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Fault Diagnosis Technology of Heavy Truck ABS Based on Modified LM Neural Network

  • Xiao Juan YangEmail author
  • Shu Quan Xv
  • Fu Jia Liu
  • Tian Hao Zhang
Conference paper
  • 31 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1060)

Abstract

Aiming at the problem that traditional artificial fault diagnosis is inefficient and cannot meet the requirement of modern automobile intelligent development, a fault diagnosis technology for heavy truck ABS based on improved neural network is proposed in this paper. By analyzing the working principle of ABS system, this paper summarizes the common failure modes and causes of ABS. The data flow of ABS under different fault modes is collected through the real vehicle fault simulation test which is a scarce condition in current research. After pretreatment of the collected data, the BP neural network model optimized by LM algorithm is used to train the data. The model after training has high accuracy in predicting ABS-related faults, which is suitable for a wide range of applications, not only improving the efficiency and accuracy of ABS fault diagnosis, but also providing a new direction for the intellectualization of automobile fault diagnosis and maintenance.

Keywords

ABS Neural network Fault diagnosis Heavy truck 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Xiao Juan Yang
    • 1
    Email author
  • Shu Quan Xv
    • 1
  • Fu Jia Liu
    • 1
  • Tian Hao Zhang
    • 1
  1. 1.Research Institute of Highway Ministry of TransportBeijingChina

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