<p>Machine learning and other data-driven methods have developed at a prolific rate for industrial applications due to the advent of industrial big data. However, industrial datasets may not be especially well-suited to supervised learning approaches that require extensive domain knowledge in the complete and accurate labeling of datasets. To address these challenges, a semi-supervised learning approach is proposed that makes use of partially labeled subsets. The proposed methodology is applied to high-dimensional in-process measurement data, utilizing a convolutional autoencoder for unsupervised feature extraction. Semi-supervised anomaly detection algorithms such as those based on principal component analysis, one-class support vector machines, and local outlier factor are reviewed, which typically train on a normal set to identify unspecified anomalies. Then, a multiclass extension for semi-supervised anomaly diagnosis is proposed based on principal component analysis, intersecting the results of targeted one-against-all phases on partially labeled sets to classify faults. Experiments in a case study on semiconductor manufacturing measurement data are performed to explore the relationship between latent features extracted and anomaly detection performance. The application of the proposed algorithm results in a true positive detection rate of at least 92% with false positive rate under 9% for both local and global anomaly types, as well as the identification of positive samples undetected by human experts. These results are promising for the application of the proposed semi-supervised methodology in real industrial settings.</p>

A Semi-Supervised Multiclass Anomaly Detection Approach for Partially Labeled In-Process Measurement Data

Paper Proceedings: https://asmedigitalcollection.asme.org/MSEC/MSEC2021/volume/85062

2021 International Manufacturing Science and Engineering Conference (MSEC2021)

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