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Effective Random Forest-Based Fault Detection and Diagnosis for Wind Energy Conversion Systems

Random Forest (RF) is one of the mostly used machine learning techniques in fault detection and diagnosis of industrial systems. However, its implementation suffers from certain drawbacks when considering the correlations between variables. In addition, to perform a fault detection and diagnosis, the classical RF only uses the raw data by the direct use of measured variables. The direct raw data could yield to poor performance due to the data redundancies and noises. Thus, this paper proposes four improved RF methods to overcome the above-mentioned limitations. The developed methods aim to reduce at first the amount of the training data and select the first kernel principal components (KPCs) using different kernel principal component analysis (PCA) based dimensionality reduction schemes. Then, the retained KPCs are fed to the RF classifier for fault diagnosis purposes. Finally, the proposed techniques are applied to a wind energy conversion (WEC) system. Different case studies were investigated in order to illustrate the effectiveness and robustness of the developed techniques compared to the state-of-the-art methods. The obtained results show the low computation time and high diagnosis accuracy of the proposed approaches (an average accuracy of 91%).