Online temperature drift compensation of Fabry-Perot filter based on machine learning and linear fitting

Sheng, Wenjuan; Lou, Haitao; Pan, Junfeng; Wen, Jianxiang; Peng, G.D.

doi:10.1016/j.sna.2023.114774

Sensors and Actuators A: Physical

2023

DOI: 10.1016/j.sna.2023.114774

|View full text |Cite

Online temperature drift compensation of Fabry-Perot filter based on machine learning and linear fitting

Wenjuan Sheng,

Haitao Lou,

Junfeng Pan

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

2025

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Machine learning-augmented multi-arrayed fiber bragg grating sensors for enhanced structural health monitoring by discriminating strain and temperature variations

Saha,

Hadigheh,

Rukhlenko

et al. 2024

J Civil Struct Health Monit

View full text Add to dashboard Cite

Fiber optic sensors (FOS) in long-term structural health monitoring (SHM) have drawn significant attention due to their pivotal role in detecting defects and measuring structural performance in diverse infrastructures. While using FOS, temperature variation due to environmental factors is still considered one of the major challenges to isolating sensing parameters. To address this issue, we reported a machine learning (ML)-augmented multi-parameter sensing system that enables simultaneous detection of strain and temperature effects based on one single fiber Bragg gratings (FBGs) sensor for SHM. The initial phase entailed designing, fabricating, and characterizing a novel FBG sensor in the laboratory, incorporating a set of four FBGs, each distinguished by distinct Bragg wavelengths. In the next phase, ML algorithms are employed to separate temperature effects from strain variations. As a proof of concept, mechanical loading tests are conducted on the sensor, exposing the FBG portion to various temperature conditions. In the final phase, data collected from a post-tensioned concrete bridge embedded with both strain and temperature FBG sensors are utilized, and the developed ML models are applied to observe real-environment outcomes. Despite the limited feature points of collected FBG spectrums, the developed ML models effectively address cross-sensitivity issues induced by temperature perturbations. The long-term benefit of using FOS is that it will enable a better understanding and utilization of aging infrastructure. This will potentially reduce embodied carbon of infrastructure in the future and assist in the global efforts to achieve Net-Zero.

show abstract

Machine learning-augmented multi-arrayed fiber bragg grating sensors for enhanced structural health monitoring by discriminating strain and temperature variations

Saha,

Hadigheh,

Rukhlenko

et al. 2024

J Civil Struct Health Monit

View full text Add to dashboard Cite

show abstract

A novel temperature drift compensation method based on LSTM for NMR sensor

Wang,

Li,

Zhang

et al. 2025

Measurement

View full text Add to dashboard Cite

Temperature Compensation Model for Monitoring Sensor in Steel Industry Load Management

Liyuan Sun,

Zeming Yang,

Nan Pan

et al. 2024

Int. j. eng. technol. innov.

View full text Add to dashboard Cite

The iron ore industry faces increasing electricity demand due to industrialization, making effective management of electricity demand crucial. This study proposes a temperature compensation model using Support Vector Regression (SVR), aiming to enhance the accuracy of sensors in monitoring electricity demand. An experiment is conducted to assess the impact of temperature on sensor measurements, and a modified Whale Optimization Algorithm is employed to correct the sensor outputs. The proposed model is compared with both PSO-SVR and unimproved WOA-SVR. Results show that the proposed model significantly improves accuracy, achieving a determination coefficient of 0.7882 and a relative standard deviation of the error square sum of 4.6412%. The results of this study not only enhance power demand management in iron mining but also hold potential applications across various industries.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Online temperature drift compensation of Fabry-Perot filter based on machine learning and linear fitting

Cited by 3 publications

References 30 publications

Machine learning-augmented multi-arrayed fiber bragg grating sensors for enhanced structural health monitoring by discriminating strain and temperature variations

Machine learning-augmented multi-arrayed fiber bragg grating sensors for enhanced structural health monitoring by discriminating strain and temperature variations

A novel temperature drift compensation method based on LSTM for NMR sensor

Temperature Compensation Model for Monitoring Sensor in Steel Industry Load Management

Contact Info

Product

Resources

About