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 multi-stream 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.
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ASME 2016 11th International Manufacturing Science and Engineering Conference
June 27–July 1, 2016
Blacksburg, Virginia, USA
Conference Sponsors:
- Manufacturing Engineering Division
ISBN:
978-0-7918-4990-3
PROCEEDINGS PAPER
Profile Monitoring and Fault Diagnosis via Sensor Fusion for Ultrasonic Welding Available to Purchase
Weihong Guo,
Weihong Guo
Rutgers, The State University of New Jersey, Piscataway, NJ
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Jionghua (Judy) Jin,
Jionghua (Judy) Jin
University of Michigan, Ann Arbor, MI
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S. Jack Hu
S. Jack Hu
University of Michigan, Ann Arbor, MI
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Weihong Guo
Rutgers, The State University of New Jersey, Piscataway, NJ
Jionghua (Judy) Jin
University of Michigan, Ann Arbor, MI
S. Jack Hu
University of Michigan, Ann Arbor, MI
Paper No:
MSEC2016-8750, V002T04A028; 10 pages
Published Online:
September 27, 2016
Citation
Guo, W, Jin, J(, & Hu, SJ. "Profile Monitoring and Fault Diagnosis via Sensor Fusion for Ultrasonic Welding." Proceedings of the ASME 2016 11th International Manufacturing Science and Engineering Conference. Volume 2: Materials; Biomanufacturing; Properties, Applications and Systems; Sustainable Manufacturing. Blacksburg, Virginia, USA. June 27–July 1, 2016. V002T04A028. ASME. https://doi.org/10.1115/MSEC2016-8750
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