391 / 2022-02-01 07:04:17
On the automated failure diagnostic system for the hydraulic pressing machine
Failure diagnostic,Failure prediction,Hydraulic pressing machine,Vibration measurement
Final Paper
Zhe LI / Toyama Prefectural University
Taisei Kusano / Toyama Prefectural University
Naoyuki Takeda / Toyama Prefectural University
Osamu Terashima / Toyama Prefectural University
In recent years, owing to the effects of the global coronavirus pandemic and decline of the working-age population, production efficiency and labor productivity are being actively improved in manufacturing and production sites by the introduction of digital technologies. Given these trends, we have developed a system to determine the manufacturing statuses of machines at the early stage to aid production efficiency within a manufacturing company. To detect failures and abnormalities in hydraulic presses at the early stages, we have built and commenced operation of a failure diagnostic system using machine learning. A sensor that measures vibration acceleration is attached to the cylinder, oil pump, and pump drive motor, which are the main parts of the press machine; signals are continuously collected, and the signal for normal operation is modeled on the basis of the standard deviation, crest factor, and maximum signal values. Failures and abnormalities are also detected on the basis of the amount of temporal changes and deviations of the model. Thus, the possibility to predict the time to failure by monitoring such variations is shown.
Important Date
  • Conference Date

    Nov 01

    2022

    to

    Nov 03

    2022

  • Oct 30 2022

    Draft paper submission deadline

  • Nov 09 2022

    Registration deadline

Sponsored By
Qingdao University of Technology