A Peak-Finding Siamese Convolutional Neural Network (PF-SCNN) for Aero-Engine Hot Jet FT-IR Spectrum Classification
Abstract
:1. Introduction
- To achieve the classification of aero-engines, this paper employs an infrared spectroscopy detection method to measure the spectra of aero-engines’ hot jets, which are significant sources of aero-engines’ infrared radiation, using an FT-IR spectrometer. The infrared spectra measured using the FT-IR spectrometer offers characteristic molecular-level information about substances, thereby enhancing the scientific basis for classifying aero-engines based on their infrared spectra.
- This paper presents a SCNN for classifying the hot jet spectra of aero-engines using a data matching method. The network is based on 1DCNN, and feature similarity is calculated using the Euclidean distance metric. Subsequently, a spectral comparison method is employed for the purpose of performing spectrum classification.
- The objective of this paper is to propose an algorithm for identifying peaks that will optimize the training and prediction speed of the SCNN. The algorithm identifies the peak value in the mid-infrared spectrum data and quantifies the high-frequency peaks, which are subsequently employed as input for the SCNN.
2. Experimental Design and Data Set Production for Hot Jet Infrared Spectrum Measurement of Aero-Engines
2.1. Aero-Engine Spectrum Measurement Experiment Design
2.2. Spectrum Data Preprocessing and Data Set Production
3. Architectural Design of Peak-Finding Siamese Convolutional Neural Network
3.1. Overall Network Structure Design
3.2. Base Network Architecture
3.2.1. One-Dimensional Convolutional Layer (Cov1D Layer)
3.2.2. Maximum Pooling Layer (MaxPool Layer)
3.2.3. Flattening Layer
3.2.4. Fully Connected Layer (FC Layer)
3.2.5. Comparison Layer
3.3. Peak-Finding Algorithm
Algorithm 1: Peak-Finding and Peak Statistics Algorithm |
Input: Spectrum data |
Output: Peak data |
|
3.4. Network Training Methodology
3.4.1. Optimizer
3.4.2. Loss Function and Classification Accuracy
3.4.3. Similarity Score Function
4. Experiments and Results
4.1. Performance Measures and Experiment Results
4.2. The Traditional Method Classifies and Compares Experimental Results
4.3. Ablation Experiment Analysis
4.3.1. Peak Feature Effectiveness
4.3.2. SCNN Model
4.3.3. Optimizer Selection
4.3.4. Learning Rate Selection
4.3.5. Running Time
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Name | Measurement Pattern | Spectral Resolution (cm−1) | Spectral Measurement Range (μm) | Full Field of View Angle |
---|---|---|---|---|
EM27 | Active/Passive | Active: 0.5/1 Passive: 0.5/1/4 | 2.5~12 | 30 mrad (no telescope) (1.7°) |
Telemetry Fourier Transform Infrared Spectrometer | Passive | 1 | 2.5~12 | 1.5° |
Aero-Engine Serial Number | Environmental Temperature | Environmental Humidity | Detection Distance |
---|---|---|---|
Engine 1 (Turbofan) | 19 °C | 58.5% Rh | 5 m |
Engine 2 (Turbofan) | 16 °C | 67% Rh | 5 m |
Engine 3 (Turbojet) | 14 °C | 40% Rh | 5 m |
Engine 4 (Turbojet UAV) | 30 °C | 43.5% Rh | 11.8 m |
Engine 5 (Turbojet UAV with propeller at tail) | 20 °C | 71.5% Rh | 5 m |
Engine 5 (Turbojet) | 19 °C | 73.5% Rh | 10 m |
Number | Type | Number of Data Pieces | Number of Error Data | Full Band Data Volume | Mid-Infrared Range Data Volume |
---|---|---|---|---|---|
1 | Engine 1 (Turbojet UAV) | 193 | 0 | 16,384 | 7464 |
2 | Engine 2 (Turbojet UAV with propeller at tail) | 48 | 0 | 16,384 | 7464 |
3 | Engine 3 (Turbojet) | 202 | 3 | 16,384 | 7464 |
4 | Engine 4 (Turbofan) | 792 | 17 | 16,384 | 7464 |
5 | Engine 5 (Turbofan) | 258 | 2 | 16,384 | 7464 |
6 | Engine 6 (Turbojet) | 384 | 4 | 16,384 | 7464 |
Forecast Results | |||
---|---|---|---|
Positive Samples | Negative Samples | ||
Real results | Positive samples | TP | TN |
Negative samples | FP | FN |
Methods | Parameter Settings |
---|---|
PF-SCNN | Conv1D (32, 3), Conv1D (64, 3), Conv1D (128, 3), activation = ‘relu’ |
MaxPooling1D (2) (x) | |
Dense (128, activation = ‘relu’) | |
Optimizers = RMSProp, (learning_rate = 0.0001) | |
Loss = contrastive_loss, metrics = [accuracy] | |
Epochs = 500 |
Evaluation Criterion | Accuracy | Precision | Recall | Confusion Matrix | F1-Score |
---|---|---|---|---|---|
Data set | 99.46% | 99.77% | 99.56% | [27 0 0 0 0 0] [ 0 72 0 0 0 0] [ 0 0 21 0 0 0] [ 0 1 0 37 0 0] [ 0 0 0 0 20 0] [ 0 0 0 0 0 6] | 99.66% |
Characteristic Peak Type | Emission Peak (cm−1) | Absorption Peak (cm−1) | ||
---|---|---|---|---|
Standard feature peak value | 2350 | 2390 | 720 | 667 |
Feature peak range value | 2350.5–2348 | 2377–2392 | 722–718 | 666.7–670.5 |
Methods | Parameter Settings |
---|---|
SVM | decision_function_shape = ‘ovr’, kernel = ‘rbf’ |
XGBoost | objective = ‘multi:softmax’, num_classes = num_classes |
CatBoost | loss_function = ‘MultiClass’ |
Adaboost | n_estimators = 200 |
Random Forest | n_estimators = 300 |
LightGBM | objective’: ‘multiclass’, ‘num_class’: num_classes |
Neural Network | hidden_layer_sizes = (100), activation = ‘relu’, solver = ‘adam’, max_iter = 200 |
Evaluation Criterion | Accuracy | Precision Score | Recall | Confusion Matrix | F1-Score | |
---|---|---|---|---|---|---|
Classification Methods | ||||||
CO2 feature vector + SVM | 59.78% | 44.15% | 47.67% | [ 8 0 3 0 0 0] [ 0 3 0 0 0 0] [ 9 1 12 0 0 0] [ 0 3 1 84 22 33] [ 0 0 0 0 0 0] [ 0 0 0 0 0 0] | 42.38% | |
CO2 feature vector + XGBoost | 94.97% | 92.44% | 93.59% | [15 0 3 0 0 0] [ 0 7 0 0 0 0] [ 2 0 13 0 0 0] [ 0 0 0 83 3 0] [ 0 0 0 1 19 0] [ 0 0 0 0 0 33] | 92.95% | |
CO2 feature vector + CatBoost | 94.41% | 90.35% | 93.52% | [15 0 2 0 0 0] [ 0 6 0 0 0 0] [ 2 0 14 0 0 0] [ 0 0 0 83 4 0] [ 0 1 0 1 18 0] [ 0 0 0 0 0 33] | 91.81% | |
CO2 feature vector + AdaBoost | 79.89% | 63.66% | 71.49% | [17 5 6 0 0 0] [ 0 2 0 0 0 0] [ 0 0 10 0 0 0] [ 0 0 0 84 18 3] [ 0 0 0 0 0 0] [ 0 0 0 0 4 30] | 62.56% | |
CO2 feature vector + Random Forest | 94.41% | 91.40% | 92.70% | [15 0 4 0 0 0] [ 0 7 0 0 0 0] [ 2 0 12 0 0 0] [ 0 0 0 83 3 0] [ 0 0 0 1 19 0] [ 0 0 0 0 0 33] | 91.91% | |
CO2 feature vector + LightGBM | 94.41% | 90.68% | 92.40% | [14 0 2 0 0 0] [ 0 6 0 0 0 0] [ 3 0 14 0 0 0] [ 0 0 0 82 2 0] [ 0 1 0 2 20 0] [ 0 0 0 0 0 33] | 91.42% | |
CO2 feature vector + Neural Networks | 84.92% | 76.79% | 76.57% | [17 0 2 0 0 0] [ 0 6 0 0 0 0] [ 0 0 12 0 0 0] [ 0 0 2 84 18 0] [ 0 1 0 0 0 0] [ 0 0 0 0 4 33] | 76.02% |
Methods | Accuracy | Precision | Recall | Confusion Matrix | F1-Score | Running Time |
---|---|---|---|---|---|---|
Peaks + SVM | 58.15 | 48.09 | 43.58 | [ 0 0 0 0 0 0] [13 42 0 0 0 0] [ 0 0 20 0 13 6] [14 30 0 38 0 0] [ 0 0 1 0 7 0] [ 0 0 0 0 0 0] | 41.02 | 0.54 |
Peaks + XGBoost | 98.91 | 96.78 | 99.01 | [27 0 0 0 0 0] [ 0 72 0 1 0 0] [ 0 0 21 0 0 1] [ 0 0 0 37 0 0] [ 0 0 0 0 20 0] [ 0 0 0 0 0 5] | 97.76 | 1.27 |
Evaluation Criterion | Accuracy | Precision | Recall | Confusion Matrix | F1-Score |
---|---|---|---|---|---|
Data set | 99.46% | 99.24% | 99.56% | [27 0 0 0 0 0] [ 0 72 0 0 0 0] [ 0 0 21 0 0 0] [ 0 0 1 37 0 0] [ 0 0 0 0 20 0] [ 0 0 0 0 0 6] | 99.39% |
Methods | Parameter Settings |
---|---|
RMSProp | learning rate = 0.0001, clipvalue = 1.0 |
Adam | learning rate = 0.0001, clipvalue = 1.0 |
Nadam | learning rate = 0.0001, clipvalue = 1.0 |
SGD | learning rate = 0.0001, clipvalue = 1.0 |
Adagrad | learning rate = 0.0001, clipvalue = 1.0 |
Adadelta | learning rate = 0.0001, clipvalue = 1.0 |
Optimizers | Prediction Accuracy | Training Time/s |
---|---|---|
RMSProp | 96% | 1180.96 |
Adam | 96% | 1014.82 |
Nadam | 89% | 1523.14 |
SGD | 88% | 1101.65 |
Adagrad | 73% | 1021.90 |
Adadelta | 68% | 991.90 |
Learning Rate | Prediction Accuracy | Training Time/s |
---|---|---|
0.001 | 0.50 | 1283.49 |
0.0001 | 0.96 | 1252.83 |
0.00001 | 0.88 | 1171.39 |
0.000001 | 0.84 | 1193.72 |
Methods | Prediction Time |
---|---|
PF-SCNN | 71 s Each data; 3:44:17 total |
SCNN | 79 s Each data; 4:30:45.78 total |
CO2 feature vector + SVM | 0.12 s |
CO2 feature vector + XGBoost | 0.30 s |
CO2 feature vector + CatBoost | 4.74 s |
CO2 feature vector + AdaBoost | 0.39 s |
CO2 feature vector + Random Forest | 0.56 s |
CO2 feature vector + LightGBM | 0.44 s |
CO2 feature vector + Neural Networks | 0.85 s |
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Share and Cite
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. Aerospace 2024, 11, 703. https://doi.org/10.3390/aerospace11090703
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. Aerospace. 2024; 11(9):703. https://doi.org/10.3390/aerospace11090703
Chicago/Turabian StyleDu, Shuhan, Wei Han, Zhenping Kang, Fengkun Luo, Yurong Liao, and Zhaoming Li. 2024. "A Peak-Finding Siamese Convolutional Neural Network (PF-SCNN) for Aero-Engine Hot Jet FT-IR Spectrum Classification" Aerospace 11, no. 9: 703. https://doi.org/10.3390/aerospace11090703