Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

On fault diagnosis for high-g accelerometers via data-driven models

Wen, Jingjing, Yao, Houpu, Ji, Ze and Xia, Min 2020. On fault diagnosis for high-g accelerometers via data-driven models. IEEE Sensors Journal 10.1109/JSEN.2020.3019632

[img] PDF - Accepted Post-Print Version
Download (1MB)

Abstract

Shock test is a pivotal stage for designing and manufacturing space instruments. As the essential components in shock test systems to measure shock signals accurately, high-g accelerometers are usually exposed to hazardous shock environment and could be subjected to various damages. Owing to that these damages to the accelerometers could result in erroneous measurements which would further lead to shock test failures, accurately diagnosing the fault type of each high-g accelerometer can be vital to ensure the reliability of the shock test experiments. Additionally, in practice, an accelerometer in one malfunction form usually outputs mutable signal waveforms, so that it is difficult to empirically judge the fault type of the accelerometer based on the erroneous readings. Moreover, traditional hardware diagnosis approaches require disassembling the sensor’s package shell and manually observing the damage of the elements inner the sensor, which are less-efficient and uneconomical. Aiming at these problems, several data-driven approaches are incorporated to diagnose the fault types of high-g accelerometers in this work. Firstly, several high-g accelerometers with most frequent types of damage are collected, and a shock signal dataset is gathered by conducting shock tests on these faulty accelerometers. Then, the obtained dataset is used to train several base classifiers to identify the fault types in a supervised fashion. Lastly, a hybrid ensemble learning model is established by integrating these base classifiers with both heterogeneous and homogeneous models. Experimental results show that these data-driven methods can accurately identify the fault types of high-g accelerometers from their mutable erroneous readings.

Item Type: Article
Date Type: Published Online
Status: In Press
Schools: Engineering
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
ISSN: 1530-437X
Date of First Compliant Deposit: 28 August 2020
Date of Acceptance: 17 August 2020
Last Modified: 02 Sep 2020 08:17
URI: http://orca.cf.ac.uk/id/eprint/134555

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics