault early warning of WT components. In order to reduce the information redundancy of operational parameters, the maximum conditional mutual information (CMI) feature selection algorithm is applied to the training data of MSET. To improve the performance and flexibility of MSET, a dynamic memory matrix (MM) construction method based on k-nearest neighbor algorithm is proposed, which can provide a dynamic MM that varies in real-time with the current operational condition. A fast calculation method of the dynamic MM is also proposed, which can reduce the computation time by 15–26%. Two residual-based CM methods are proposed to realize fault early warning. The real-time method is based on the real-time residuals and if several consecutive residuals exceed the threshold, the fault alerts will be issued. The long-term method divides the historical residuals into day-level and analyzes them based on control charts. The proposed method is applied to two real cases of the gearbox and generator bearing overheating faults. The experimental results show that the maximum CMI algorithm and the dynamic MM significantly improve the estimation error of MSET. Compared with the recorded fault information, the proposed method realizes the fault early warning about 2–20 days in advance.

随着风能行业的发展，状态监测（CM）已成为提高风力发电机（WT）可靠性的重要方法之一。提出了一种基于新型多元状态估计技术（MSET）的数据驱动WTCM方法，该方法可以实现WT组件的故障预警。为了减少操作参数的信息冗余，将最大条件互信息（CMI）特征选择算法应用于MSET的训练数据。为了提高MSET的性能和灵活性，提出了一种基于k最近邻算法的动态记忆矩阵（MM）构建方法，该方法可以提供随当前操作条件实时变化的动态MM。还提出了动态MM的快速计算方法，这样可以将计算时间减少15–26％。提出了两种基于残差的CM方法来实现故障预警。实时方法基于实时残差，如果多个连续残差超过阈值，将发出故障警报。长期方法将历史残差划分为日级别，并根据控制图对其进行分析。该方法适用于齿轮箱和发电机轴承过热故障的两种实际情况。实验结果表明，最大的CMI算法和动态MM显着提高了MSET的估计误差。与记录的故障信息相比，该方法可以提前约2–20天实现故障预警。实时方法基于实时残差，如果多个连续残差超过阈值，将发出故障警报。长期方法将历史残差划分为日级别，并根据控制图对其进行分析。该方法适用于齿轮箱和发电机轴承过热故障的两种实际情况。实验结果表明，最大的CMI算法和动态MM显着提高了MSET的估计误差。与记录的故障信息相比，该方法可以提前约2–20天实现故障预警。实时方法基于实时残差，如果多个连续残差超过阈值，将发出故障警报。长期方法将历史残差划分为日级别，并根据控制图对其进行分析。该方法适用于齿轮箱和发电机轴承过热故障的两种实际情况。实验结果表明，最大的CMI算法和动态MM显着提高了MSET的估计误差。与记录的故障信息相比，该方法可以提前约2–20天实现故障预警。长期方法将历史残差划分为日级别，并根据控制图对其进行分析。该方法适用于齿轮箱和发电机轴承过热故障的两种实际情况。实验结果表明，最大的CMI算法和动态MM显着提高了MSET的估计误差。与记录的故障信息相比，该方法可以提前约2–20天实现故障预警。长期方法将历史残差划分为日级别，并根据控制图对其进行分析。该方法适用于齿轮箱和发电机轴承过热故障的两种实际情况。实验结果表明，最大的CMI算法和动态MM显着提高了MSET的估计误差。与记录的故障信息相比，该方法可以提前约2–20天实现故障预警。