Fault diagnosis is important to the industrial process. This paper proposes an orthogonal incremental extreme learning machine based on driving amount (DAOI-ELM) for recognizing the faults of the Tennessee-Eastman process (TEP). The basic idea of DAOI-ELM is to incorporate the Gram–Schmidt orthogonalization method and driving amount into an incremental extreme learning machine (I-ELM). The case study for the 2-D nonlinear function and regression problems from the UCI dataset results show that DAOI-ELM can obtain better generalization ability and a more compact structure of ELM than I-ELM, convex I-ELM (CI-ELM), orthogonal I-ELM (OI-ELM), and bidirectional ELM. The experimental training and testing data are derived from the simulations of TEP. The performance of DAOI-ELM is evaluated and compared with that of the back propagation neural network, support vector machine, I-ELM, CI-ELM, and OI-ELM. The simulation results show that DAOI-ELM diagnoses the TEP faults better than other methods.

中文翻译：

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基于驱动量的正交增量极限学习机对田纳西-伊斯曼过程的故障诊断

故障诊断对工业过程很重要。本文提出了一种基于驱动量的正交增量式极限学习机（DAOI-ELM），用于识别田纳西-伊士曼过程（TEP）的故障。DAOI-ELM的基本思想是将Gram-Schmidt正交化方法和驱动量合并到增量式极限学习机（I-ELM）中。根据UCI数据集的二维非线性函数和回归问题的案例研究表明，与I-ELM，凸I-ELM（CI-ELM）相比，DAOI-ELM具有更好的泛化能力和更紧凑的ELM结构，正交I-ELM（OI-ELM）和双向ELM。实验训练和测试数据是从TEP的模拟中得出的。评估DAOI-ELM的性能，并将其与反向传播神经网络的性能进行比较，支持向量机，I-ELM，CI-ELM和OI-ELM。仿真结果表明，DAOI-ELM能够比其他方法更好地诊断TEP故障。