@inproceedings{1022080be8aa456cad4f5cd74fecd3a3,
title = "Profile monitoring and fault diagnosis via sensor fusion for ultrasonic welding",
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.",
keywords = "Fault diagnosis, Profile monitoring, Sensor fusion, Tensor decomposition",
author = "Weihong Guo and Jionghua Jin and Hu, \{S. Jack\}",
note = "Publisher Copyright: Copyright {\textcopyright} 2016 by ASME.; ASME 2016 11th International Manufacturing Science and Engineering Conference, MSEC 2016 ; Conference date: 27-06-2016 Through 01-07-2016",
year = "2016",
doi = "10.1115/MSEC2016-8750",
language = "English (US)",
series = "ASME 2016 11th International Manufacturing Science and Engineering Conference, MSEC 2016",
publisher = "American Society of Mechanical Engineers",
booktitle = "Materials; Biomanufacturing; Properties, Applications and Systems; Sustainable Manufacturing",
}