Interval-Valued Reduced RNN for Fault Detection and Diagnosis for Wind Energy Conversion Systems

Recurrent neural network (RNN) is one of the most used deep learning techniques in fault detection and diagnosis (FDD) of industrial systems. However, its implementation suffers from some limitations presented in the hard training step and the high time complexity. Besides, most used RNN-based FDD techniques do not deal with system uncertainties. Therefore, this paper proposes enhanced RNN techniques that detect and classify faults in wind energy conversion (WEC) systems. First, we develop a reduced RNN in order to simplify the model in terms of training and complexity time as well. Reduced RNN is based on Hierarchical K-means clustering to treat the correlations between samples and extract a reduced number of observations from the training data matrix. Second, two reduced RNN-based interval-valued-data techniques are proposed to distinguish between the different WEC system operating modes. The proposed techniques for interval-valued data are able to improve both fault diagnosis robustness and susceptibility while maintaining a satisfactory and stable performance over long periods of process operation. The presented results confirm the high feasibility and effectiveness of the proposed FDD techniques (an accuracy greater than 98% for all the proposed methods).

Automated summary

Recurrent neural network (RNN) is widely used in fault detection and diagnosis (FDD) of industrial systems. This paper proposes enhanced RNN techniques for fault detection and classification in wind energy conversion systems. The techniques include a reduced RNN model and interval-valued data techniques, which improve fault diagnosis robustness and susceptibility.