Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Bearings: Problem Solving01:24

Bearings: Problem Solving

Understanding the calculations and concepts related to double-collar bearings is essential for engineers and designers to optimize the performance of these components in various applications. By analyzing the bearing under different conditions, one can ensure that it can withstand the forces and moments experienced during operation. This knowledge enables better decision-making when designing and selecting bearings for specific purposes and configurations. Consider a double-collar bearing with...
Journal Bearings01:23

Journal Bearings

Journal bearings are mechanical components that support and provide lateral stability to rotating shafts and axles. They are crucial in reducing friction, wear, and vibration in machinery such as engines, turbines, and pumps. The principle behind journal bearings is forming a thin lubricant film between the bearing surface and the rotating shaft, which minimizes direct contact and reduces frictional forces.
To better understand the concept of journal bearings, consider a rope winch with dry or...
Pivot Bearings01:23

Pivot Bearings

In mechanical systems, bearings are crucial in facilitating relative motion between two components while minimizing friction and wear. They help distribute various loads (radial, axial or a combination of both loads) across machinery parts, ensuring smooth and efficient operation.
A pivot bearing is a specialized type of bearing designed to support axial loads on a rotating shaft. The bearing surface, or the pivot, is positioned at the end of a shaft to support the axial thrust. The pivot may...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Metabolic reprogramming landscape orchestrating chlamydospore formation in <i>Volvariella volvacea</i>.

Frontiers in microbiology·2026
Same author

Dynamic shifts in cutaneous bacterial and fungal communities throughout human aging: a pilot study.

BMC microbiology·2026
Same author

Comparison of functional disability in older surgical patients with and without probable cognitive impairment: A longitudinal prospective cohort study.

Journal of anesthesia and translational medicine·2026
Same author

Robotic-Assisted Lingual Mucosa Graft Ureteroplasty for Long Proximal Ureteral Stricture Following Hematopoietic Stem Cell Transplantation: A Case Report and Literature Review.

Urologia internationalis·2026
Same author

Establishment of epidemiological cutoff values against <i>Fonsecaea monophora</i>, an agent of chromoblastomycosis and cerebral phaeohyphomycosis.

Journal of clinical microbiology·2026
Same author

MRSA transmission in hospitals across Alberta, Canada: a comparative study combining unidentified colonized cases upon admission.

BMC infectious diseases·2026

Related Experiment Video

Updated: May 28, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Data mining based full ceramic bearing fault diagnostic system using AE sensors.

David He1, Ruoyu Li, Junda Zhu

  • 1Intelligent Systems Modeling & Development Laboratory, Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA. davidhe@uic.edu

IEEE Transactions on Neural Networks
|October 13, 2011
PubMed
Summary

This study introduces a novel diagnostic system for full ceramic bearings using acoustic emission (AE) signals. It enables effective fault detection in ceramic bearings, crucial for future oil-free engines.

More Related Videos

Using Micro-Electro-Mechanical Systems (MEMS) to Develop Diagnostic Tools
16:05

Using Micro-Electro-Mechanical Systems (MEMS) to Develop Diagnostic Tools

Published on: October 1, 2007

Related Experiment Videos

Last Updated: May 28, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Using Micro-Electro-Mechanical Systems (MEMS) to Develop Diagnostic Tools
16:05

Using Micro-Electro-Mechanical Systems (MEMS) to Develop Diagnostic Tools

Published on: October 1, 2007

Area of Science:

  • Mechanical Engineering
  • Materials Science
  • Condition Monitoring

Background:

  • Full ceramic bearings are key components for future oil-free engines.
  • No prior research exists on fault diagnostics for full ceramic bearings using acoustic emission (AE) sensors.
  • Existing signal processing methods for steel bearings are not directly applicable to ceramic bearings.

Purpose of the Study:

  • To develop a data mining-based fault diagnostic system for full ceramic bearings.
  • To establish effective methods for extracting AE fault characteristic features for ceramic bearings.
  • To create a reliable fault classification system for ceramic bearings.

Main Methods:

  • Utilized a novel signal processing method based on the Hilbert-Huang Transform (HHT) to extract AE fault features.
  • Computed Condition Indicators (CIs) from the extracted AE features.
  • Developed a fault classifier using a k-nearest neighbor (k-NN) algorithm with the computed CIs.
  • Conducted seeded fault tests on full ceramic bearings (outer race, inner race, balls, cage) and collected AE data.

Main Results:

  • Successfully extracted AE fault features and computed CIs for full ceramic bearings.
  • Developed and validated a data mining-based fault diagnostic system.
  • Demonstrated the effectiveness of the system using real seeded fault test data.

Conclusions:

  • The presented data mining-based system effectively diagnoses faults in full ceramic bearings using AE signals.
  • The developed signal processing and classification methods are suitable for ceramic bearing condition monitoring.
  • This research lays the groundwork for implementing AE-based diagnostics in full ceramic, oil-free engine applications.