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Updated: May 25, 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

A Multi-Scale Self-Supervision Approach for Bearing Anomaly Detection Using Sensor Data Under Multiple Operating

Zhuoheng Dai¹, Lei Jiang¹, Feifan Li¹

¹College of Science and Technology, Ningbo University, Ningbo 315300, China.

Sensors (Basel, Switzerland)

|February 26, 2025

Summary

This study introduces an advanced bearing early anomaly detection method using contrastive learning and reconstruction. The technique achieves high accuracy and detects faults significantly earlier than existing unsupervised methods.

Keywords:

early anomaly detection imbalanced industrial time series multiple operating conditions self-supervised learning

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Last Updated: May 25, 2025

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Data Acquisition Protocol for Determining Embedded Sensitivity Functions

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Published on: April 20, 2016

Area of Science:

Mechanical Engineering
Data Science
Artificial Intelligence

Background:

Industrial equipment failures are costly and disruptive.
Early fault detection is crucial for predictive maintenance.
Challenges include imbalanced data and changing operational modes.

Purpose of the Study:

To develop an effective early anomaly detection method for industrial bearings.
To address data imbalance and operational variability in fault detection.
To improve the accuracy and timeliness of fault identification.

Main Methods:

Utilized Ricker wavelet transform for noise reduction and signal extraction from vibration data.
Employed a BYOL-based contrastive learning network for discriminative global feature representation.
Incorporated a reconstruction loss to learn local details and preserve data integrity.

Main Results:

Achieved an average fault detection accuracy of 96.97% on the CWRU dataset.
Detected early faults at least 2.3 hours earlier than other unsupervised methods on the IMS dataset.
Demonstrated excellent generalization ability on the XJTU-SY dataset.

Conclusions:

The proposed method effectively detects early bearing faults under varying conditions.
It overcomes limitations of traditional unsupervised methods by not requiring negative samples.
The approach offers superior performance in accuracy and early detection capabilities.