Skip to main content

Screw Life Prediction Based on Accelerometer and Compact Support Gaussian Fuzzy Neural Networks

Buy Article:

$105.00 plus tax (Refund Policy)

To evaluate accurately screw residual life in the process of machining operation, accelerometers are used to construct an on-line monitoring system, and relation between screw life and vibration signals is modeled by neural network. Two B&K 4321 three-way accelerometer and a B&K 4368 accelerometer are installed to monitor the changing trend of screw pair life. The empirical mode decomposition (EMD) and Hilbert-Huang transform (HHT) are introduced to process vibration signals. First, signals are separated into several intrinsic mode functions (IMFs) by using EMD. Then the features of each IMF can be obtained. Key features to screw life are selected according to correlation coefficient. Second, the relation between screw life and vibration features was built by Compact Support Gaussian Fuzzy Neural Networks (CSGFNN), which parameters are optimized by an adaptive learning algorithm. Finally, screw residual life could be given by proposed model. The experimental results show maximum error is 328 hours and minimum error is 49 hours; meet the need of active maintenance.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Keywords: NEURAL NETWORK; RESIDUAL LIFE; SCREW; VIBRATION SIGNAL

Document Type: Research Article

Publication date: 2011-10-01

More about this publication?
  • The growing interest and activity in the field of sensor technologies requires a forum for rapid dissemination of important results: Sensor Letters is that forum. Sensor Letters offers scientists, engineers and medical experts timely, peer-reviewed research on sensor science and technology of the highest quality. Sensor Letters publish original rapid communications, full papers and timely state-of-the-art reviews encompassing the fundamental and applied research on sensor science and technology in all fields of science, engineering, and medicine. Highest priority will be given to short communications reporting important new scientific and technological findings.
  • Editorial Board
  • Information for Authors
  • Subscribe to this Title
  • Terms & Conditions
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more