MEMS Inertial Sensor Fault Diagnosis Using a CNN-Based Data-Driven Method

Tong GAO, Wei SHENG*, Mingliang ZHOU, Bin FANG, Liping ZHENG

*Corresponding author for this work

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

10 Citations (Scopus)


In this paper, we propose a novel fault diagnosis (FD) approach for micro-electromechanical systems (MEMS) inertial sensors that recognize the fault patterns of MEMS inertial sensors in an end-to-end manner. We use a convolutional neural network (CNN)-based data-driven method to classify the temperature-related sensor faults in unmanned aerial vehicles (UAVs). First, we formulate the FD problem for MEMS inertial sensors into a deep learning framework. Second, we design a multi-scale CNN which uses the raw data of MEMS inertial sensors as input and which outputs classification results indicating faults. Then we extract fault features in the temperature domain to solve the non-uniform sampling problem. Finally, we propose an improved adaptive learning rate optimization method which accelerates the loss convergence by using the Kalman filter (KF) to train the network efficiently with a small dataset. Our experimental results show that our method achieved high fault recognition accuracy and that our proposed adaptive learning rate method improved performance in terms of loss convergence and robustness on a small training batch.

Original languageEnglish
Article number2059048
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number14
Early online date18 May 2020
Publication statusPublished - 30 Dec 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2020 World Scientific Publishing Company.


  • adaptive learning rate
  • convolutional neural network
  • Fault diagnosis
  • temperature sliding window
  • UAV sensors


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