Fault detection based on Principal Component Analysis in the context of Missing Data | IEEE Conference Publication | IEEE Xplore

Fault detection based on Principal Component Analysis in the context of Missing Data


Abstract:

Improving fault detection and diagnosis (FDD) in systems with incomplete features is a prevalent challenge in industrial applications. In this context, two distinct appro...Show More
Notes: This article was mistakenly omitted from the original submission to IEEE Xplore. It is now included as part of the conference record.

Abstract:

Improving fault detection and diagnosis (FDD) in systems with incomplete features is a prevalent challenge in industrial applications. In this context, two distinct approaches (k-means clustering and k-nearest neighbor imputation KNN) for addressing Missing Data (MD) were presented and compared. The evaluation was conducted with introducing of MD, ranging from a 10% to 50%. The proposals aim to enhance the monitoring and diagnosis of the system through the utilization of principal component analysis (PCA) for feature extraction an fault detection.
Notes: This article was mistakenly omitted from the original submission to IEEE Xplore. It is now included as part of the conference record.
Date of Conference: 15-17 May 2024
Date Added to IEEE Xplore: 05 July 2024
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Conference Location: Paris, France

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