Application of Mallat wavelet fast transforms and IDRNN in real-time fault detection and identification for satellites

Zhaohui Cen, Jiao Long Wei, Rui Jiang

Research output: Contribution to journalArticle

Abstract

A real-time fault detection and identification (FDI) scheme of time-variant signals for a complex system was studied. A sliding-window Mallat wavelet fast transform was first introduced to avoid depending on the signals in all periods for the classical wavelet transform, and the computing effect was improved, which makes sense that the real-time fault detection is effective. Secondly, aimed at the problem that it is difficult to identify the fault by using time-variant signals, an improved dynamic recurrent neural network (IDRNN) was utilized to identify the fault intelligently after detecting the fault. Finally, the scheme, including fault detection based on the sliding-window Mallat wavelet and fault isolation based on the optimized IDRNN, was applied into a satellite attitude control simulation platform to verify the online diagnosis result. Experimental results show that the sliding-window Mallat wavelet fast transform is consistent with the classical wavelet transform in real-time scenarios, IDRNN has a better generalization ability for identifying time-variant signals, and the scheme including the sliding-window Mallat wavelet and IDRNN can implement detecting the faults and classifying the multiple faults based on real-time monitoring signals for the complex system.

Original languageEnglish
Pages (from-to)90-95
Number of pages6
JournalBeijing Keji Daxue Xuebao/Journal of University of Science and Technology Beijing
Volume34
Issue number1
Publication statusPublished - 1 Jan 2012
Externally publishedYes

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Recurrent neural networks
Fault detection
Satellites
Wavelet transforms
Large scale systems
Attitude control
Monitoring

Keywords

  • Attitude control
  • Fault diagnosis
  • Neural networks
  • Satellites
  • Wavelet transforms

ASJC Scopus subject areas

  • Engineering(all)

Cite this

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title = "Application of Mallat wavelet fast transforms and IDRNN in real-time fault detection and identification for satellites",
abstract = "A real-time fault detection and identification (FDI) scheme of time-variant signals for a complex system was studied. A sliding-window Mallat wavelet fast transform was first introduced to avoid depending on the signals in all periods for the classical wavelet transform, and the computing effect was improved, which makes sense that the real-time fault detection is effective. Secondly, aimed at the problem that it is difficult to identify the fault by using time-variant signals, an improved dynamic recurrent neural network (IDRNN) was utilized to identify the fault intelligently after detecting the fault. Finally, the scheme, including fault detection based on the sliding-window Mallat wavelet and fault isolation based on the optimized IDRNN, was applied into a satellite attitude control simulation platform to verify the online diagnosis result. Experimental results show that the sliding-window Mallat wavelet fast transform is consistent with the classical wavelet transform in real-time scenarios, IDRNN has a better generalization ability for identifying time-variant signals, and the scheme including the sliding-window Mallat wavelet and IDRNN can implement detecting the faults and classifying the multiple faults based on real-time monitoring signals for the complex system.",
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N2 - A real-time fault detection and identification (FDI) scheme of time-variant signals for a complex system was studied. A sliding-window Mallat wavelet fast transform was first introduced to avoid depending on the signals in all periods for the classical wavelet transform, and the computing effect was improved, which makes sense that the real-time fault detection is effective. Secondly, aimed at the problem that it is difficult to identify the fault by using time-variant signals, an improved dynamic recurrent neural network (IDRNN) was utilized to identify the fault intelligently after detecting the fault. Finally, the scheme, including fault detection based on the sliding-window Mallat wavelet and fault isolation based on the optimized IDRNN, was applied into a satellite attitude control simulation platform to verify the online diagnosis result. Experimental results show that the sliding-window Mallat wavelet fast transform is consistent with the classical wavelet transform in real-time scenarios, IDRNN has a better generalization ability for identifying time-variant signals, and the scheme including the sliding-window Mallat wavelet and IDRNN can implement detecting the faults and classifying the multiple faults based on real-time monitoring signals for the complex system.

AB - A real-time fault detection and identification (FDI) scheme of time-variant signals for a complex system was studied. A sliding-window Mallat wavelet fast transform was first introduced to avoid depending on the signals in all periods for the classical wavelet transform, and the computing effect was improved, which makes sense that the real-time fault detection is effective. Secondly, aimed at the problem that it is difficult to identify the fault by using time-variant signals, an improved dynamic recurrent neural network (IDRNN) was utilized to identify the fault intelligently after detecting the fault. Finally, the scheme, including fault detection based on the sliding-window Mallat wavelet and fault isolation based on the optimized IDRNN, was applied into a satellite attitude control simulation platform to verify the online diagnosis result. Experimental results show that the sliding-window Mallat wavelet fast transform is consistent with the classical wavelet transform in real-time scenarios, IDRNN has a better generalization ability for identifying time-variant signals, and the scheme including the sliding-window Mallat wavelet and IDRNN can implement detecting the faults and classifying the multiple faults based on real-time monitoring signals for the complex system.

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