COMMON CROSSING CONDITION MONITORING WITH ON BOARD INERTIAL MEASUREMENTS

A railway turnout is an element of the railway infrastructure that influences the reliability of a railway traffic operation the most. The growing necessity for the reliability and availability in the railway transportation promotes a wide use of condition monitoring systems. These systems are typically based on the measurement of the dynamic response during operation. The inertial dynamic response measurement with on-board systems is the simplest and reliable way of monitoring the railway infrastructure. However, the new possibilities of condition monitoring are faced with new challenges of the measured information utilization. The paper deals with the condition monitoring of the most critical part of turnouts - the common crossing. The application of an on-board inertial measurement system ESAH-F for a crossing condition monitoring is presented and explained. The inertial measurements are characterized with the low correlation of maximal vertical accelerations to the lifetime. The data mining approach is used to recover the latent relations in the measurement’s information. An additional time domain and spectral feature sets are extracted from axle-box acceleration signals. The popular spectral kurtosis features are used additionally to the wavelet ones. The feature monotonicity ranking is carried out to select the most suited features for the condition indicator. The most significant features are fused in a one condition indicator with a principal component analysis. The proposed condition indicator delivers an almost two-time higher correlation to the lifetime as the maximal vertical accelerations. The regression analysis of the indicator to the lifetime with an exponential fit proves its good applicability for the crossing residual useful life prognosis

Auto-Classifier: A Robust Defect Detector Based on an AutoML Head

The dominant approach for surface defect detection is the use of hand-crafted feature-based methods. However, this falls short when conditions vary that affect extracted images. So, in this paper, we sought to determine how well several state-of-the-art Convolutional Neural Networks perform in the task of surface defect detection. Moreover, we propose two methods: CNN-Fusion, that fuses the prediction of all the networks into a final one, and Auto-Classifier, which is a novel proposal that improves a Convolutional Neural Network by modifying its classification component using AutoML. We carried out experiments to evaluate the proposed methods in the task of surface defect detection using different datasets from DAGM2007. We show that the use of Convolutional Neural Networks achieves better results than traditional methods, and also, that Auto-Classifier out-performs all other methods, by achieving 100% accuracy and 100% AUC results throughout all the datasets.Comment: 12 pages, 2 figures. Published in ICONIP2020, proceedings published in the Springer's series of Lecture Notes in Computer Scienc

An approach of ontology and knowledge base for railway maintenance

Maintenance methods have become automated and innovative, especially with the transition to maintenance 4.0. However, social issues such as coronavirus disease of 2019 (COVID-19) and the war in Ukraine have caused significant departures of maintenance experts, resulting in the loss of enormous know-how. As part of this work, we will propose a solution by exploring the knowledge and expertise of these experts for the purpose of sharing and conservation. In this perspective, we have built a knowledge base based on experience and feedback. The proposed method illustrates a case study based on the single excitation configuration interaction (SECI) method to optimally capture the explicit and tacit knowledge of each technician, as well as the theoretical basis, the model of Nonaka and Takeuchi

Advances in fault diagnosis for high-speed railway: A review

The high speed railway (HSR) is a complex system with many subsystems and components. The reliability of its core subsystems is a key consideration in ensuring the safety and operation efficiency of the whole system. As the service time increases, the degradation of these subsystems and components may lead to a range of faults and deteriorate the whole system performance. To ensure the operation safety and to develop reasonable maintenance strategies, fault detection and isolation is an indispensable functionality in high speed railway systems. In this paper, the traction power supply system, bogie system, civil infrastructure system, and control and signaling system of HSR are briefly summarized, and then different fault diagnosis methods for these subsystems are comprehensively reviewed. Finally, some future research topics are discussed

Developing Hybrid Machine Learning Models to Assign Health Score to Railcar Fleets for Optimal Decision Making

A large amount of data is generated during the operation of a railcar fleet, which can easily lead to dimensional disaster and reduce the resiliency of the railcar network. To solve these issues and offer predictive maintenance, this research introduces a hybrid fault diagnosis expert system method that combines density-based spatial clustering of applications with noise (DBSCAN) and principal component analysis (PCA). Firstly, the DBSCAN method is used to cluster categorical data that are similar to one another within the same group. Secondly, PCA algorithm is applied to reduce the dimensionality of the data and eliminate redundancy in order to improve the accuracy of fault diagnosis. Finally, we explain the engineered features and evaluate the selected models by using the Gain Chart and Area Under Curve (AUC) metrics. We use the hybrid expert system model to enhance maintenance planning decisions by assigning a health score to the railcar system of the North American Railcar Owner (NARO). According to the experimental results, our expert model can detect 96.4% of failures within 50% of the sample. This suggests that our method is effective at diagnosing failures in railcars fleet.Comment: 21 pages, 7 figures, 3 table

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201