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首页> 外文期刊>IEEE transactions on industrial informatics >Restricted-Boltzmann-Based Extreme Learning Machine for Gas Path Fault Diagnosis of Turbofan Engine
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Restricted-Boltzmann-Based Extreme Learning Machine for Gas Path Fault Diagnosis of Turbofan Engine

机译：基于限制的Boltzmann的气体路径故障诊断涡轮机发动机的基于极端学习机

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摘要

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.
机译：极端学习机（ELM）拥有较低的计算工作和单隐层结构的简单拓扑的优势。然而，普通榆树的性能对输入重量，偏差和隐藏神经元数敏感;并且前两个是随机产生的。本文开发了一个限制的Boltzmann策略，与Moore-PenRose广义相结合，以学习输入和输出层中的拓扑参数。一种基于受限制的Boltzmann榆树的新型极端学习模型，构建了一种特征映射并递归调谐输入神经元和隐藏神经元之间的重量。本文的贡献是提供一个简单的ELM拓扑网络，以处理更好的准确性和稳定性的优点。拟议的方法是在加州大学欧文（UCI）基准数据集进行分类问题的评估，然后扩展到涡轮机发动机的气体路径故障诊断。实验结果证实了普通榆树的优越性。

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