An Unsupervised Learning Approach for Wayside Train Wheel Flat Detection
Abstract
:1. Introduction
- (1)
- Development of an unsupervised data-driven methodology using acceleration responses on the rail for detecting defective wheels from healthy ones;
- (2)
- Implementation of AR, ARX, CWT, and PCA for feature extraction from multiple sensors to transform the time series measurements into damage-sensitive features, where the correlation with the damage can be more easily observed;
- (3)
- Analysis of the performance of the four feature extraction methods considering the different number and locations of the sensors on the rails;
- (4)
- Comparison of the sensitivity of the proposed methodologies to the side (left vs. right) of the defective wheel in a train axle;
- (5)
- Evaluation of the effectiveness of the proposed method with respect to the minimalist layout of sensors;
- (6)
- Improvement in wheel flat detection by applying a two-stage fusion process: in the first step, the features from each sensor are merged and, in the second stage, the multi-sensor information is fused to enhance the sensibility to the damage.
2. Numerical Simulation
2.1. Train–Track Dynamic Interaction
2.2. Virtual Wayside System
2.3. Baseline and Damaged Scenarios
3. Unsupervised Learning Methodology for Wheel Flat Detection
- Features extraction: application of four advanced data-driven models, including the continuous wavelet transform (CWT), auto-regressive model (AR), principal component analysis (PCA), and ARX to extract the damage-sensitive features from the time series;
- Feature normalization: normalization of the extracted features by the principal component analysis (PCA) method to increase the sensitivity to damage and remove environmental and operational variations (EOVs);
- Data fusion: implementation of a Mahalanobis distance (MD) to merge the features derived from each sensor and detect wheel defects more effectively. In the first stage, the features from each sensor are merged and, in the second stage, the multi-sensor information is fused to enhance the sensibility to the damage [26,32];
- Outlier analyses: upon completion of the previous step, a damage indicator (DI) is generated for each train passage; to distinguish each DI into a defective or a healthy wheel a statistical-based approach is used, in particular, an inverse cumulative distribution function that allows estimating a statistical confidence boundary (CB).
4. Application of the Methodology of Wheel Flat Detection to a Freight Train
4.1. Feature Extraction
4.1.1. AR Model
4.1.2. ARX Model
4.1.3. CWT
4.1.4. PCA
4.2. Feature Normalization
4.2.1. AR Model
4.2.2. ARX Model
4.2.3. CWT
4.2.4. PCA
4.3. Data Fusion
4.3.1. AR Model
4.3.2. ARX Model
4.3.3. CWT
4.3.4. PCA
4.4. Outlier Analysis
4.4.1. AR Model
4.4.2. ARX Model
4.4.3. CWT
4.4.4. PCA
5. Conclusions
- the AR and ARX methods are the most accurate feature extraction methods for wheel flat damage detection as they can robustly detect defects; these two methods are sensitive to the side of the damage being the most promising to automatically distinguish an existing defective wheel on the right side from the left side in future works;
- the CWT method is only capable of detecting damaged wheels and is not sensitive to the side of the defect;
- the accuracy of the PCA method to detect the defective wheel is low and damage detection using this method lacks reliability;
- the ARX method is the only method that can robustly detect the wheel flat with accelerometers placed in the sleepers.
- One of the novelties of this research in relation to previous works [5,8] is the comparison of the accuracy of four different feature extraction techniques using an unsupervised learning methodology to automatically detect a defective wheel, which is a clear step forward in terms of the effectiveness of the proposed method, and allows full implementation for real-world applications.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Baseline Scenarios | Damaged Scenarios | |
---|---|---|
Train | Freight—Laagrss wagon | |
Number of loading schemes | 6 | 1 (full capacity) |
Unevenness profiles | 4 | 1 |
Speeds (km/h) | 40–120 | 80 |
Noise ratio | 5% | |
Flat lengths (mm) | − | 50–100 |
Number of numerical analyses | 100 | 30 |
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Mohammadi, M.; Mosleh, A.; Vale, C.; Ribeiro, D.; Montenegro, P.; Meixedo, A. An Unsupervised Learning Approach for Wayside Train Wheel Flat Detection. Sensors 2023, 23, 1910. https://doi.org/10.3390/s23041910
Mohammadi M, Mosleh A, Vale C, Ribeiro D, Montenegro P, Meixedo A. An Unsupervised Learning Approach for Wayside Train Wheel Flat Detection. Sensors. 2023; 23(4):1910. https://doi.org/10.3390/s23041910
Chicago/Turabian StyleMohammadi, Mohammadreza, Araliya Mosleh, Cecilia Vale, Diogo Ribeiro, Pedro Montenegro, and Andreia Meixedo. 2023. "An Unsupervised Learning Approach for Wayside Train Wheel Flat Detection" Sensors 23, no. 4: 1910. https://doi.org/10.3390/s23041910