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Related Experiment Video

Updated: Sep 27, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

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Published on: October 28, 2022

Anomaly Detection with Feature Extraction Based on Machine Learning Using Hydraulic System IoT Sensor Data.

Doyun Kim¹, Tae-Young Heo¹

¹Department of Information & Statistics, Chungbuk National University, Cheongju 28644, Korea.

Sensors (Basel, Switzerland)

|April 12, 2022

Summary

This summary is machine-generated.

This study uses machine learning on Internet of Things (IoT) sensor data to detect hydraulic system faults. It accurately identifies component issues, improving industrial maintenance and stability.

Failed At:

2026-06-19T13:39:29.617684+00:00

Keywords:

Boruta algorithm IoT sensor anomaly detection condition monitoring hydraulic components true negative rate true positive rate

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