Wind Turbine Drivetrain Gearbox Fault Diagnosis Using Information Fusion on Vibration and Current Signals
Wind Turbine Drivetrain Gearbox Fault Diagnosis Using Information Fusion on Vibration and Current Signals
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DOI：
10.1109/tim.2021.3083891
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发表时间：
2021
影响因子：
5.6
通讯作者：
Yayu Peng;W. Qiao;Fangzhou Cheng;Liyan Qu
中科院分区：
文献类型：
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作者：
Yayu Peng;W. Qiao;Fangzhou Cheng;Liyan Qu
为提高传统基于振动的涡轮机传动系统齿轮箱故障诊断系统的可靠性，提出了一种融合齿轮箱振动信号和发电机电流信号的故障诊断方法。首先，分别分析了齿轮箱振动信号和发电机电流信号所包含的故障特征。其次，提出了一种具有概率输出的多类支持向量机（SVM）模型，设计了两个分类器，分别根据从振动信号和电流信号中提取的输入故障特征输出不同齿轮箱故障类型的概率。然后，分别基于Dempster-Shafer理论和softmax回归技术设计了不可训练组合器和可训练组合器，在决策层融合来自振动和当前SVM分类器的信息。每个组合器的输出是最终的诊断结果。从测试齿轮箱不同类型的故障得到的实验结果验证了所提出的方法。验证结果表明，该方法能提高故障诊断的准确率，且比传统的单一信号故障诊断系统具有更好的鲁棒性。
To improve the reliability of the conventional vibration-based wind turbine drivetrain gearbox fault diagnosis system, this article proposes a novel fault diagnosis method by fusing the information from gearbox vibration and generator current signals. First, the fault features contained in the gearbox vibration signals and the generator current signals are analyzed, respectively. Second, a multiclass support vector machine (SVM) model with probabilistic output is proposed to design two classifiers which output the probabilities of different gearbox fault types according to the input fault features extracted from the vibration signals and the current signals separately. Then, a nontrainable combiner and a trainable combiner are designed based on the Dempster–Shafer theory and the softmax regression technique, respectively, to fuse the information from the vibration and current SVM classifiers at decision level. The output of each combiner is the final diagnosis result. The proposed method is validated by experimental results obtained from a test gearbox with different types of faults. The validation results show that the proposed method can increase the fault diagnostic accuracy and is more robust than the conventional fault diagnosis systems that only use one type of signals.