Railway track component condition monitoring using optical fibre Bragg grating sensors

The use of optical fibre Bragg grating (FBG) strain sensors to monitor the condition of safety critical rail components is investigated. Fishplates, switchblades and stretcher bars on the Stagecoach Supertram tramway in Sheffield in the UK have been instrumented with arrays of FBG sensors. The dynamic strain signatures induced by the passage of a tram over the instrumented components have been analysed to identify features indicative of changes in the condition of the components

Prognostics and health management for maintenance practitioners - Review, implementation and tools evaluation.

In literature, prognostics and health management (PHM) systems have been studied by many researchers from many different engineering fields to increase system reliability, availability, safety and to reduce the maintenance cost of engineering assets. Many works conducted in PHM research concentrate on designing robust and accurate models to assess the health state of components for particular applications to support decision making. Models which involve mathematical interpretations, assumptions and approximations make PHM hard to understand and implement in real world applications, especially by maintenance practitioners in industry. Prior knowledge to implement PHM in complex systems is crucial to building highly reliable systems. To fill this gap and motivate industry practitioners, this paper attempts to provide a comprehensive review on PHM domain and discusses important issues on uncertainty quantification, implementation aspects next to prognostics feature and tool evaluation. In this paper, PHM implementation steps consists of; (1) critical component analysis, (2) appropriate sensor selection for condition monitoring (CM), (3) prognostics feature evaluation under data analysis and (4) prognostics methodology and tool evaluation matrices derived from PHM literature. Besides PHM implementation aspects, this paper also reviews previous and on-going research in high-speed train bogies to highlight problems faced in train industry and emphasize the significance of PHM for further investigations

Real-Time Train Wheel Condition Monitoring by Fiber Bragg Grating Sensors

Wheel defects on trains, such as flat wheels and out-of-roundness, inevitably jeopardize the safety of railway operations. Regular visual inspection and checking by experienced workers are the commonly adopted practice to identify wheel defects. However, the defects may not be spotted in time. Therefore, an automatic, remote-sensing, reliable, and accurate monitoring system for wheel condition is always desirable. The paper describes a real-time system to monitor wheel defects based on fiber Bragg grating sensors. Track strain response upon wheel-rail interaction is measured and processed to generate a condition index which directly reflects the wheel condition. This approach is verified by extensive field test, and the preliminary results show that this electromagnetic-immune system provides an effective alternative for wheel defects detection. The system significantly increases the efficiency of maintenance management and reduces the cost for defects detection, and more importantly, avoids derailment timely

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

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