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Updated: May 17, 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

Research Progress on Data-Driven Industrial Fault Diagnosis Methods.

Liang Lei¹, Weibin Li¹, Shiwei Zhang²

¹School of Artificial Intelligence, Xidian University, Xi'an 710071, China.

Sensors (Basel, Switzerland)

|May 14, 2025

Summary

This summary is machine-generated.

This review explores industrial fault diagnosis methods using big data, highlighting deep learning and large models. Future research should focus on data quality, AI interpretability, and edge computing for enhanced equipment maintenance.

Keywords:

deep learning fault diagnosis industrial big data large language models

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Last Updated: May 17, 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

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Data Acquisition Protocol for Determining Embedded Sensitivity Functions

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Published on: April 20, 2016

Area of Science:

Industrial Engineering and Big Data Analytics
Machine Learning for Predictive Maintenance

Background:

Industry 5.0 emphasizes intelligent equipment maintenance and condition monitoring.
Industrial big data is crucial for understanding and diagnosing equipment faults.

Purpose of the Study:

To systematically review mainstream industrial fault diagnosis methods.
To analyze the evolution and application of data-driven techniques, especially deep learning and large models.
To identify future research directions in industrial fault diagnosis.

Main Methods:

Comprehensive literature review of industrial big data sources, datasets, and platforms.
Analysis of multi-source heterogeneous data in fault diagnosis.
In-depth examination of data-driven fault diagnosis, deep learning, and large model applications.

Main Results:

Deep learning algorithms play a pivotal role in modern industrial fault diagnosis.
Large models show significant potential for enhancing diagnostic intelligence and generalization.
The review covers data sources, datasets, platforms, and evolutionary paths of fault diagnosis methods.

Conclusions:

Data quality, interpretability of deep learning models, and edge-based large models are critical for future advancements.
Continued research is needed to overcome current limitations and improve the robustness of industrial fault diagnosis systems.