Derailment detection and data collection in freight trains, based on a wireless sensor network

We report the development of a network of wireless ultralow-power sensors to be deployed on freight railway cars, with the main purpose of detecting derailment events and alerting the engineer in the cab of the leading locomotive. Because no power bus is available on freight cars, we plan to rely on energy scavenging from vibrations; therefore, minimization of the power consumption has been one of our main priorities. We have, therefore, focused on ultralow-power hardware and strived to reduce the time intervals during which it is in active mode, achieving an average power consumption of ~0.5 mW with an active cycle of ~20 ms every 2 s. We discuss the overall concept that we propose, including the self-initialization protocol and the communication strategy that we have developed, and present the results of measurements on a prototype network that we have implemented

Railcar Wheel Impact Detection Utilizing Vibration-Based Wireless Onboard Condition Monitoring Modules

The current limitations in established rail transport condition monitoring methods have motivated the UTCRS railway research team at UTRGV to investigate a novel solution that can address these deficiencies through wired, onboard, and vibration-based analytics. Due to the emergence of the Internet of Things (IoT), the research team has now transitioned into developing wireless modules that take advantage of the rapid data processing and wireless communication features these systems possess. This has enabled UTCRS to partner with Hum Industrial Technology, Inc. to assist them in the development of their “Boomerang” wireless condition monitoring system. Designed to revolutionize the way the railway industry monitors rolling stock assets; the product is intended to provide preemptive maintenance scheduling through the continuous monitoring of railcar wheels and bearings. Ultimately, customers can save time, money, and avoid potentially catastrophic events. The wheel condition monitoring capabilities of the Boomerang were evaluated through various laboratory experiments that mimicked rail service operating conditions. The possible optimization of the system by incorporating a filter was also investigated. To further validate the efficacy of the prototype, a pilot field test consisting of 40 modules was conducted. The exhibited agreement between the laboratory and field pilot test data as well as the detection of a faulty wheelset demonstrates the functionality of the sensor module as a railcar wheel health monitoring device

Wireless Sensor Networks for Condition Monitoring in the Railway Industry : a Survey

In recent years, the range of sensing technologies has expanded rapidly, whereas sensor devices have become cheaper. This has led to a rapid expansion in condition monitoring of systems, structures, vehicles, and machinery using sensors. Key factors are the recent advances in networking technologies such as wireless communication and mobile adhoc networking coupled with the technology to integrate devices. Wireless sensor networks (WSNs) can be used for monitoring the railway infrastructure such as bridges, rail tracks, track beds, and track equipment along with vehicle health monitoring such as chassis, bogies, wheels, and wagons. Condition monitoring reduces human inspection requirements through automated monitoring, reduces maintenance through detecting faults before they escalate, and improves safety and reliability. This is vital for the development, upgrading, and expansion of railway networks. This paper surveys these wireless sensors network technology for monitoring in the railway industry for analyzing systems, structures, vehicles, and machinery. This paper focuses on practical engineering solutions, principally,which sensor devices are used and what they are used for; and the identification of sensor configurations and network topologies. It identifies their respective motivations and distinguishes their advantages and disadvantages in a comparative review

An autonomous, low cost, distributed method for observing vehicle track interactions

Experience and field studies have shown that track geometry alone is not a good predictor of rail vehicle response. This paper describes a family of "Health Card" devices - an autonomous device that can be distributed on rolling stock to analyse the vehicle responses. As a distributed system is desired, and the intent is to apply this technology widely across a vehicle fleet, a low initial capital cost and low operating cost solution is desirable. As a consequence the Health Card performs all its sensing operations on the car body and avoids the costs and complications of sensing below the car body especially on unsprung components. Health Cards use solid-state transducers including accelerometers and angular rate sensors with a coordinate transform to resolve car body motions into six degrees of freedom. They then apply spectrogram techniques to obtain a time-frequency representation of the car body motion. These representations are autonomously analyzed to detect and classify transient dynamic events and to infer track degradation or operational risk

Study of an onboard wired-wireless health monitoring system equipped with power save algorithm for freight railway wagons

Goods transport is an essential factor for the European market for its significant contribution to economic growth and thus to the creation of new employment. Nowadays, approximately 75% of goods are transported by road within the European Union. The use of more efficient and sustainable modes of transportation, such as rail transport and inland waterways, would reduce oil imports and pollution abatement. The growth of rail goods transport must be accompanied by an increasing introduction of tools and technologies that make possible to constantly monitor the European rolling stock. The introduction of monitoring technologies that allow to constantly know the status of the wagon would bring real and concrete benefits to the world of rail transport enabling to optimize the maintenance of rolling stock thus reducing costs but ensuring at the same time a maximization of the safety. Currently, the only information available are provided by the equipment installed along the railway network, separated by tens of kilometers. However, to identify and intervene on an incipient failure, it is necessary to have continuous monitoring and a communication system that can warn the train conductor and the maintenance staff of wagon’s owners. A good monitoring system has to be: cheap, energy autonomous, wireless and reliable. Currently monitoring systems can be divided into two large groups. The former are those developed by universities or research centres within projects financed by third parties, while the latter are monitoring systems developed individually by companies operating in the logistics sector. In light of the existing research projects and products already available on the market, the following thesis work aims to develop a monitoring system demonstrator dedicated to freight wagons that can demonstrate the effectiveness of these devices. The results of preliminary literature and market analyses served as the base for the realization of a first wired demonstrator. All the subsystems of the first demonstrator were long tested in laboratory in order to guarantee the maximum reliability of the device and maximum repeatability of the recorded data. The parameters monitored were the pressures of the pneumatic braking system, the temperature of the cast-iron brake blocks and the dynamics of the body frame. The second demonstrator developed was significantly more complex. In fact, it consists of two wireless units: a base station which represents the further development of the first demonstrator and a completely new axle box node monitoring system. From the analysis of the brake block temperature data two fundamental aspects emerge. The first is the need and importance of maintaining the braking system always in good conditions, doing maintenance in line with the regulations. The second is related to the adoption of new brake blocks in synthetic material. In fact, in addition to the complete review of the brake system as prescribed by the regulation, also the material of the wheelsets must be suitable for the use of new type of brake blocks. Another aspect subject to monitoring in this work is the vibration monitoring. Vibrations of particular interest for freight wagon monitoring are those along the vertical axis and the longitudinal axis. The accelerations along the vertical axis in fact describe the stability of the vehicle and its interaction with the rails. Vertical acceleration is a parameter that allows to determine if the wagon is traveling safely or not. In fact, this parameter makes it possible to identify a possible derailment, if the acceleration level recorded is anomalous. The longitudinal acceleration is a parameter monitored by all the railway monitoring devices present on the market. It is important to know the longitudinal accelerometric levels both in the phases of train composition and during the braking operations in order to identify possible incorrect behaviour. The second demonstrator allowed to monitor the external temperature of the axle box cover and verified the correct behaviour of bearings. The most important result of the second demonstrator was the creation of a wireless network that makes it possible to monitor any quantities without invasive wiring. The creation of a wireless network has also required the development of power saving algorithms for the reduction of energy consumption in order to obtain the maximum operating time. In both prototypes developed, the monitored parameters were very numerous and were sampled with a very high frequency, especially those related to temperatures and pressures. This is a typical feature of the demonstrators. Instead, in order to monitor and study the phenomena related to the dynamics of the wagon it is necessary a sampling frequency as the one adopted. The developed prototypes, even if marked by a strong manual activity, have shown a very high reliability. Monitoring all these parameters for such a long distance led to the creation of a large database. Generally, only large industrial groups can boast such prolonged tests. The prototypes made, thanks to their hardware and software effectiveness, were the basis for the most complex monitoring system that we have set ourselves to achieve with the SWAM Rail project. In conclusion, the project carried out in these three years has therefore obtained as results the realization of demonstrators of monitoring devices, the collection of data that would allow to understand and study the operation of a wagon in optimal maintenance conditions, the development of thermal models and the identification of threshold parameters for delimiting conditions of normal operation by fault conditions

Quantifying the damage of in-service rolling stock wheelsets using remote condition monitoring

The global railway network is set to continue to expand in terms of size, passenger numbers and freight tonnage in the coming decades. The occurrence of derailments can lead to major network disruption, significant financial losses, damage to infrastructure and rolling stock assets, environmental damage, and possibly fatalities and injuries. Defects in rolling stock wheelsets can potentially result in severe derailments if left to grow to a critical level. Rolling stock wheelsets are maintained using preventative maintenance techniques. Predictive maintenance solutions prevent unexpected failure, boost operational efficiency, and lower costs. The railway industry has been looking into the development of advanced and effective condition monitoring with a low capital cost for the online and real-time assessment of the rolling stock wheels' structural integrity and subcomponents (wheels, bearings, brakes and suspension). Existing wayside measurement systems are based on different technologies, including hot boxes, acoustic arrays, wheel impact load detectors, etc. However, significant flaws, especially bearing failures, are challenging to identify. Hot boxes can only detect bad bearings after they overheat. This indicates that the bearing has failed and will be seized soon. The combination of acoustic emission (AE) and vibration analysis has been used in this study to identify wheelset defects, particularly in wheels and axle bearings. Based on the new approach and thanks to the capability of early fault detection, predictive maintenance methods can be effectively applied whilst minimising the risk of catastrophic failure and reducing the level of disruption to an absolute minimum. The present study looked into the quantitative evaluation of damage in axle bearings using an advanced customised vibroacoustic remote condition monitoring system developed at the University of Birmingham to improve the early fault detectability in in-service rolling stock wheelsets and improve maintenance planning. Laboratory tests using AE sensors and accelerometers were conducted to compare the sensitivity of each technique and evaluate the synergy in combining them. An experiment using the Amsler machine and bearing test rig proved that raw data and Fast Fourier transform (FFT) are inefficient for defect detection. More advanced signal processing techniques, including Kurtosis, were also applied to find the ideal core frequency and bandwidth for a band-pass filter. Cepstral analysis determines the complex natural logarithm of data's Fourier transform, and the power spectrum's inverse Fourier transform. It helps identify the bearing defect's harmonics from vibration measurement. High-frequency harmonics arising from wheel and axle bearing faults were proven to be detectable from the acquired AE signals. The trial at Bescot yard demonstrates wayside measurement using a compact data acquisition system. Kurtogram-based band-pass filters eliminate environmental and undesired vibrations. The filtered signal with a better signal-to-noise ratio has less noise than the original signal. Another real-world wayside measurement was conducted at the Cropredy site to demonstrate train and wheelset defect detection

Modeling and Monitoring of the Dynamic Response of Railroad Bridges using Wireless Smart Sensors

Railroad bridges form an integral part of railway infrastructure in the USA, carrying approximately 40 % of the ton-miles of freight. The US Department of Transportation (DOT) forecasts current rail tonnage to increase up to 88 % by 2035. Within the railway network, a bridge occurs every 1.4 miles of track, on average, making them critical elements. In an effort to accommodate safely the need for increased load carrying capacity, the Federal Railroad Association (FRA) announced a regulation in 2010 that the bridge owners must conduct and report annual inspection of all the bridges. The objective of this research is to develop appropriate modeling and monitoring techniques for railroad bridges toward understanding the dynamic responses under a moving train. To achieve the research objective, the following issues are considered specifically. For modeling, a simple, yet effective, model is developed to capture salient features of the bridge responses under a moving train. A new hybrid model is then proposed, which is a flexible and efficient tool for estimating bridge responses for arbitrary train configurations and speeds. For monitoring, measured field data is used to validate the performance of the numerical model. Further, interpretation of the proposed models showed that those models are efficient tools for predicting response of the bridge, such as fatigue and resonance. Finally, fundamental software, hardware, and algorithm components are developed for providing synchronized sensing for geographically distributed networks, as can be found in railroad bridges. The results of this research successfully demonstrate the potentials of using wirelessly measured data to perform model development and calibration that will lead to better understanding the dynamic responses of railroad bridges and to provide an effective tool for prediction of bridge response for arbitrary train configurations and speeds.National Science Foundation Grant No. CMS-0600433National Science Foundation Grant No. CMMI-0928886National Science Foundation Grant No. OISE-1107526National Science Foundation Grant No. CMMI- 0724172 (NEESR-SD)Federal Railroad Administration BAA 2010-1 projectOpe

Towards the Internet of Smart Trains: A Review on Industrial IoT-Connected Railways

[Abstract] Nowadays, the railway industry is in a position where it is able to exploit the opportunities created by the IIoT (Industrial Internet of Things) and enabling communication technologies under the paradigm of Internet of Trains. This review details the evolution of communication technologies since the deployment of GSM-R, describing the main alternatives and how railway requirements, specifications and recommendations have evolved over time. The advantages of the latest generation of broadband communication systems (e.g., LTE, 5G, IEEE 802.11ad) and the emergence of Wireless Sensor Networks (WSNs) for the railway environment are also explained together with the strategic roadmap to ensure a smooth migration from GSM-R. Furthermore, this survey focuses on providing a holistic approach, identifying scenarios and architectures where railways could leverage better commercial IIoT capabilities. After reviewing the main industrial developments, short and medium-term IIoT-enabled services for smart railways are evaluated. Then, it is analyzed the latest research on predictive maintenance, smart infrastructure, advanced monitoring of assets, video surveillance systems, railway operations, Passenger and Freight Information Systems (PIS/FIS), train control systems, safety assurance, signaling systems, cyber security and energy efficiency. Overall, it can be stated that the aim of this article is to provide a detailed examination of the state-of-the-art of different technologies and services that will revolutionize the railway industry and will allow for confronting today challenges.Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; ED431C 2016-045Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; ED341D R2016/012Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; ED431G/01Agencia Estatal de Investigación (España); TEC2013-47141-C4-1-RAgencia Estatal de Investigación (España); TEC2015-69648-REDCAgencia Estatal de Investigación (España); TEC2016-75067-C4-1-

Assessing the Effectiveness and Efficacy of Wireless Onboard Condition Monitoring Modules in Identifying Defects in Railroad Rolling Stock

Many industries have begun shifting into the Internet of Things (IoT) in the 21st century. To help ease this transition for the rail industry, The University Transportation Center for Railway Safety (UTCRS) has partnered with Hum Industrial Technology, Inc. (HUM) to help complete the development of a Wireless Onboard Condition Monitoring system named the ‘Boomerang™’. This product allows customers to schedule proactive maintenance on their railcars to replace defective wheels and/or bearings, saving them time, money and potentially preventing costly catastrophic derailments. The UTCRS research team has established thresholds to determine defective bearings utilizing thermal and mechanical sensors. The effectiveness and efficacy of the Boomerang™ devices to identify different types of defects on bearings were tested and compared against validated UTCRS wired and wireless sensor modules. Laboratory testing was performed on dynamic bearing test rigs that mimic rail service operation. Additionally, a methodical temperature calibration was performed for the Boomerang™ to predict bearing operating temperatures to within 8℃ for a wide range of operating conditions. After a few optimization cycles followed by a functionality verification, 40 Boomerangs were assembled and deployed for a field pilot test. Results of the pilot test confirm the Boomerang™ readiness for field service implementation

A holistic approach to remote condition monitoring for the accurate evaluation of railway infrastructure and rolling stock

The rail industry needs to address a number of important operational challenges in the foreseeable future. First of all, the safety of rail transport needs to be maintained at an absolute maximum matching the achievements of the European airline industry of zero fatalities. Secondly, promote sustainable growth to support increasing demand for both passenger and freight rail transport. Thirdly, support the implementation of measurable innovations and improvements that help increase capacity of current infrastructure through enhanced availability. Finally, maximise the environmentally benign character of railway transport through exploitation of novel technologies such as hydrogen trains and advanced electrification employing renewable energy sources. This project, primarily focused on the UK Rail infrastructure, investigated the benefits arising from a holistic approach in the application of Remote Condition Monitoring (RCM) as a critical means for the accurate, efficient, reliable and cost-effective evaluation of key railway infrastructure assets and rolling stock. This work involved the use of several techniques and innovative methodologies based primarily on Acoustic Emission (AE) and vibration analysis in order to address the evaluation requirements for different components of interest. The results obtained have been very promising and present rail infrastructure managers and rolling stock operators with new opportunities for improved and more reliable operations. This work has led to the instrumentation of multiple sites across the UK rail network enabling measurements to be carried out on various assets under actual operational conditions. At Cropredy an integrated high-frequency vibro-acoustic RCM system has successfully been installed on the Chiltern railway line on the way from London to Birmingham. This customised system has been fully operational since 2015 measuring more than 200 passenger and freight trains every day moving at speeds up to 100 miles per hour (MPH). Prior to the installation of the system at Cropredy a Certificate (PA05/06524) of Acceptance was issued by Network Rail which after being renewed recently is now valid until September 2021. The system is due for an upgrade in the following stage of development, employing wireless sensors and advanced energy harvesting devices which are being developed under a collaborative Engineering and Physical Sciences Research Council (EPSRC) project between Exeter and Birmingham Universities, Network Rail, Swiss Approval UK and Quatrro. The widespread implementation of the techniques and methodologies researched will give rise to significant potential impact with respect to the effectiveness of maintenance strategies, particularly in terms of cost efficiency, improved availability of railway assets and better planning of available resources. As modern rail transport moves towards 24-hour railway, the inspection, maintenance and track renewal and upgrade regime will need to be re-thought at a fundamental level. Effective RCM will be a key factor in realistically enabling true round the clock operations. The results presented in this thesis have been part of a six-year research effort with a clear focus on addressing the true industrial need. The findings of this work have led to a re-think within Network Rail regarding the new possibilities arising from the effective use of RCM in designing and implementing more efficient and cost-effective railway operations whilst helping reduce the cost. The use of autonomous sensing systems in the future will change the inspection and maintenance strategies currently used shifting towards a truly prognostic operational strategy