Application of Array Pressure Sensor in Roller Fault Detection

DOI

10.2991/mecae-18.2018.9How to use a DOI?

Keywords

Array pressure sensor; Fault Diagnosis; Neural Network; Probabilistic Neural Network (PNN); Roller group.

Abstract

In order to diagnose the fault of roller group, we have designed an array pressure sensor and a scan circuit with weak crosstalk of the column signal for collecting pressure distribution data. Furthermore, these data are processed via both the probabilistic neural network (PNN) fault diagnosis model and the back propagation (BP) neural network fault diagnosis model. Experimental results reveal that the PNN fault diagnosis model has more accuracy and shorter period of constructing and training model than BP fault diagnosis model. Our research opens up a new way for roller fault detection from via indirect information (such as sound, vibration and other signals) to via direct digitization pressure image. This method reduces the interference of environmental noise on fault detection. It is more accurate, intuitive, and potential for the applications in all kinds of mechanical fault detection.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yukun Qu
AU  - Shuran Yin
AU  - Jun Yang
AU  - Hongtao Liu
AU  - Tai Sun
AU  - Leyong Yu
AU  - Yun Hu
AU  - Dapeng Wei
PY  - 2018/03
DA  - 2018/03
TI  - Application of Array Pressure Sensor in Roller Fault Detection
BT  - Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)
PB  - Atlantis Press
SP  - 51
EP  - 58
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecae-18.2018.9
DO  - 10.2991/mecae-18.2018.9
ID  - Qu2018/03
ER  -