Method for fault diagnosis of temperature-related mems inertial sensors by combining hilbert– huang transform and deep learning

Tong GAO, Wei SHENG*, Mingliang ZHOU, Bin FANG, Futing LUO, Jiajun LI

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

13 Citations (Scopus)

Abstract

In this paper, we propose a novel method for fault diagnosis in micro-electromechanical system (MEMS) inertial sensors using a bidirectional long short-term memory (BLSTM)-based Hilbert–Huang transform (HHT) and a convolutional neural network (CNN). First, the method for fault diagnosis of inertial sensors is formulated into an HHT-based deep learning problem. Second, we present a new BLSTM-based empirical mode decomposition (EMD) method for converting one-dimensional inertial data into two-dimensional Hilbert spectra. Finally, a CNN is used to perform fault classification tasks that use time–frequency HHT spectrums as input. According to our experimental results, significantly improved performance can be achieved, on average, for the proposed BLSTM-based EMD algorithm in terms of EMD computational efficiency compared with state-of-the-art algorithms. In addition, the proposed fault diagnosis method achieves high accuracy in fault classification.

Original languageEnglish
Article number5633
Pages (from-to)1-27
Number of pages27
JournalSensors (Switzerland)
Volume20
Issue number19
DOIs
Publication statusPublished - 1 Oct 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • BLSTM
  • CNN
  • Fault diagnosis
  • Hilbert-Huang transform

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