Wheel Defect Detection Using a Hybrid Deep Learning Approach

Abstract

Defective wheels pose a significant challenge in railway transportation, impacting operational performance and safety. Excessive traction and braking forces give rise to deviations from the intended conical tread shape, resulting in amplified vibrations and noise. Moreover, these deviations contribute to the accelerated damage of track components. Detecting wheel defects at an early stage is crucial to ensure safe and comfortable operation, as well as to minimize maintenance costs. However, the presence of various vibrations, such as those induced by the track, traction motors, and other rolling stock subsystems, poses a significant challenge for onboard detection techniques. These vibrations create difficulties in accurately identifying wheel defects in real-time during operational activities, often resulting in false alarms. This research paper aims to address this issue by using a hybrid deep learning-based approach for the accurate detection of various types of wheel defects using accelerometer data. The proposed approach aims to enhance wheel defect detection accuracy while considering onboard techniques' cost-effectiveness and efficiency. A realistic simulation model of the railway wheelset is developed to generate a comprehensive dataset. To generate vibration data in various scenarios, the model is simulated for 20 s under different conditions, including one non-faulty scenario and six faulty scenarios. The simulations are conducted at different speeds and track conditions to capture a wide range of operating conditions. Within each simulation iteration, a total of 200,000 data points are generated, providing a comprehensive dataset for analysis and evaluation. The generated data are then utilized to train and evaluate a hybrid deep learning model, employing a multi-layer perceptron (MLP) as a feature extractor and multiple machine learning models (support vector machine, random forest, decision tree, and k-nearest neighbors) for performance comparison. The results demonstrate that the MLP-RF (multi-layer perceptron with random forest) model achieved an accuracy of 99%, while the MLP-DT (multi-layer perceptron with decision tree) model achieved an accuracy of 98%. These high accuracy values indicate the effectiveness of the models in accurately classifying and predicting the outcomes. The contributions of this research work include the development of a realistic simulation model, the evaluation of sensor layout effectiveness, and the application of deep learning techniques for improved wheel flat detections.

Keywords: MLP; deep learning; false flange; nonlinear dynamics; wheel defects; wheel flats.

Figure 1

Data collection process.

Figure 2

Lateral acceleration of the wheelset in different conditions.

Figure 3

Model architecture.

Figure 4

(a) Pre-training process. (b) MLP Network pruned with ML-based classifier. Once the MLP has been pre-trained and equipped with the ability to extract relevant features, these extracted features serve as inputs to the subsequent machine learning model, as shown in (b). The machine learning model leverages the extracted features to perform the final classification of conicity values. By utilizing the comprehensive and representative features obtained from the MLP, the machine learning model can make accurate predictions regarding the severity of defects in railway wheelsets.

Figure 5

MLP networks’ architecture with three parallel branches and concatenation layer.

Figure 6

Bar chart of performance metrics of MLP, SVM, RF, DT, and k-N.

Figure 7

Classification results.