Early Identification of Unbalanced Freight Traffic Loads Based on Wayside Monitoring and Artificial Intelligence
Abstract
:1. Introduction
- -
- This automatic AI-based methodology is an improvement regarding the common methods found in literature, based on direct observations and contact force calculations;
- -
- This methodology is based on responses obtained not only from strain gauges but also from accelerometers. The use of accelerometers in WIM systems is not so common but brings some advantages. One is the ease of installation, and another is the possibility of being used in a wider monitoring system to detect other types of vehicle damages;
- -
- The proposed methodology is tested regarding the number and type of sensors showing a very high accuracy even with a reduced number of sensors.
2. Modelling
2.1. Freight Vehicle
2.2. Track
2.3. Vehicle–Track Interaction
3. Simulation
3.1. Virtual Wayside Monitoring Device
3.2. Baseline
3.3. Unbalanced Scenarios
3.4. Track Dynamic Response
4. Methodology for Unbalanced Loads Detection
4.1. Overview
- (i)
- The feature extraction from the measured data is made by testing two different indicator extraction techniques, namely, ARX models and PCA.
- (ii)
- To remove the effects of operational and environmental variations, the method of latent variables PCA is adopted.
- (iii)
- The Mahalanobis distance is then used for the fusion of all features into one damage index for each simulation but also to combine features from various sources. Thus, in the first level is performed the fusion of all features, in the second level, the fusion of all sensors, and in the third level, the fusion of the different measured quantities, in this case, accelerations and strains.
- (iv)
- For feature discrimination, automatic detection is achieved by combining ARX-based features and outliers analysis, and for classification, an automatic clustering process based on the k-means technique and PCA-based features is implemented.
4.2. Feature Extraction—ARX vs. PCA
- -
- Matrix XARX with 149 × 80, ARX features;
- -
- Matrix XPCA with 149 × 4, PCA features.
4.3. Feature Normalization—PCA
4.4. Data Fusion—Mahalanobis Distance
4.5. Feature Discrimination
4.5.1. Outlier Analysis
4.5.2. Cluster Analysis
4.5.3. Sensitivity for Different Sensor Layouts
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Carbody | Wheelset | Suspensions | |||
---|---|---|---|---|---|
Parameter | Value | Parameter | Value | Parameter | Value |
Mass | 13.5 | Mass | 1247 | Longitudinal stiffness | 44,981 |
Roll moment of inertia, | 49 | Roll moment of inertia | 312 | Lateral stiffness | 30,948 |
Pitch moment of inertia, | 673 | Yaw moment of inertia | 312 | Vertical stiffness | 1860 |
Yaw moment of inertia | 665 | Vertical damping | 16.7 | ||
Length | 10 | ||||
Height | 2.17 | ||||
Width | 2.297 |
Parameter | Value | |
---|---|---|
Rail | Ar (m2) | 7.67 × 10−4 |
ρr (kg.m3) | 7850 | |
Ir (m4) | 30.38 × 10−6 | |
Er (N/m2) | 210 × 109 | |
Rail pad | Kp (N/m) (Kx/Ky Kz) | 20 × 106/20 × 106/500 × 106 |
Cp (N.s/m) (Cx/Cy Cz) | 50 × 103/50 × 103/200 × 103 | |
Sleeper | ρs (N/m) | 2590 |
Ballast | Kb (N/m) (Kx/Ky Kz) | 900 × 103/2250 × 103/30 × 106 |
Cb (N/m) (Cx/Cy Cz) | 15 × 103/15 × 103/15 × 103 | |
Foundation | Kf (N/m) (Kx/Ky Kz) | 20 × 106/20 × 106/20 × 106 |
Sensors (Accelerometers + Strain Gauges) | Detection | Classification | |||
---|---|---|---|---|---|
False Positives | False Negatives | Cluster | Groups | False Classifications | |
16 (8 + 8) | 0% (0/113) | 0% (0/36) | 3 | (Long 1-2) (Transv1) (Transv2) | 6% (2/36) |
12 (6 + 6) | 0% (0/113) | 0% (0/36) | 2 | (Long 1-2) (Transv 1-2) | 6% (2/36) |
8 (4 + 4) | 0% (0/113) | 0% (0/36) | 2 | (Long 1-2) (Transv 1-2) | 8% (3/36) |
4 (2 + 2) | 2% (2/113) | 0% (0/36) | 2 | (Long 1-2) (Transv 1-2) | 25% (9/36) |
2 (2 + 0) | 1% (1/113) | 0% (0/36) | 2 | (Long 1-2) (Transv 1-2) | 28% (10/36) |
2 (0 + 2) | 1% (1/113) | 0% (0/36) | 2 | (Long 1-2) (Transv 1-2) | 39% (14/36) |
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Silva, R.; Guedes, A.; Ribeiro, D.; Vale, C.; Meixedo, A.; Mosleh, A.; Montenegro, P. Early Identification of Unbalanced Freight Traffic Loads Based on Wayside Monitoring and Artificial Intelligence. Sensors 2023, 23, 1544. https://doi.org/10.3390/s23031544
Silva R, Guedes A, Ribeiro D, Vale C, Meixedo A, Mosleh A, Montenegro P. Early Identification of Unbalanced Freight Traffic Loads Based on Wayside Monitoring and Artificial Intelligence. Sensors. 2023; 23(3):1544. https://doi.org/10.3390/s23031544
Chicago/Turabian StyleSilva, R., A. Guedes, D. Ribeiro, C. Vale, A. Meixedo, A. Mosleh, and P. Montenegro. 2023. "Early Identification of Unbalanced Freight Traffic Loads Based on Wayside Monitoring and Artificial Intelligence" Sensors 23, no. 3: 1544. https://doi.org/10.3390/s23031544
APA StyleSilva, R., Guedes, A., Ribeiro, D., Vale, C., Meixedo, A., Mosleh, A., & Montenegro, P. (2023). Early Identification of Unbalanced Freight Traffic Loads Based on Wayside Monitoring and Artificial Intelligence. Sensors, 23(3), 1544. https://doi.org/10.3390/s23031544