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.
Profile Monitoring and Fault Diagnosis Via Sensor Fusion for Ultrasonic Welding
Manuscript received May 17, 2018; final manuscript received May 1, 2019; published online June 10, 2019. Assoc. Editor: Dragan Djurdjanovic.
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Guo, W. (., Jin, J. (., and Jack Hu, S. (June 10, 2019). "Profile Monitoring and Fault Diagnosis Via Sensor Fusion for Ultrasonic Welding." ASME. J. Manuf. Sci. Eng. August 2019; 141(8): 081001. https://doi.org/10.1115/1.4043731
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