Predicting the traction power of metropolitan railway lines using different machine learning models

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Rail Transportation on 2021, available online: http://www.tandfonline.com/10.1080/23248378.2020.1829513[EN] Railways are an efficient transport mean with lower energy consumption and emissions in comparison to other transport means for freight and passengers, and yet there is a growing need to increase their efficiency. To achieve this, it is needed to accurately predict their energy consumption, a task which is traditionally carried out using deterministic models which rely on data measured through money- and time-consuming methods. Using four basic (and cheap to measure) features (train speed, acceleration, track slope and radius of curvature) from MetroValencia (Spain), we predicted the traction power using different machine learning models, obtaining that a random forest model outperforms other approaches in such task. The results show the possibility of using basic features to predict the traction power in a metropolitan railway line, and the chance of using this model as a tool to assess different strategies in order to increase the energy efficiency in these lines.This work was supported by the Ministerio de Economia y Competitividad [TRA2011-26602].Pineda-Jaramillo, J.; Martínez Fernández, P.; Villalba Sanchis, I.; Salvador Zuriaga, P.; Insa Franco, R. (2021). Predicting the traction power of metropolitan railway lines using different machine learning models. International Journal of Rail Transportation. 9(5):461-478. https://doi.org/10.1080/23248378.2020.1829513S4614789

Development of a new approach for predicting tram track degradation based on passenger ride/comfort data

These days tram as a type of the public transport system has become popular because of its attractive features such as road usage efficiency, low emission of pollutants, reduction in traffic congestion and efficiency in capital costs and maintenance expenses compared to private cars. For the case study, the Melbourne tram network, which is the longest tram network in the world, has been targeted. Melbourne tram system consists of 493 trams, 24 routes, and 1,763 tram stops. According to the operator of the Melbourne tram network, the total number of patronage in 2017-2018 was 206.3 million. In parallel with the annual increase in tram demand and patronage, tram infrastructure systems need to bear more stresses and traffic pressure. Track degradation is a common problem in the area of tram track infrastructure. One of the main aspects of track degradation is the presence of irregularity in track geometric parameters. In order to deal with degradation problems, tram track infrastructure maintenance management systems have been developed for design and implementation of maintenance works and renewal activities. Track degradation prediction models are the core and the main part of these management systems. Without accurately predicting the future condition of tram tracks, designing and providing preventive maintenance strategies are not feasible. In this research, the collected data which cover six sequential years (2010 to 2015) have been analysed and influencing parameters in tram track degradation have been identified. Gauge and twist were identified as the influencing track geometry parameters in the tram track degradation. Besides that, track surface and rail support as structural parameters were identified as significant parameters in prediction of future track geometry parameters and consequently tram track degradation. In order to develop tram track degradation prediction models and according to the successful experience of the previous studies, three types of prediction models including Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest Regression (RFR) models have been created. According to the results, RFR models provide better predictions in terms of the performance indicators including the coefficient of determination and Root Mean Squared Error (RMSE) compared to the ANN and SVM models. In this research, based on the Melbourne tram track dataset, a new track degradation index has been proposed. Track degradation indices can be used as an indicator of rail condition concerning the risk of damage or failure over a period of time. The index can be applied in establishing a sustainable tram track maintenance management system. The new index composed of two main parts including the mean value of the geometry deviation and the average differential geometry deviation. The proposed index has been compared with three major track geometry degradation indices. For this purpose, the predictability performance of the indices has been considered. In this regard, the Pearson correlation analysis was applied to previous and current values of the indices. According to the results, the correlation coefficient of the proposed index was higher than the other indices. The finding of the evaluation presented that the proposed index can be used as an effective measure for the assessment of the geometric condition of tram tracks. In this research, a new approach has been proposed to predict the tram track degradation were which is cost-effective and can be carried out repeatedly without imposing delay to tram services. Conventional approaches are mainly based on the previous track geometry parameters which have been discussed in this research. In the new approach, passenger ride comfort data or acceleration data has been used to predict the future condition of track geometry parameters which has been represented by the tram track degradation index. For developing the degradation prediction models, the previous models which have been used to predict the degradation based on the track geometry parameters were applied. The future degradation index has been targeted as the target variable and acceleration parameter besides the structural parameters have been used as the explanatory variables. According to the results of the evaluation, the RFR model can predict the future degradation index with approximately 10 percent higher R2 and 9 percent lower prediction error compared to other developed models. In this research two methods for predicting the future tram track degradation index, first was the method based on the previous track geometry parameters and the second was the method based on the acceleration data, have been presented. According to the results of the degradation index prediction based on the previous track geometry parameters, RMSE was 0.35 and R2 value was 0.95. On the other hand, for the prediction based on the acceleration data, RMSE was 1.04 and R2 value was 0.74. The comparison of these methods shows that although the prediction error has been increased and R2 value has been decreased in the latest method, the values of the performance indicators are still in acceptable ranges. These results imply that the prediction of tram track degradation based on the acceleration data can be considered as a reliable method along with conventional tram track degradation prediction method for maintaining tram tracks. The proposed method can provide more predictions of potential future faults by reducing inspection costs and inspection intervals

Statistical modelling of railway track geometry degradation using hierarchical Bayesian models

Railway maintenance planners require a predictive model that can assess the railway track geometry degradation. The present paper uses a hierarchical Bayesian model as a tool to model the main two quality indicators related to railway track geometry degradation: the standard deviation of longitudinal level defects and the standard deviation of horizontal alignment defects. Hierarchical Bayesian Models (HBM) are flexible statistical models that allow specifying different spatially correlated components between consecutive track sections, namely for the deterioration rates and the initial qualities parameters. HBM are developed for both quality indicators, conducting an extensive comparison between candidate models and a sensitivity analysis on prior distributions. HBM is applied to provide an overall assessment of the degradation of railway track geometry, for the main Portuguese railway line Lisbon-Oporto

Heterogeneous machine learning ensembles for predicting train delays

Train delays have been a serious persisting problem in the UK and also many other countries. Due to increasing demand, rail networks are running close to their full capacity. As a consequence, an initial delay can cause many knock-on delays to other trains, and this is the main reason for the overall deterioration in the performance of the rail networks. Therefore, it is really useful to have an AI-based method that can predict delays accurately and reliably, to help train controllers to make and apply alternative plans in time to reduce or prevent further delays, when a delay occurs. However, existing machine learning models are not only inaccurate but more importantly unreliable. In this study, we have proposed a new approach to build heterogeneous ensembles with two novel model selection methods based on accuracy and diversity. We tested our heterogeneous ensembles using the real-world data and the results indicated that they are more accurate and robust than single models and state-of-the-art homogeneous ensembles, e.g. Random Forest and XGBoost. We then verified their performances with an independent dataset from a different train operating company and found that they achieved the consistent and accurate results

Anomaly Detection in Industrial Machinery using IoT Devices and Machine Learning: a Systematic Mapping

Anomaly detection is critical in the smart industry for preventing equipment failure, reducing downtime, and improving safety. Internet of Things (IoT) has enabled the collection of large volumes of data from industrial machinery, providing a rich source of information for Anomaly Detection. However, the volume and complexity of data generated by the Internet of Things ecosystems make it difficult for humans to detect anomalies manually. Machine learning (ML) algorithms can automate anomaly detection in industrial machinery by analyzing generated data. Besides, each technique has specific strengths and weaknesses based on the data nature and its corresponding systems. However, the current systematic mapping studies on Anomaly Detection primarily focus on addressing network and cybersecurity-related problems, with limited attention given to the industrial sector. Additionally, these studies do not cover the challenges involved in using ML for Anomaly Detection in industrial machinery within the context of the IoT ecosystems. This paper presents a systematic mapping study on Anomaly Detection for industrial machinery using IoT devices and ML algorithms to address this gap. The study comprehensively evaluates 84 relevant studies spanning from 2016 to 2023, providing an extensive review of Anomaly Detection research. Our findings identify the most commonly used algorithms, preprocessing techniques, and sensor types. Additionally, this review identifies application areas and points to future challenges and research opportunities