sensor fault diagnosis for aero engine based on online sequential extreme learning machine with memory principle

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ID: 242472
2017
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Abstract
The on-board sensor fault detection and isolation (FDI) system is essential to guarantee the reliability and safety of an aero engine. In this paper, a novel online sequential extreme learning machine with memory principle (MOS-ELM) is proposed for detecting, isolating, and reconstructing the fault sensor signal of aero engines. In many practical online applications, the sequentially coming data chunk usually possesses a characteristic of timeliness, and the overdue training data may mislead the subsequent learning process. The proposed MOS-ELM can improve the training process by introducing the concept of memory principle into the online sequential extreme learning machine (OS-ELM) to tackle the timeliness of the data chunk. Simulations on some time series problems and some benchmark databases show that MOS-ELM performs better in generalization performance, stability, and prediction accuracy than OS-ELM. The experiment results of the MOS-ELM-based sensor fault diagnosis system also verify the excellent generalization performance of MOS-ELM and indicate the effectiveness and feasibility of the developed diagnosis system.
Reference Key
lu2017energiessensor Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Junjie Lu;Jinquan Huang;Feng Lu
Journal acs combinatorial science
Year 2017
DOI
10.3390/en10010039
URL
http://www.mdpi.com/1996-1073/10/1/39
Keywords
online learning extreme learning machine (elm)

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