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

Nondestructive Testing in Composite Materials

In this era of technological progress and given the need for welfare and safety, everything that is manufactured and maintained must comply with such needs. We would all like to live in a safe house that will not collapse on us. We would all like to walk on a safe road and never see a chasm open in front of us. We would all like to cross a bridge and reach the other side safely. We all would like to feel safe and secure when taking a plane, ship, train, or using any equipment. All this may be possible with the adoption of adequate manufacturing processes, with non-destructive inspection of final parts and monitoring during the in-service life of components. Above all, maintenance should be imperative. This requires effective non-destructive testing techniques and procedures. This Special Issue is a collection of some of the latest research in these areas, aiming to highlight new ideas and ways to deal with challenging issues worldwide. Different types of materials and structures are considered, different non-destructive testing techniques are employed with new approaches for data treatment proposed as well as numerical simulations. This can serve as food for thought for the community involved in the inspection of materials and structures as well as condition monitoring

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

DEVELOPMENT OF A NOVEL VEHICLE GUIDANCE SYSTEM: VEHICLE RISK MITIGATION AND CONTROL

Over a half of fatal vehicular crashes occur due to vehicles leaving their designated travel lane and entering other lanes or leaving the roadway. Lane departure accidents also result in billions of dollars in cost to society. Recent vehicle technology research into driver assistance and vehicle autonomy has developed to assume various driving tasks. However, these systems are do not work for all roads and travel conditions. The purpose of this research study was to begin the development a novel vehicle guidance approach, specifically studying how the vehicle interacts with the system to detect departures and control the vehicle A literature review was conducted, covering topics such as vehicle sensors, control methods, environment recognition, driver assistance methods, vehicle autonomy methods, communication, positioning, and regulations. Researchers identified environment independence, recognition accuracy, computational load, and industry collaboration as areas of need in intelligent transportation. A novel method of vehicle guidance was conceptualized known as the MwRSF Smart Barrier. The vision of this method is to send verified road path data, based AASHTO design and vehicle dynamic aspects, to guide the vehicle. To further development research was done to determine various aspects of vehicle dynamics and trajectory trends can be used to predict departures and control the vehicle. Tire-to-road friction capacity and roll stability were identified as traits that can be prevented with future road path knowledge. Road departure characteristics were mathematically developed. It was shown that lateral departure, orientation error, and curvature error are parametrically linked, and discussion was given for these metrics as the basis for of departure prediction. A three parallel PID controller for modulating vehicle steering inputs to a virtual vehicle to remain on the path was developed. The controller was informed by a matrix of XY road coordinates, road curvature and future road curvature and was able to keep the simulated vehicle to within 1 in of the centerline target path. Recommendations were made for the creation of warning modules, threshold levels, improvements to be applied to vehicle controller, and ultimately full-scale testing. Advisor: Cody S. Stoll

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

Proceedings of COMADEM 201

Smart Energy and Intelligent Transportation Systems

With the Internet of Things and various information and communication technologies, a city can manage its assets in a smarter way, constituting the urban development vision of a smart city. This facilitates a more efficient use of physical infrastructure and encourages citizen participation. Smart energy and smart mobility are among the key aspects of the smart city, in which the electric vehicle (EV) is believed to take a key role. EVs are powered by various energy sources or the electricity grid. With proper scheduling, a large fleet of EVs can be charged from charging stations and parking infrastructures. Although the battery capacity of a single EV is small, an aggregation of EVs can perform as a significant power source or load, constituting a vehicle-to-grid (V2G) system. Besides acquiring energy from the grid, in V2G, EVs can also support the grid by providing various demand response and auxiliary services. Thanks to this, we can reduce our reliance on fossil fuels and utilize the renewable energy more effectively. This Special Issue “Smart Energy and Intelligent Transportation Systems” addresses existing knowledge gaps and advances smart energy and mobility. It consists of five peer-reviewed papers that cover a range of subjects and applications related to smart energy and transportation

Online condition monitoring of railway wheelsets

The rail industry has focused on the improvement of maintenance through the effective use of online condition monitoring of rolling stock and rail infrastructure in order to reduce the occurrence of unexpected catastrophic failures and disruption that arises from them to an absolute minimum. The basic components comprising a railway wheelset are the wheels, axle and axle bearings. Detection of wheelset faults in a timely manner increases efficiency as it helps minimise maintenance costs and increase availability. The main aim of this project has been the development of a novel integrated online acoustic emission (AE) and vibration testing technique for the detection of wheel and axle bearing defects as early as possible and well before they result in catastrophic failure and subsequently derailment. The approach employed within this research study has been based on the combined use of accelerometers and high-frequency acoustic emission sensors mounted on the rail or axle box using magnetic hold-downs. Within the framework of this project several experiments have been carried out under laboratory conditions, as well as in the field at the Long Marston Test Track and in Cropredy on the Chiltern Railway line to London

Automated Fault Diagnosis in Rotating Machinery

Rotating machinery are an important part of industrial equipment. Their components are subjected to harsh operating environments, and hence experience significant wear and tear. It is necessary that they function efficiently all the time in order to avoid significant monetary losses and down-time. Monitoring the health of such machinery components has become an essential part in many industries to ensure their continuous operation and avoiding loss in productivity. Traditionally, signal processing methods have been employed to analyze the vibration signals emitted from rotating machines. With time, the complexity of machinery components has increased, which makes the process of condition monitoring complex and time consuming, and consequently costly. Hence, a paradigm shift in condition monitoring methods towards data-driven approaches has recently taken place towards reducing complexity in estimation, where the monitoring of machinery is focused on purely data-driven methods. In this thesis, a novel data-driven framework to condition monitoring of gearbox is studied and illustrated using simulated and experimental vibration signals. This involves analyzing the signal, deriving feature sets and using machine learning algorithms to discern the condition of machinery. The algorithm is implemented on data from a drivetrain dynamics simulator (DDS), equipment designed by Spectraquest Inc. for academic and industrial research purposes. Datasets from pristine state and faulty gearboxes are collected and the algorithms are tested against this data. This framework has been developed to facilitate automated monitoring of machinery in industries, thus reducing the need for manual supervision and interpretation

Advanced Turbine Technology Applications Project (ATTAP)

Work to develop and demonstrate the technology of structural ceramics for automotive engines and similar applications is described. Long-range technology is being sought to produce gas turbine engines for automobiles with reduced fuel consumption and reduced environmental impact. The Advanced Turbine Technology Application Project (ATTAP) test bed engine is designed such that, when installed in a 3,000 pound inertia weight automobile, it will provide low emissions, 42 miles per gallon fuel economy on diesel fuel, multifuel capability, costs competitive with current spark ignition engines, and noise and safety characteristics that meet Federal standards

Integrated railway remote condition monitoring

The profound value of wayside monitoring in helping safeguard the RAMS of railway operations is undeniable. However, despite significant investments by the rail industry, the efficiency and reliability of wayside monitoring have not reached the desired level. Structural deterioration of the rail infrastructure and rolling stock faults still remain a significant problem which needs to be addressed as traffic density, train speeds and axle loads increase in rail networks around the world. The main objectives of this study were to develop and evaluate an advanced wayside monitoring system based on acoustic emission and vibration analysis that can detect various types of axle bearing defects in rolling stock and structural deterioration in cast manganese crossings. The potential architecture for different levels of system correlation has been proposed which can be further integrated with customised monitoring system. A novel signal processing technique based on spectral coherence has been developed. This particular method is based on the identification of suitable templates containing features of interest. It also features in identifying the severity of the defect. In addition, a suitable approach for data fusion from various sensors has been investigated. Successful tests have been carried out under simulated conditions and in the UK network