A New Dual-Mass MEMS Gyroscope Fault Diagnosis Platform

Rang Cui; Tiancheng Ma; Wenjie Zhang; Min Zhang; Longkang Chang; Ziyuan Wang; Jingzehua Xu; Wei Wei; Huiliang Cao

doi:10.3390/mi14061177

A New Dual-Mass MEMS Gyroscope Fault Diagnosis Platform

Micromachines (Basel). 2023 May 31;14(6):1177. doi: 10.3390/mi14061177.

Authors

Rang Cui¹, Tiancheng Ma², Wenjie Zhang², Min Zhang³, Longkang Chang⁴, Ziyuan Wang⁵, Jingzehua Xu², Wei Wei², Huiliang Cao¹

Affiliations

¹ Key Laboratory of Instrumentation Science & Dynamic Measurement, Ministry of Education, North University of China, Taiyuan 030051, China.
² Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
³ School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science & Technology University, Beijing 100192, China.
⁴ College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China.
⁵ Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.

Abstract

MEMS gyroscopes are one of the core components of inertial navigation systems. The maintenance of high reliability is critical for ensuring the stable operation of the gyroscope. Considering the production cost of gyroscopes and the inconvenience of obtaining a fault dataset, in this study, a self-feedback development framework is proposed, in which a dualmass MEMS gyroscope fault diagnosis platform is designed based on MATLAB/Simulink simulation, data feature extraction, and classification prediction algorithm and real data feedback verification. The platform integrates the dualmass MEMS gyroscope Simulink structure model and the measurement and control system, and reserves various algorithm interfaces for users to independently program, which can effectively identify and classify seven kinds of signals of the gyroscope: normal, bias, blocking, drift, multiplicity, cycle and internal fault. After feature extraction, six algorithms, ELM, SVM, KNN, NB, NN, and DTA, were respectively used for classification prediction. The ELM and SVM algorithms had the best effect, and the accuracy of the test set was up to 92.86%. Finally, the ELM algorithm is used to verify the actual drift fault dataset, and all of them are successfully identified.

Keywords: MEMS gyroscope; fault diagnosis platform; feature extraction.

Grants and funding

This work is supported by the National Key Research and Development Program of China (No. 2022YFB3205000), the National Natural Science Foundation of China, the NSAF (Grant No. U2230206), the Technology Field Fund of Basic Strengthening Plan of China (2020JCJQJJ409, 2021-JCJQ-JJ-0315), and the Pre-Research Field Foundation of Equipment Development Department of China (No. 61405170104 and No. 80917010501). The research is also supported by the Fundamental Research Program of Shanxi Province (20210302123020 and 20210302123062), the Shanxi Province Key Laboratory of Quantum Sensing and Precision Measurement (201905D121001), the Key Research and Development (R&D) Projects of Shanxi Province (202003D111004), the Beijing Key Laboratory of High Dynamic Navigation Technology Open Founding (HDN2021102), and the Fund for Shanxi “1331Project” Key Subjects Construction.