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Machine Learning-Based Adaptive Fault Detection Method for Wind Turbine Gearboxes with Imbalanced Data through An IIoT Platform
参考中译:基于机器学习的风力机齿轮箱不平衡数据自适应故障检测方法


     

文集名：4th International Academic Exchange Conference on Science and Technology Innovation (IAECST 2022)
作者：Xiao Fu（School of Information Management Shanghai Lixin University of Accounting and Finance）
Xinnuo Li（Golden Data Ltd.）
Kuo Cao（Golden Data Ltd.）
Junyi Han（Yingke Zhushu Network Technology Co., Ltd.）
会议名：4th International Academic Exchange Conference on Science and Technology Innovation (IAECST 2022)
会议日期：9-11 December 2022
会议地点：Guangzhou, China
出版年：2022
页码：1383-1387
总页数：5
馆藏号：347926
分类号：G3-53/I59/(4th-v.2)
关键词：Industrial internet of things (IIoT)；Condition monitoring；Adaptive fault detection；Imbalanced data；Wind turbine gearbox
参考中译：工业物联网（IIoT）；状态监测；自适应故障检测；不平衡数据；风力涡轮机齿轮箱
语种：eng
文摘：In the context of Industry 4.0, machine learning algorithms have been commonly used to monitor the health state of wind turbine gearboxes to avoid catastrophic failure and reduce maintenance costs. However, due to the lack of a certain category of data (i.e., healthy or faulty) and the various working conditions of wind turbines, many existing methods may not provide reliable results in practical industrial applications. To solve this problem, we create an industrial internet of things (IIoT) platform, through which a machine learning-based adaptive fault detection method for wind turbine gearboxes is proposed. The features are extracted and adapted to fine-tune the pre-trained model on newly arriving samples from different wind turbines, components, or failure modes. The adaptation performance is evaluated with accuracy, false alarm rate, and fault detection rate. Case studies are then performed using high-frequency vibration signals acquired from two megawatts (MW) onshore wind turbines. The results show that the proposed adaptive method significantly improves the fault detection performance when class distribution is not balanced, and can be easily applied to the fault diagnosis of large numbers of wind turbines. This, integrated with the IIoT platform that alleviates the shortage of computational and storage capacity in wind farms and requires less user involvement, allows for a more effective condition monitoring system.
参考中译：在工业4.0的背景下，机器学习算法已被普遍用于监测风电机组齿轮箱的健康状态，以避免灾难性故障，降低维护成本。然而，由于缺乏某一类数据(即健康或故障)以及风力机的各种工况，许多现有的方法在实际工业应用中可能不会提供可靠的结果。为了解决这一问题，我们创建了一个工业物联网(IIoT)平台，在此平台上提出了一种基于机器学习的风电齿轮箱自适应故障检测方法。提取特征并对其进行调整，以根据来自不同风力涡轮机、部件或故障模式的新样本来微调预先训练的模型。通过准确率、虚警率和故障检测率来评估自适应性能。然后，使用从两兆瓦陆上风力涡轮机获取的高频振动信号进行案例研究。结果表明，在类别分布不均衡的情况下，自适应方法显著提高了故障检测性能，可方便地应用于大量风电机组的故障诊断。这与IIoT平台相结合，缓解了风力发电场计算和存储能力的不足，需要更少的用户参与，从而实现了更有效的状态监测系统。

注：参考中译为机器自动翻译，仅供参考。



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