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Paper ID:IJSREM62022

Design and Implementation of a Humidity-Based Corrosion Risk Monitoring System for Aircraft Structures

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM62022

Volume:

10 |

4 |

April 2026

Pages:

1-4

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM62022

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

LSRM

Layha Shree R M

Department of Aviation

Vels Institute of Science, Technology & Advanced Studies, Chennai 600117, Tamil Nadu, India

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MA

Mohamed Abdulla.A

Department of Aviation

Vels Institute of Science, Technology & Advanced Studies, Chennai 600117, Tamil Nadu, India

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U

UmaKarthicka

Department of Aviation

Vels Institute of Science, Technology & Advanced Studies, Chennai 600117, Tamil Nadu, India

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PM

Peer Mohamed.M

Department of Aviation

Vels Institute of Science, Technology & Advanced Studies, Chennai 600117, Tamil Nadu, India

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Mentors

DR

Dr.S Ramasubramanian

Department of Aviation

Vels Institute of Science, Technology & Advanced Studies, Chennai 600117, Tamil Nadu, India

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Abstract

Corrosion is a critical issue in aircraft structures as it directly affects structural integrity, safety, and service life. Aircraft operate in varying environmental conditions such as high humidity, temperature variations, and exposure to saline atmosphere, which accelerate corrosion in metallic components. If not detected and controlled at an early stage, corrosion can lead to structural weakening and increased maintenance costs. The main objective of this project is to study corrosion in aircraft structures and to develop a simple humidity-based corrosion risk detection model to demonstrate preventive monitoring. In this project, a detailed study of different types of aircraft corrosion such as uniform corrosion, galvanic corrosion, pitting corrosion, and stress corrosion cracking was carried out. A prototype model was developed using a humidity sensor and microcontroller to monitor environmental moisture levels. The sensed humidity data is continuously compared with a predefined threshold value. When the humidity exceeds the critical limit, visual and audio alerts are activated to indicate a high corrosion risk condition. The results show that increased humidity levels significantly raise the probability of corrosion formation. The working model successfully demonstrates early-stage environmental monitoring as a preventive approach.This project highlights the importance of condition-based monitoring in aviation maintenance. The proposed system can be further enhanced with advanced sensors and integrated monitoring systems for real-time aircraft health management.

KEYWORDS

Corrosion galvanic corrosion pitting corrosion and stress corrosion

Cite This Article

Layha Shree R M, Mohamed Abdulla.A, UmaKarthicka, Peer Mohamed.M, Dr.S Ramasubramanian, "Design and Implementation of a Humidity-Based Corrosion Risk Monitoring System for Aircraft Structures," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, pp. 1-4, April 2026. https://doi.org/10.55041/IJSREM62022

Full Article/Conclusion

The project successfully presents the development of a humidity-based corrosion risk detection system using an Arduino microcontroller and a DHT11 sensor. The system continuously monitors environmental conditions and accurately detects variations in humidity levels, which are a major factor contributing to corrosion in aircraft structures. By comparing the sensed humidity with a predefined threshold value, the system effectively differentiates between safe and corrosion-prone conditions. When the humidity level exceeds the threshold, the system immediately activates visual and audio alerts, ensuring quick identification of potential risk. The experimental results and data analysis clearly demonstrate that as humidity increases, the probability of corrosion also increases. This validates the effectiveness of the system in providing early warning signals, which is essential for preventive maintenance in the aviation industry. The implementation of such a system can help reduce unexpected structural damage, improve operational safety, and minimize maintenance costs by enabling timely action before severe corrosion occurs. In addition, the project highlights the importance of real-time environmental monitoring compared to traditional periodic inspection methods. While conventional techniques may fail to detect early-stage corrosion conditions, this system provides continuous monitoring and immediate response, making it more efficient and reliable. Although the current model focuses only on humidity-based detection, it establishes a strong foundation for future improvements. Further enhancements can include the integration of additional sensors such as temperature and corrosion-specific sensors, as well as advanced technologies like Internet of Things (IoT) and Artificial Neural Networks (ANN) for intelligent prediction and remote monitoring. These advancements can transform the system into a fully automated and smart corrosion monitoring solution suitable for real-world aircraft applications. Overall, the project successfully demonstrates a simple, cost-effective, and efficient approach to detecting corrosion risk at an early stage. It emphasizes the significance of preventive maintenance and showcases how embedded systems can be effectively used to improve the safety, reliability, and lifespan of aircraft structures

References

[1]             Lucy Li, Mounia Chakik, Ravi Prakash, A Review of Corrosion in Aircraft Structures and Graphene-Based Sensors for Advanced Corrosion Monitoring, Sensors Journal, 2021. [2]             Magdalena Czaban, Aircraft Corrosion – Review of Corrosion Processes and its Effects, 2019. [3]             Keliang Ren et al., Influence of Environmental Factors on Corrosion Damage, 2011. [4]             Li et al., Corrosion Monitoring using Advanced Sensors, 2021. [5]             Kamińska et al., Corrosion Resistance of Aircraft Structure Joints, 2020. [6]             Goodfellow, I., Bengio, Y., & Courville, A., Deep Learning, MIT Press, 2016.

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Publication Date

4/30/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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