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

You might also read

Related Articles

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

Sort by
Same author

Characterization of Powder Bed Fusion-Laser Beam Ti6Al4V Samples in the As-Built and Stress-Relief States.

Materials (Basel, Switzerland)·2026
Same author

Design of a Multi-Mode Hybrid Micro-Gripper for Surface Mount Technology Component Assembly.

Micromachines·2023
Same author

On the Use of Self-Organizing Map for Text Clustering in Engineering Change Process Analysis: A Case Study.

Computational intelligence and neuroscience·2017
See all related articles

Related Experiment Video

Updated: Nov 25, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

11.0K

Fault Diagnosis by Multisensor Data: A Data-Driven Approach Based on Spectral Clustering and Pairwise Constraints.

Massimo Pacella1, Gabriele Papadia1

  • 1Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy.

Sensors (Basel, Switzerland)
|December 16, 2020
PubMed
Summary

This study enhances spectral clustering for multisensor data fault diagnosis. The improved method uses pairwise constraints for efficient identification of fault scenarios in high-dimensional spaces.

Keywords:
PCAfault detectionfuel-injection systemsemi-supervised classificationspectral clustering

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.0K
ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
07:11

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

Published on: August 19, 2021

2.8K

Related Experiment Videos

Last Updated: Nov 25, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

11.0K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.0K
ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
07:11

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

Published on: August 19, 2021

2.8K

Area of Science:

  • Signal Processing
  • Machine Learning
  • Manufacturing Systems

Background:

  • Multisensor data in high-dimensional spaces presents challenges for traditional clustering.
  • Spectral clustering, based on affinity matrices and Laplacian graphs, is effective for grouping data.
  • Fault diagnosis in manufacturing relies heavily on clustering techniques.

Purpose of the Study:

  • To present an enhanced spectral clustering approach for multisensor data.
  • To improve the efficiency and accuracy of fault diagnosis in high-dimensional spaces.
  • To incorporate pairwise constraints into spectral clustering for better performance.

Main Methods:

  • Utilizing spectral clustering algorithms for data grouping.
  • Constructing an affinity matrix to represent pairwise data point similarity.
  • Applying spectral decomposition of the Laplacian graph.
  • Augmenting the spectral clustering approach with pairwise constraints.

Main Results:

  • The enhanced spectral clustering approach demonstrates efficient identification of fault scenarios.
  • The method is validated through a case study on a diesel injection control system.
  • Improved fault detection capabilities were observed.

Conclusions:

  • The proposed enhanced spectral clustering method is effective for fault diagnosis using multisensor data.
  • Pairwise constraints significantly improve the performance of spectral clustering in this context.
  • The approach offers a valuable tool for fault detection in manufacturing applications.