Fault Diagnosis of Fuel System Based on Improved Extreme Learning Machine

  • Hairui Wang
  • Wanting JingEmail author
  • Ya Li
  • Hongwei YangEmail author


In this paper, extreme learning machine (ELM) method is used to classify the faults of fuel system. Although the learning speed of ELM is fast, its classification accuracy and generalization ability need to be improved. Bat Algorithm has a strong ability of global optimization. In order to make up for the deficiency of the ELM, this paper proposes a fault diagnosis model based on an improved bat algorithm to optimize the ELM. The experimental results show that the improved bat algorithm greatly improves the classification accuracy and generalization ability of the ELM, and verifies the validity of the proposed model.


Extreme learning machine Bat algorithm Fuel system Fault diagnosis 



This work was supported by the National Natural Science Foundation of China (Grant Nos. 61263023 and 61863016).


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© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Faculty of Information Engineering and AutomationKunming University of Science and TechnologyKunmingChina
  2. 2.Yunnan Provincial Education DepartmentKunmingChina

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