Temperature interpretation method for temperature indicating paint based on spectrogram

被引:4
作者
Ge, Junfeng [1 ,2 ]
Wang, Li [1 ]
Gui, Kang [1 ,2 ,3 ]
Ye, Lin [1 ,2 ]
机构
[1] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automation, Wuhan 430074, Hubei, Peoples R China
[2] Natl Key Lab Multispectral Informat Intelligent Pr, Wuhan 430074, Hubei, Peoples R China
[3] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automation, Room 311,Bldg East 2,1037 Luoyu Rd, Wuhan 430074, Hubei, Peoples R China
关键词
Temperature indicating paint; Reflectance spectrum; Spectrogram; Convolutional Neural Network;
D O I
10.1016/j.measurement.2023.113317
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Accurate interpretation of temperature indicating paint (TIP) is of great importance in aviation and other industrial applications. This study presents a novel temperature interpretation approach based on the 2D spectrogram of TIP. A deep learning model called ESTNet(efficient spectrogram-temperature network) has been developed. The Gramian Angular Field (GAF) and the Continuous Wavelet Transform (CWT) are used to transform 1D reflectance spectrum into 2D spectrogram, GASF-Graph and scale2-Graph. The residual blocks and two composite coefficients are used to design RSTNet with an accuracy of 94% on KN3A samples when the input is GASF-Graph or scale2-Graph. Furthermore, RSTNet is optimized as ESTNet by the spectrogram information fusion strategy and channel attention mechanism. Using ESTNet, the interpretation accuracy of KN3A, KN6, and KN8 samples is 96%, 94%, and 96%, respectively, and RMSE is 1.5 & DEG;C, 2.1 & DEG;C, 1.8 & DEG;C, respectively. This research provides valuable insight and reference for other spectroscopic applications.
引用
收藏
页数:12
相关论文
共 44 条
[11]   Automated gender-Parkinson's disease detection at the same time via a hybrid deep model using human voice [J].
Kaya, Duygu .
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2022, 34 (26)
[12]   The mRMR-CNN based influential support decision system approach to classify EEG signals [J].
Kaya, Duygu .
MEASUREMENT, 2020, 156
[13]  
Keogh E. J., 2000, Proceedings. KDD-2000. Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, P285, DOI 10.1145/347090.347153
[14]   Color recognition with a camera: A supervised algorithm for classification [J].
Lalanne, T ;
Lempereur, C .
1998 IEEE SOUTHWEST SYMPOSIUM ON IMAGE ANALYSIS AND INTERPRETATION, 1998, :198-204
[15]   Surface temperature measurement on engine components by means of irreversible thermal coatings [J].
Lempereur, C. ;
Andral, R. ;
Prudhomme, J. Y. .
MEASUREMENT SCIENCE AND TECHNOLOGY, 2008, 19 (10)
[16]   The Research of Temperature Indicating Paints and Its Application in Aero-engine Temperature Measurement [J].
Li Yang ;
Li Zhi-min .
2014 ASIA-PACIFIC INTERNATIONAL SYMPOSIUM ON AEROSPACE TECHNOLOGY, APISAT2014, 2015, 99 :1152-1157
[17]  
Loshchilov I., 2016, arXiv, DOI DOI 10.48550/ARXIV.1608.03983
[18]  
Maosong L, 2007, 2007 8 INT C EL MEAS, DOI [10.1109/ICEMI.2007.4350848, DOI 10.1109/ICEMI.2007.4350848]
[19]   The influence of training sample size on the accuracy of deep learning models for the prediction of soil properties with near-infrared spectroscopy data [J].
Ng, Wartini ;
Minasny, Budiman ;
Mendes, Wanderson de Sousa ;
Melo Dematt, Jose Alexandre .
SOIL, 2020, 6 (02) :565-578
[20]  
Nguyen RMH, 2014, LECT NOTES COMPUT SC, V8695, P186, DOI 10.1007/978-3-319-10584-0_13