Adaptive noise cancelling and time–frequency techniques for rail surface defect detection

Adaptive noise cancelling (ANC) is a technique which is very effective to remove additive noises from the contaminated signals. It has been widely used in the fields of telecommunication, radar and sonar signal processing. However it was seldom used for the surveillance and diagnosis of mechanical systems before late of 1990s. As a promising technique it has gradually been exploited for the purpose of condition monitoring and fault diagnosis. Time-frequency analysis is another useful tool for condition monitoring and fault diagnosis purpose as time-frequency analysis can keep both time and frequency information simultaneously. This paper presents an ANC and time-frequency application for railway wheel flat and rail surface defect detection. The experimental results from a scaled roller test rig show that this approach can significantly reduce unwanted interferences and extract the weak signals from strong background noises. The combination of ANC and time-frequency analysis may provide us one of useful tools for condition monitoring and fault diagnosis of railway vehicles

Vibration induced phase noise in Mach-Zehnder atom interferometers

The high inertial sensitivity of atom interferometers has been used to build accelerometers and gyrometers but this sensitivity makes these interferometers very sensitive to the laboratory seismic noise. This seismic noise induces a phase noise which is large enough to reduce the fringe visibility in many cases. We develop here a model calculation of this phase noise in the case of Mach-Zehnder atom interferometers and we apply this model to our thermal lithium interferometer. We are thus able to explain the observed dependence of the fringe visibility with the diffraction order. The dynamical model developed in the present paper should be very useful to further reduce this phase noise in atom interferometers and this reduction should open the way to improved interferometers

Contemporary Inspection and Monitoring for High-Speed Rail System

Non-destructive testing (NDT) techniques have been explored and extensively utilised to help maintaining safety operation and improving ride comfort of the rail system. As an ascension of NDT techniques, the structural health monitoring (SHM) brings a new era of real-time condition assessment of rail system without interrupting train service, which is significantly meaningful to high-speed rail (HSR). This chapter first gives a review of NDT techniques of wheels and rails, followed by the recent applications of SHM on HSR enabled by a combination of advanced sensing technologies using optical fibre, piezoelectric and other smart sensors for on-board and online monitoring of the railway system from vehicles to rail infrastructure. An introduction of research frontier and development direction of SHM on HSR is provided subsequently concerning both sensing accuracy and efficiency, through cutting-edge data-driven analytic studies embracing such as wireless sensing and compressive sensing, which answer for the big data’s call brought by the new age of this transport

Acoustic monitoring of rail faults in the German railway network

The early detection of rail surface defects such as squats, poor welds, or wheel burns is important to prevent further rail deterioration. In this paper, a methodology for acoustic monitoring of squats in the German railway network is proposed based on the measurement of axle box acceleration (ABA) on the DB noise measurement car (SMW) and the previously developed numerical model WERAN for wheel/rail interaction. Specific characteristics of squats in the ABA signals are determined with the model and verified by pass-by measurements combined with direct geometry measurements of the squats. Based on these re- sults, a logistic regression classifier is devised for the detection of squats in the measured ABA signals of the SMW. Trained with simulated and measured data, the classifier identifies all of the known severe squats and 87% of the known light squats in the measured test data

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

Condition monitoring of the rolling stock and infrastructure: results of a pilot project

The application of Prognostics and Health Monitoring (PHM) concepts in rail vehicles and railway infrastructure is a rapidly growing field of research, and extensive efforts are being spent with the aim of improving the reliability and availability of railway systems and of substantially reducing maintenance costs by switching from time-based to event-driven maintenance policies. This paper presents the results of a research project in which concepts were developed and demonstrated for the health monitoring of the rolling stock (traction equipment) and of the railway infrastructure (track and overhead equipment). A prototype monitoring system was installed on a E464 locomotive and results were gathered across a time span of 14 months from December 2014 to January 2016

Damage identification in warren truss bridges by two different time–frequency algorithms

Recently, a number of authors have been focusing on drive-by monitoring methods, exploiting sensors mounted on the vehicle rather than on the bridge to be monitored, with clear advantages in terms of cost and flexibility. This work aims at further exploring the feasibility and effectiveness of novel tools for indirect health monitoring of railway structures, by introducing a higher level of accuracy in damage modelling, achieve more close-to-reality results. A numerical study is carried out by means of a FE 3D model of a short span Warren truss bridge, simulating the dynamic interaction of the bridge/track/train structure. Two kinds of defects are simulated, the first one affecting the connection between the lower chord and the side diagonal member, the second one involving the joint between the cross-girder and the lower chord. Accelerations gathered from the train bogie in different working conditions and for different intensities of the damage level are analyzed through two time-frequency algorithms, namely Continuous Wavelet and Huang-Hilbert transforms, to evaluate their robustness to disturbing factors. Compared to previous studies, a complete 3D model of the rail vehicle, together with a 3D structural scheme of the bridge in place of the 2D equivalent scheme widely adopted in the literature, allow a more detailed and realistic representation of the effects of the bridge damage on the vehicle dynamics. Good numerical results are obtained from both the two algorithms in the case of the time-invariant track profile, whereas the Continuous Wavelet Transform is found to be more robust when a deterioration of track irregularity is simulated

Railway Axle Condition Monitoring Technique Based on Wavelet Packet Transform Features and Support Vector Machines

Railway axles are critical to the safety of railway vehicles. However, railway axle maintenance is currently based on scheduled preventive maintenance using Nondestructive Testing. The use of condition monitoring techniques would provide information about the status of the axle between periodical inspections, and it would be very valuable in the prevention of catastrophic failures. Nevertheless, in the literature, there are not many studies focusing on this area and there is a lack of experimental data. In this work, a reliable real-time condition-monitoring technique for railway axles is proposed. The technique was validated using vibration measurements obtained at the axle boxes of a full bogie installed on a rig, where four different cracked railway axles were tested. The technique is based on vibration analysis by means of the Wavelet Packet Transform (WPT) energy, combined with a Support Vector Machine (SVM) diagnosis model. In all cases, it was observed that the WPT energy of the vibration signals at the first natural frequency of the axle when the wheelset is first installed (the healthy condition) increases when a crack is artificially created. An SVM diagnosis model based on the WPT energy at this frequency demonstrates good reliability, with a false alarm rate of lower than 10% and defect detection for damage occurring in more than 6.5% of the section in more than 90% of the cases. The minimum number of wheelsets required to build a general model to avoid mounting effects, among others things, is also discussed.This research was funded by the Spanish Government through the project MAQSTATUS with grantnumber DPI2015-69325-C2-1-R