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Energy Losses in Transformers

Energy Losses in Transformers

In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality, the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
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The first cause can be the high resistance of the...

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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Multi-Scale Frequency-Aware Transformer for Pipeline Leak Detection Using Acoustic Signals.

Menghan Chen^1,2, Yuchen Lu^1,2, Wangyu Wu³

¹School of Integrated Circuit Engineering, Guangdong University of Technology, Guangzhou 510006, China.

Sensors (Basel, Switzerland)

|October 29, 2025

Summary

This summary is machine-generated.

A new Multi-Scale Frequency-Aware Transformer (MSFAT) improves pipeline leak detection accuracy to 97.2% by better using acoustic signal features and adapting to noise. This AI approach enhances reliability in industrial settings.

Keywords:

MSFAT acoustic measurement frequency attention leak detection noise filtering

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Area of Science:

Engineering
Artificial Intelligence
Signal Processing

Background:

Pipeline leak detection using acoustic signals faces challenges with time-frequency analysis, noise, and prior knowledge integration.
Existing deep learning methods struggle to effectively utilize acoustic data for reliable leak identification.

Purpose of the Study:

To propose a novel Multi-Scale Frequency-Aware Transformer (MSFAT) architecture for enhanced pipeline leak detection.
To address limitations in current AI-based acoustic signal analysis for industrial measurements.

Main Methods:

Developed MSFAT with a frequency-aware embedding layer for joint time-frequency feature learning.
Incorporated a multi-head frequency attention mechanism and an adaptive noise filtering module.
Utilized a multi-scale feature aggregation mechanism for robust global representation.

Main Results:

MSFAT achieved 97.2% accuracy and a 10.9% improved F1-score compared to standard Transformers.
Demonstrated robust performance across signal-to-noise ratios from 5 to 30 dB.
Ablation studies confirmed the significant contribution of frequency-aware mechanisms.

Conclusions:

The MSFAT architecture effectively integrates domain-specific knowledge into AI for superior pipeline leak detection.
The proposed method offers enhanced precision, reliability, and adaptability in complex industrial environments.