Abstract:The efficacy of machine learning (ML) algorithms for turbomachinery condition monitoring can be compromised by the lack of robust historical data for training. While unsupervised or deep learning (DL) algorithms may be used when sufficient volumes of ‘labeled’ data are unavailable, they offer limited insights into detected anomalies or outliers. Additionally, the inherent dependency on data volume and variety delays the deployment of these algorithms, making an ML-only approach unsuitable for situations such a… Show more
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