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Fault Types

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Related Experiment Video

Updated: Jan 28, 2026

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

Multimodal fault detection model for photovoltaic modules.

Shuaishuai Yu¹, Fubao Gan², Tao Han¹

¹School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, Anhui, China.

Scientific Reports

|January 26, 2026

Summary

This study introduces Photovoltaic-DETR, a multimodal model for detecting photovoltaic module faults using infrared and visible light images. The new model enhances detection accuracy and efficiency while reducing computational load.

Keywords:

Fault detection Multi-scale fusion Multimodal Photovoltaic modules RT-DETR

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Last Updated: Jan 28, 2026

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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Published on: June 13, 2010

Area of Science:

Renewable Energy Engineering
Computer Vision
Artificial Intelligence

Background:

The photovoltaic industry is rapidly expanding, necessitating advanced fault detection methods.
Existing single-modality image-based models have limitations in photovoltaic module fault detection.
Multimodal approaches can improve the accuracy and robustness of fault detection systems.

Purpose of the Study:

To develop a multimodal fault detection model for photovoltaic modules.
To overcome the limitations of single-modality image-based detection models.
To improve the efficiency and accuracy of photovoltaic module fault detection.

Main Methods:

Proposed Photovoltaic-DETR, a multimodal fault detection model based on RT-DETR.
Constructed a lightweight backbone network with ORPELAN and ReLA Block modules and an auxiliary reversible branch.
Developed a reconstructed feature fusion network with attention-scale sequence fusion and reparameterization.
Implemented dynamic upsampling and downsampling using the DySample module.

Main Results:

Photovoltaic-DETR demonstrated improved mAP@50% across multiple datasets compared to the baseline RT-DETR model.
Achieved a 28.6% reduction in parameter count and a 28.5% decrease in computational load.
Showcased excellent adaptability in multimodal fault detection for photovoltaic modules.

Conclusions:

Photovoltaic-DETR offers superior performance in multimodal photovoltaic module fault detection.
The model provides a robust technical foundation for industrial applications.
The developed model enhances the reliability and efficiency of clean energy infrastructure.