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首页> 外文期刊>International Journal Of Modelling & Simulation >Wind turbine gearbox condition monitoring and fault diagnosis based on multi-sensor information fusion of SCADA and DSER-PSO-WRVM method
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Wind turbine gearbox condition monitoring and fault diagnosis based on multi-sensor information fusion of SCADA and DSER-PSO-WRVM method

机译：基于SCADA和DSER-PSO-WRVM方法多传感器信息融合的风力涡轮机变速箱状态监测与故障诊断

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摘要

A gearbox is one of the important parts in wind turbines (WTs). This part has a high downtime rate. In this paper, a structure is presented based on SCADA information fusion, and there is no need for adding new sensors and extra data collection equipment. The proposed method is an intelligent method based on generation and extraction of a feature from residual signal. The behavioural model of gearbox thermal signals is extracted from an Artificial Neural Network (ANN). The feature vector is the input of the feature level fusion algorithm. Relevance Vector Machine (RVM) is used for the feature level fusion. RVM is a kernel-based learning algorithm. Wavelet (W) basis is used as kernel function. To determine the optimal parameters of WRVM, Particle Swarm Optimization (PSO) is used as optimization algorithm. The initial decision-making in each PSO-WRVM block made the decision matrix based on the intensity of the fault. This matrix is the input of the Dempster-Shafer evidential reasoning (DSER) for the final decision-making. The simulation and experimental are conducted based on the data from a real 2.5 MW WT with faulty and healthy gearboxes. Results show that the proposed strategy is successful in diagnosis of gearbox serious damages.
机译：变速箱是风力涡轮机（WTS）中的重要部件之一。这部分具有高停机率。在本文中，基于SCADA信息融合呈现了一种结构，并且不需要添加新的传感器和额外的数据收集设备。所提出的方法是基于生成和提取来自残差信号的智能方法。齿轮箱热信号的行为模型从人工神经网络（ANN）中提取。特征向量是特征级融合算法的输入。相关矢量机（RVM）用于特征级融合。 RVM是基于内核的学习算法。小波（W）的基础用作内核功能。为了确定WRVM的最佳参数，粒子群优化（PSO）用作优化算法。每个PSO-WRVM块中的初始决策基于故障强度使得决策矩阵。该矩阵是对最终决策的Dempster-Shafer简单推理（DSER）的输入。基于具有故障和健康齿轮箱的真实2.5 MW WT的数据进行仿真和实验。结果表明，拟议的策略在诊断齿轮箱严重损害方面取得了成功。

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