A fault diagnosis framework based on extreme learning machine (ELM) and AdaBoost.SAMME is proposed in a nuclear power plant (NPP) in this paper. After briefly describing the principles of ELM and AdaBoost.SAMME algorithm, the fault diagnosis framework sets ELM algorithm as the weak classifier and then integrates several weak classifiers into a strong one using the AdaBoost.SAMME algorithm. Furthermore, some experiments are put forward for the setting of two algorithms. The results of simulation experiments on the HPR1000 simulator show that the combined method has higher precision and faster speed by improving the performance of weak classifiers compared to the BP neural network and verify the feasibility and validity of the ensemble learning method for fault diagnosis. Meanwhile, the results also indicate that the proposed method can meet the requirements of a real-time diagnosis of the nuclear power plant.

中文翻译：

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基于ELM-AdaBoost.SAMME的核电站故障诊断研究

本文提出了一种基于极限学习机（ELM）和AdaBoost.SAMME的故障诊断框架。在简要描述了ELM和AdaBoost.SAMME算法的原理之后，故障诊断框架将ELM算法设置为弱分类器，然后使用AdaBoost.SAMME算法将多个弱分类器集成为一个强分类器。此外，针对两种算法的设置提出了一些实验。在HPR1000模拟器上的仿真实验结果表明，与BP神经网络相比，该组合方法通过改善弱分类器的性能，具有更高的精度和更快的速度，并验证了集成学习方法用于故障诊断的可行性和有效性。与此同时，