Profile Monitoring and Fault Diagnosis Via Sensor Fusion for Ultrasonic Welding

Weihong Grace Guo, Jionghua Judy Jin, S. Jack Hu

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Sensor signals acquired during the manufacturing process contain rich information that can be used to facilitate effective monitoring of operational quality, early detection of system anomalies, and quick diagnosis of fault root causes. This paper develops a method for effective monitoring and diagnosis of multisensor heterogeneous profile data based on multilinear discriminant analysis. The proposed method operates directly on the multistream profiles and then extracts uncorrelated discriminative features through tensor-to-vector projection, and thus, preserving the interrelationship of different sensors. The extracted features are then fed into classifiers to detect faulty operations and recognize fault types. The developed method is demonstrated with both simulated and real data from ultrasonic metal welding.

Original languageEnglish (US)
Article number081001
JournalJournal of Manufacturing Science and Engineering, Transactions of the ASME
Volume141
Issue number8
DOIs
StatePublished - Aug 1 2019

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Mechanical Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

Keywords

  • fault diagnosis
  • profile monitoring
  • sensor fusion
  • tensor decomposition

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