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Related Experiment Video

Updated: Oct 7, 2025

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

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

Published on: October 28, 2022

A High-Dimensional and Small-Sample Submersible Fault Detection Method Based on Feature Selection and Data

Penghui Zhao¹, Qinghe Zheng¹, Zhongjun Ding²

¹School of Information Science and Engineering, Shandong University, Qingdao 266237, China.

Sensors (Basel, Switzerland)

|January 11, 2022

Summary

This summary is machine-generated.

This study introduces an advanced fault detection method for manned submersibles, addressing scarce sensor data. The approach enhances safety by achieving 97% accuracy in identifying submersible faults using deep learning techniques.

Keywords:

data augmentation fault detection feature selection high-dimensional sensor data limited fault event manned submersible

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Last Updated: Oct 7, 2025

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Area of Science:

Marine Engineering
Robotics and Autonomous Systems
Artificial Intelligence

Background:

Manned submersible safety is critical, yet hampered by scarce and high-dimensional sensor data for fault detection.
Existing fault detection methods struggle with the unique challenges posed by submersible operational environments.

Purpose of the Study:

To propose a novel fault detection method for manned submersibles to enhance equipment and personnel safety.
To overcome limitations of scarce and high-dimensional sensor data through advanced feature selection and data augmentation.

Main Methods:

A feature selection module utilizing hierarchical clustering and Autoencoder (AE) to identify critical sensor features.
An improved Deep Convolutional Generative Adversarial Networks (DCGAN)-based data augmentation module for generating synthetic sensor data.
A fault detection module employing a Convolutional Neural Network (CNN) with a LeNet-5 architecture, trained on augmented data.

Main Results:

The proposed method successfully detected fault samples in submersible hydraulic system sensor data.
Achieved a high detection accuracy of 97% for submersible faults.
Demonstrated superior performance compared to other classic fault detection algorithms.

Conclusions:

The integrated approach of feature selection, data augmentation, and CNN-based detection effectively addresses data scarcity in submersible fault detection.
The developed method offers a robust and accurate solution for improving the safety and reliability of manned submersibles.