Du, S.; Han, W.; Kang, Z.; Luo, F.; Liao, Y.; Li, Z. A Peak Finding Siamese Convolutional Neural Network (PF-SCNN) for Aero-engine Hot Jet FT-IR Spectrum Classification. Preprints2024, 2024071383. https://doi.org/10.20944/preprints202407.1383.v1
APA Style
Du, S., Han, W., Kang, Z., Luo, F., Liao, Y., & Li, Z. (2024). A Peak Finding Siamese Convolutional Neural Network (PF-SCNN) for Aero-engine Hot Jet FT-IR Spectrum Classification. Preprints. https://doi.org/10.20944/preprints202407.1383.v1
Chicago/Turabian Style
Du, S., Yurong Liao and Zhaoming Li. 2024 "A Peak Finding Siamese Convolutional Neural Network (PF-SCNN) for Aero-engine Hot Jet FT-IR Spectrum Classification" Preprints. https://doi.org/10.20944/preprints202407.1383.v1
Abstract
Aiming at solving difficulties related to aero-engine classification and identification, the infrared spectra of aero-engine hot jets are measured by telemetry Fourier transform infrared spectrometer, and a six types aero-engine hot jet spectrum data set is generated. The spectrum data set were randomly sampled at a ratio of 8:1:1 to generate training set, validation set and prediction set. In this paper, a Peak Finding Siamese Convolutional Neural Network (PF-SCNN) is designed to match and classify spectrum data. In the first place, the matching pairs of training set and validation set are made independently with the labels of positive and negative samples creating. The spectrum feature extraction and distance similarity calculation are carried out by the Siamese convolutional neural network (SCNN). When entering the prediction phase, the prediction set and training set are integrated to predict in the trained model. The prediction set labels are ultimately determined by matching them with the training set labels having the highest similarities to ensure accurate predictions. To improve the operation efficiency of the SCNN, a peak finding method is introduced to extract the spectrum peaks. Peak positions of high frequency are counted, while the peak data are extracted. The peak data are used to take the place of the original data for training and prediction. The performance measures are defined as Accuracy, Precision, Recall, Confusion matrix and F1-score, and the prediction accuracy was up to 99%. The experimental results indicate that the proposed PF-SCNN is suitable for our spectral dataset and can complete the task of infrared spectrum classification of aero-engine hot jets.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
