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In general, the wheel defect detection using machine learning generally says whether the wheel has a defect or not by using the sensors and uses CNN algorithm. The problem is that it is more cost expensive because of the use of sensors and by using the CNN algorithm, it just says whether the wheel has a defect or not and not the location of the defect and what is the reason of the defect. So we propose a new method of solving these conflicts by using RCNN algorithm.
This project describes a successful but challenging application of data mining in the railway industry. The objective is to optimize the maintenance and operation of trains through prognostics of wheel failures. In addition to reducing maintenance costs, the proposed technology will help improve railway safety and augment throughput. Building on established techniques from data mining and machine learning, we present a methodology to learn models to predict train wheel failures from readily available operational and maintenance data. This methodology addresses various data mining tasks such as automatic labeling, feature extraction, model building, model fusion, and evaluation. After a detailed description of the methodology, we report results from large-scale experiments. These results clearly show the great potential of this innovative application of data mining in the railway industry.
Why: Problem statement
The wheel defect detection using machine learning just detects the flaws present in the wheel and uses sensors. This leads to some of the limitations like cost-effective and time increases to find the location of flaws.
How: Solution description
The solution is that we use RCNN algorithm in which it does the work of sensor so that cost can be reduced and another advantage is that, the defect can be located and the reason for the defect can be said.
Wheel defects on railway wagons have been identified as an important source of damage to the railway infrastructure and rolling stock. They also cause noise and vibration emissions that are costly to mitigate. We propose two machine learning methods to automatically detect these wheel defects, based on the wheel vertical force measured by a permanently installed sensor system on the railway network. Our methods automatically learn different types of wheel defects and predict during normal operation if a wheel has a defect or not. The first method is based on novel features for classifying time series data and it is used for classification with a support vector machine. To evaluate the performance of our method we construct multiple data sets for the following defect types: flat spot, shelling, and non-roundness. We outperform classical defect detection methods for flat spots and demonstrate prediction for the other two defect types for the first time. Motivated by the recent success of artificial neural networks for image classification, we train custom artificial neural networks with convolutional layers on 2-D representations of the measurement time series. The neural network approach improves the performance on wheels with flat spots and non-roundness by explicitly modeling the multi-sensor structure of the measurement system through multiple instance learning and shift-invariant networks.
Our main contributions are two methods for automatic railway wheel defect detection and classification through vertical force measurements of trains running in full operational speed. For the first method, we design novel wavelet features for time series data from multiple sensors and we learn a classifier using a support vector machine. For the second method, we design and train convolutional neural networks for different wheel defect types by deep learning.
We evaluate our novel and other classical methods for wheel defect detection on two labeled data sets with different types of wheel defects, that we have constructed from calibration runs and maintenance reports.
How is it different from competition
Earlier CNN was used which only plots the defect in the wheel. Here, we use RCNN which plots defect by pointing the exact defect correctly. Also, it tells whether it is casting or welding defect.