Profile monitoring and fault diagnosis via sensor fusion for ultrasonic welding

Weihong Guo, Jionghua Jin, S. Jack Hu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 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, quick diagnosis of fault root causes, and intelligent system design and control. This paper develops a method for effective monitoring and diagnosis of multi-sensor 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)
Title of host publicationMaterials; Biomanufacturing; Properties, Applications and Systems; Sustainable Manufacturing
PublisherAmerican Society of Mechanical Engineers
ISBN (Electronic)9780791849903
DOIs
StatePublished - 2016
EventASME 2016 11th International Manufacturing Science and Engineering Conference, MSEC 2016 - Blacksburg, United States
Duration: Jun 27 2016Jul 1 2016

Publication series

NameASME 2016 11th International Manufacturing Science and Engineering Conference, MSEC 2016
Volume2

Other

OtherASME 2016 11th International Manufacturing Science and Engineering Conference, MSEC 2016
Country/TerritoryUnited States
CityBlacksburg
Period6/27/167/1/16

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

Keywords

  • Fault diagnosis
  • Profile monitoring
  • Sensor fusion
  • Tensor decomposition

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