Application of Array Pressure Sensor in Roller Fault Detection
Yukun Qu, Shuran Yin, Jun Yang, Hongtao Liu, Tai Sun, Leyong Yu, Yun Hu, Dapeng Wei
Available Online March 2018.
- https://doi.org/10.2991/mecae-18.2018.9How to use a DOI?
- Array pressure sensor; Fault Diagnosis; Neural Network; Probabilistic Neural Network (PNN); Roller group.
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - https://doi.org/10.2991/mecae-18.2018.9 ID - Qu2018/03 ER -