Abstract
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.
Highlights
With the development of advanced technologies to increase production, modern industrial systems become more complex and expensive
3, An algorithm integrated with fast Fourier transform (FFT) and uncorrelated multi-linear principal component analysis (UMPCA) techniques is addressed for algorithms
We focus on the actuator faults and sensor faults of the wind turbines
Summary
With the development of advanced technologies to increase production, modern industrial systems become more complex and expensive. The model-based fault diagnosis approach requires a well-established model of practical processes developed by either physical principles or systems identification techniques. Machine learning techniques play an important role for data-driven fault diagnosis. Feature extractions play an important role in data-driven fault diagnosis [26,27,28,29] as well as dimensionality reduction for the samples/datasets. The PCA, as an unsupervised learning technique, is a statistical procedure that utilizes an orthogonal transformation to convert a set of correlated variables into linearly uncorrelated variables, namely principal components [35]. The conventional PCA technique may become invalid for fault diagnosis and fault classification in wind turbine systems subjected to multiple faults. There is a strong motivation to develop advanced PCA-based fault diagnosis and classification techniques for wind. A wind system is a complex industrial system, and the operation conditiontoisaharsh
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