Actuator and Sensor Fault Classification for Wind Turbine Systems Based on Fast Fourier Transform and Uncorrelated Multi-Linear Principal Component Analysis Techniques

In response to the high demand of the operation reliability and predictive maintenance, health monitoring and fault diagnosis and classification have been paramount for complex industrial systems (e.g., wind turbine energy systems). In this study, data-driven fault diagnosis and fault classification strategies are addressed for wind turbine energy systems under various faulty scenarios. A novel algorithm is addressed by integrating fast Fourier transform and uncorrelated multi-linear principal component analysis techniques in order to achieve effective three-dimensional space visualization for fault diagnosis and classification under a variety of actuator and sensor faulty scenarios in 4.8 MW wind turbine benchmark systems. Moreover, comparison studies are implemented by using multi-linear principal component analysis with and without fast Fourier transform, and uncorrelated multi-linear principal component analysis with and without fast Fourier transformation data pre-processing, respectively. The effectiveness of the proposed algorithm is demonstrated and validated via the wind turbine benchmark.

针对运行可靠性和预测性维护的高要求，健康监测以及故障诊断和分类对于复杂工业系统（例如风力涡轮机能源系统）至关重要。在本研究中，针对各种故障场景下的风力涡轮机能源系统，探讨了数据驱动的故障诊断和故障分类策略。提出了一种新算法，通过整合快速傅里叶变换和不相关多线性主成分分析技术，以便在4.8兆瓦风力涡轮机基准系统中各种执行器和传感器故障场景下实现有效的三维空间可视化，用于故障诊断和分类。此外，分别通过使用有和没有快速傅里叶变换的多线性主成分分析以及有和没有快速傅里叶变换数据预处理的不相关多线性主成分分析进行了比较研究。通过风力涡轮机基准验证并证明了所提算法的有效性。