A Review On Tribological Wear Test Rigs For Various Applications

To mimic a tribosystem setup to the real-time model while taking into consideration the subject of green tribology, many tribological wear test rigs are invented. The current work is a dedicated review on the latest development of various tribological wear test rigs build for numerous applications. With the aid of diagrams, the working principles of the machines which are built on specific standards are discussed. Their operating parameters associated with related research using these machines are also included. In addition to that, recommendations and directions for improvements in tribology machines are reported

Cable-Driven Actuation for Highly Dynamic Robotic Systems

This paper presents design and experimental evaluations of an articulated robotic limb called Capler-Leg. The key element of Capler-Leg is its single-stage cable-pulley transmission combined with a high-gap radius motor. Our cable-pulley system is designed to be as light-weight as possible and to additionally serve as the primary cooling element, thus significantly increasing the power density and efficiency of the overall system. The total weight of active elements on the leg, i.e. the stators and the rotors, contribute more than 60% of the total leg weight, which is an order of magnitude higher than most existing robots. The resulting robotic leg has low inertia, high torque transparency, low manufacturing cost, no backlash, and a low number of parts. Capler-Leg system itself, serves as an experimental setup for evaluating the proposed cable- pulley design in terms of robustness and efficiency. A continuous jump experiment shows a remarkable 96.5 % recuperation rate, measured at the battery output. This means that almost all the mechanical energy output used during push-off returned back to the battery during touch-down

A Survey on Audio-Video based Defect Detection through Deep Learning in Railway Maintenance

Within Artificial Intelligence, Deep Learning (DL) represents a paradigm that has been showing unprecedented performance in image and audio processing by supporting or even replacing humans in defect and anomaly detection. The Railway sector is expected to benefit from DL applications, especially in predictive maintenance applications, where smart audio and video sensors can be leveraged yet kept distinct from safety-critical functions. Such separation is crucial, as it allows for improving system dependability with no impact on its safety certification. This is further supported by the development of DL in other transportation domains, such as automotive and avionics, opening for knowledge transfer opportunities and highlighting the potential of such a paradigm in railways. In order to summarize the recent state-of-the-art while inquiring about future opportunities, this paper reviews DL approaches for the analysis of data generated by acoustic and visual sensors in railway maintenance applications that have been published until August 31st, 2021. In this paper, the current state of the research is investigated and evaluated using a structured and systematic method, in order to highlight promising approaches and successful applications, as well as to identify available datasets, current limitations, open issues, challenges, and recommendations about future research directions

Subsea power cable health management using machine learning analysis of low frequency wide band sonar data

Subsea power cables are critical assets for electrical transmission and distribution networks, and highly relevant to regional, national, and international energy security and decarbonization given the growth in offshore renewable energy generation. Existing condition monitoring techniques are restricted to highly constrained online monitoring systems that only prioritize internal failure modes, representing only 30% of cable failure mechanisms, and has limited capacity to provide precursor indicators of such failures or damages. To overcome these limitations, we propose an innovative fusion prognostics approach that can provide the in situ integrity analysis of the subsea cable. In this paper, we developed low-frequency wide-band sonar (LFWBS) technology to collect acoustic response data from different subsea power cable sample types, with different inner structure configurations, and collate signatures from induced physical failure modes as to obtain integrity data at various cable degradation levels. We demonstrate how a machine learning approach, e.g., SVM, KNN, BP, and CNN algorithms, can be used for integrity analysis under a hybrid, holistic condition monitoring framework. The results of data analysis demonstrate the ability to distinguish subsea cables by differences of 5 mm in diameter and cable types, as well as achieving an overall 95%+ accuracy rate to detect different cable degradation stages. We also present a tailored, hybrid prognostic and health management solution for subsea cables, for cable remaining useful life (RUL) prediction. Our findings addresses a clear capability and knowledge gap in evaluating and forecasting subsea cable RUL. Thus, supporting a more advanced asset management and planning capability for critical subsea power cables

High-Resolution Health Monitoring of Track and Rail Systems with Fiber Optic Sensors and High-Frequency Multiplexed Readouts

Health monitoring of railway systems is critical for detecting incipient faults or degradation. In order to reliably do so, an effective monitoring system must be deployed to provide railroad operators with the highest level of operational awareness and safety. In this study, we explore the use of Fiber Bragg Gratings (FBGs) and a highresolution, low-cost optical readout developed at PARC to interrogate the acoustic emissions generated by a train-rail system. The proposed sensing configuration can allow for a scalable, low-cost, field-deployable solution that could enable near real-time monitoring of tracks and wheels. A proof-of-concept was demonstrated with a G-scale train-rail system with FBGs embedded within the ballast layer. Using PARC’s wavelength shift detector, the acoustic emission signal was resolved in both the time and frequency domain. The findings of this work show promise that this could be a viable solution to deploy an optically-based health monitoring system for railroads

Pervasive Fibre-optic sensor networks in bridges: A UK case study

© 2018 Taylor & Francis Group, London. Integrating fibre-optic sensor networks in a newly-constructed infrastructure assets enables datadriven performance assessment during its construction and throughout its operational life. As part of a multimillion pound railway infrastructure redevelopment project, two new railway bridges were instrumented with an extensive network of both discrete (fibre Bragg gratings or FBGs) and distributed (based on Brillouin optical time domain reflectometry or BOTDR) fibre optic sensors to measure both strain and temperature throughout construction and in-service. Completed in 2016 in Staffordshire UK, both ‘self-sensing’ bridges contain more than 500 fibre Bragg grating sensors and over 600 metres of distributed fibre optic sensor cabling. This paper describes the sensing technologies employed, installation techniques for improving sensing robustness, the monitoring programme and objectives, data processing methods and assumptions, and the primary monitoring findings of this project. Results related to measurements of prestress losses in prestressed concrete girders, estimates of steel girder deflection using FBGs and videogrammetry, and assessments of percentage utilization of critical superstructure elements are presented. In terms of future directions, BIM-based environments which incorporate sensor elements and an emerging field of research known as Data-Centric Engineering are introduced as tools to better manage, maintain and learn from the information generated from self-sensing infrastructure.EPSRC, Innovate UK and the Lloyd's Register Foundation for funding this research through the Centre for Smart Infrastructure and Construction (CSIC) Innovation and Knowledge Centre and the Alan Turing Institute

Improvement of Low Traffic Volume Gravel Roads in Nebraska

In the state of Nebraska, over one-third of roadways are unpaved, and consequently require a significant amount of financial and operational resources to maintain their operation. Undesired behavior of surface gravel aggregates and the road surfaces can include rutting, corrugation, and ponding that may lead to reduced driving safety, speed or network efficiency, and fuel economy. This study evaluates the parameters that characterize the performance and condition of gravel roads overtime period related to various aggregate mix designs. The parameters, including width, slope, and crown profiles, are examples of performance criteria. As remote sensing technologies have advanced in the recent decade, various techniques have been introduced to collect high quality, accurate, and dense data efficiently that can be used for roadway performance assessments. Within this study, two remote sensing platforms, including an unpiloted aerial system (UAS) and ground-based lidar scanner, were used to collect point cloud data of selected roadway sites with various mix design constituents and further processed for digital assessments. Within the assessment process, statistical parameters such as standard deviation, mean value, and coefficient of variance are calculated for the extracted crown profiles. In addition, the study demonstrated that the point clouds obtained from both lidar scanners and UAS derived SfM can be used to characterize the roadway geometry accurately and extract critical information accurately

A novel method of detecting galling and other forms of catastrophic adhesion in tribotests

Tribotests are used to evaluate the performance of lubricants and surface treatments intended for use in industrial applications. They are invaluable tools for lubricant development since many lubricant parameters can be screened in the laboratory with only the best going on to production trials. Friction force or coefficient of friction is often used as an indicator of lubricant performance with sudden increases in friction coefficient indicating failure through catastrophic adhesion. Under some conditions the identification of the point of failure can be a subjective process. This raises the question: Are there better methods for identifying lubricant failure due to catastrophic adhesion that would be beneficial in the evaluation of lubricants? The hypothesis of this research states that a combination of data from various sensors measuring the real-time response of a tribotest provides better detection of adhesive wear than the coefficient of friction alone. In this investigation an industrial tribotester (the Twist Compression Test) was instrumented with a variety of sensors to record: vibrations along two axes, acoustic emissions, electrical resistance, as well as transmitted torsional force and normal force. The signals were collected at 10 kHz for the duration of the tests. In the main study D2 tool steel annular specimens were tested on coldrolled sheet steel at 100 MPa contact pressure in flat sliding at 0.01 m/s. The effects of lubricant viscosity and lubricant chemistry on the adhesive properties of the surface were examined. Tests results were analyzed to establish the apparent point of failure based on the traditional friction criteria. Extended tests of one condition were run to various points up to and after this point and the results analyzed to correlate sensor data with the test specimen surfaces. Sensor data features were used to identify adhesive wear as a continuous process. In particular an increase “friction amplitude” related to a form of stick-slip was used as a key indicator of the occurrence of galling. The findings of this research forms a knowledge base for the development of a decision support system (DSS) to identify lubricant failure based on industrial application requirements.Doctoral These