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Using Ensemble Classifiers to Detect Incipient Anomalies
Incipient anomalies present milder symptoms compared to severe ones, and are more difficult to detect and diagnose due to their close resemblance to normal operating conditions.
Generalizing Fault Detection Against Domain Shifts Using Stratification-Aware Cross-Validation
Incipient anomalies present milder symptoms compared to severe ones, and are more difficult to detect and diagnose due to their close resemblance to normal operating conditions.
Exploiting Uncertainties from Ensemble Learners to Improve Decision-Making in Healthcare AI
Ensemble learning is widely applied in Machine Learning (ML) to improve model performance and to mitigate decision risks.
Are Ensemble Classifiers Powerful Enough for the Detection and Diagnosis of Intermediate-Severity Faults?
Intermediate-Severity (IS) faults present milder symptoms compared to severe faults, and are more difficult to detect and diagnose due to their close resemblance to normal operating conditions.
Augmenting Monte Carlo Dropout Classification Models with Unsupervised Learning Tasks for Detecting and Diagnosing Out-of-Distribution Faults
The Monte Carlo dropout method has proved to be a scalable and easy-to-use approach for estimating the uncertainty of deep neural network predictions.
An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing
We show that the encoder-decoder model is able to identify the injected anomalies in a modern manufacturing process in an unsupervised fashion.
