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Restricted-Boltzmann-Based Extreme Learning Machine for Gas Path Fault Diagnosis of Turbofan Engine

Extreme learning machine (ELM) owns the advantages of less computational efforts and simple topology with single-hidden layer structure. However, the performance of plain ELM is sensitive to the input weights, bias, and the number of hidden neurons; and the former two are randomly generated. This paper develops a restricted Boltzmann strategy combined with Moore–Penrose generalized inverse to learn topological parameters in both input and output layers. A novel extreme learning model based on the restricted Boltzmann ELM, constructs a feature mapping and recursively tune the weights between input neurons and hidden neurons. The contribution of this paper is to provide a simple ELM topological network to handle low dimensionality problem with the merit of better accuracy and stability. The proposed methodology is evaluated on University of California Irvine (UCI) benchmark datasets for classification issue, and then extended to gas path fault diagnosis for a turbofan engine. The experimental results confirm the superiority to plain ELM.