Comparative Study on Semi-supervised Learning Applied for Anomaly
Detection in Hydraulic Condition Monitoring System
- URL: http://arxiv.org/abs/2306.02709v2
- Date: Tue, 20 Jun 2023 11:02:59 GMT
- Title: Comparative Study on Semi-supervised Learning Applied for Anomaly
Detection in Hydraulic Condition Monitoring System
- Authors: Yongqi Dong, Kejia Chen, Zhiyuan Ma
- Abstract summary: This study systematically compares semi-supervised learning methods applied for anomaly detection in hydraulic condition monitoring systems.
The customized extreme learning machine based semi-supervised HELM model obtained state-of-the-art performance with the highest accuracy (99.5%), the lowest false positive rate (0.015), and the best F1-score (0.985) beating other semi-supervised methods.
- Score: 0.32228025627337864
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Condition-based maintenance is becoming increasingly important in hydraulic
systems. However, anomaly detection for these systems remains challenging,
especially since that anomalous data is scarce and labeling such data is
tedious and even dangerous. Therefore, it is advisable to make use of
unsupervised or semi-supervised methods, especially for semi-supervised
learning which utilizes unsupervised learning as a feature extraction mechanism
to aid the supervised part when only a small number of labels are available.
This study systematically compares semi-supervised learning methods applied for
anomaly detection in hydraulic condition monitoring systems. Firstly, thorough
data analysis and feature learning were carried out to understand the
open-sourced hydraulic condition monitoring dataset. Then, various methods were
implemented and evaluated including traditional stand-alone semi-supervised
learning models (e.g., one-class SVM, Robust Covariance), ensemble models
(e.g., Isolation Forest), and deep neural network based models (e.g.,
autoencoder, Hierarchical Extreme Learning Machine (HELM)). Typically, this
study customized and implemented an extreme learning machine based
semi-supervised HELM model and verified its superiority over other
semi-supervised methods. Extensive experiments show that the customized HELM
model obtained state-of-the-art performance with the highest accuracy (99.5%),
the lowest false positive rate (0.015), and the best F1-score (0.985) beating
other semi-supervised methods.
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