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Keywords: principal component analysisClose
Proc. ASME. MSEC2021, Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T09A004, June 21–25, 2021
Paper No: MSEC2021-63465
... for unsupervised feature extraction. A multiclass extension for semi-supervised anomaly diagnosis is proposed that utilizes principal component analysis as the basis for anomaly scoring, and the proposed approach intersects the results of targeted one-against-all phases on partially labeled sets to classify faults...
Proc. ASME. MSEC2020, Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability, V002T07A029, September 3, 2020
Paper No: MSEC2020-8360
... classifiers such as artificial neural networks, support vector machines, and random forests are implemented and compared for handling multi-fault diagnosis using programmable logic controller signal data. For unsupervised learning, classifiers based on principal component analysis utilizing major and minor...
Proc. ASME. MSEC2011, ASME 2011 International Manufacturing Science and Engineering Conference, Volume 2, 289-296, June 13–17, 2011
Paper No: MSEC2011-50211
... collected from the controller is used both for labeling datasets into different operating conditions and for analysis. Principal component analysis (PCA) is adopted to identify critical sensors that can provide useful information. Self-organizing map (SOM)-based anomaly detection and diagnosis methods...