1,066 research outputs found

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

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    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

    Major challenges in prognostics: study on benchmarking prognostic datasets

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    Even though prognostics has been defined to be one of the most difficult tasks in Condition Based Maintenance (CBM), many studies have reported promising results in recent years. The nature of the prognostics problem is different from diagnostics with its own challenges. There exist two major approaches to prognostics: data-driven and physics-based models. This paper aims to present the major challenges in both of these approaches by examining a number of published datasets for their suitability for analysis. Data-driven methods require sufficient samples that were run until failure whereas physics-based methods need physics of failure progression

    Towards Model-Based Condition Monitoring of Railway Switches and Crossings

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    Railway switches and crossings (S&C, turnouts) connect different track sections and create a railway network by allowing for trains to change between tracks. This functionality comes at a cost as the load-inducing rail discontinuities in the switch and crossing panels cause much higher degradation rates for S&C compared to regular plain line track. The high degradation rates create a potential business case for condition monitoring systems that can allow for improved maintenance decisions compared to what can be achieved from periodic inspection intervals using measurement vehicles or visual inspection by engineers in track. \ua0To this end, this thesis addresses the development of tailored processing tools for the analysis of measured data from accelerometers mounted adjacent to the crossing transition in crossing panels. With the presented tools, a condition monitoring framework is established. The analysis procedures showed robustness in processing large datasets. The framework includes the extraction of different crossing panel condition indicators for which the interpretation is supported by multi-body simulations (MBS) of dynamic train–track interaction. Additionally, a demonstrator is presented for MBS model calibration to the measured track responses.A particularly important signal processing tool is the development of a novel sleeper displacement reconstruction method based on frequency-domain integration. Using the reconstructed displacements, the track response is separated into quasi-static and dynamic domains based on deformation wavelength regions. This separation is shown to be a promising strategy for independent observations of the ballast condition and the crossing rail geometry condition from a single measurement source. In addition to sleeper acceleration measurements, field measurements have been performed in which crossing rail geometries were scanned. The scanned geometries have been implemented into a MBS software with a structural representation of the crossing panel, where analyses have been performed to relate the concurrently measured accelerations and crossing rail geometries. To address the variation in operational conditions in the MBS environment, a sample of measured wheel profiles was accounted for in the analysis. This MBS study showed that there is a strong correlation between the crossing rail geometry condition, wheel–rail contact force, and crossing condition indicators computed from the dynamic track responses. Contrasting measured and simulated track responses from the six investigated crossing panels showed a good agreement. This observation supports the validity of the simulation-based condition assessment of crossing rail geometry. Based on the work in this thesis, a foundation is set for developing methods for automatic calibration of S&C MBS models and subsequent damage evolution modelling based on operational online condition monitoring data. This development aims to address S&C service life in a digital environment and presents a key component for building a Digital Twin prototype for S&C condition monitoring

    COMMON CROSSING CONDITION MONITORING WITH ON BOARD INERTIAL MEASUREMENTS

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    A railway turnout is an element of the railway infrastructure that influences the reliability of a railway traffic operation the most. The growing necessity for the reliability and availability in the railway transportation promotes a wide use of condition monitoring systems. These systems are typically based on the measurement of the dynamic response during operation. The inertial dynamic response measurement with on-board systems is the simplest and reliable way of monitoring the railway infrastructure. However, the new possibilities of condition monitoring are faced with new challenges of the measured information utilization. The paper deals with the condition monitoring of the most critical part of turnouts - the common crossing. The application of an on-board inertial measurement system ESAH-F for a crossing condition monitoring is presented and explained. The inertial measurements are characterized with the low correlation of maximal vertical accelerations to the lifetime. The data mining approach is used to recover the latent relations in the measurement’s information. An additional time domain and spectral feature sets are extracted from axle-box acceleration signals. The popular spectral kurtosis features are used additionally to the wavelet ones. The feature monotonicity ranking is carried out to select the most suited features for the condition indicator. The most significant features are fused in a one condition indicator with a principal component analysis. The proposed condition indicator delivers an almost two-time higher correlation to the lifetime as the maximal vertical accelerations. The regression analysis of the indicator to the lifetime with an exponential fit proves its good applicability for the crossing residual useful life prognosis

    Reconstruction of sleeper displacements from measured accelerations for model-based condition monitoring of railway crossing panels

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    Railway switches and crossings (S&C, turnouts) connect different track sections and create a railway network by allowing trains to change tracks. This functionality comes at a cost as the load-inducing rail discontinuities in the switch and crossing panels cause much larger degradation rates for S&C compared to regular plain line tracks. The high degradation rates make remote condition monitoring an interesting prospect for infrastructure managers to optimise maintenance and ensure safe operations. To this end, this paper addresses the development of tailored signal processing tools for condition monitoring using embedded accelerometers in crossing panels. Multibody simulations of the dynamic train–track interaction are used to aid the interpretation of the measured signals in a first step towards building a model-based condition monitoring system. An analysis is performed using sleeper acceleration measurement data generated by 100 000 train passages in eight crossing panels. Based on the given data, a novel frequency-domain displacement reconstruction method is developed and the robustness of the method with respect to encountered operational variability of the measured data is demonstrated. The separation of the track response into quasi-static and dynamic domains based on deformation wavelength regions is proposed as a promising strategy to observe the ballast condition and the crossing geometry condition, respectively

    Multiscale Machine Learning and Numerical Investigation of Ageing in Infrastructures

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    Infrastructure is a critical component of a country’s economic growth. Interaction with extreme service environments can adversely affect the long-term performance of infrastructure and accelerate ageing. This research focuses on using machine learning to improve the efficiency of analysing the multiscale ageing impact on infrastructure. First, a data-driven campaign is developed to analyse the condition of an ageing infrastructure. A machine learning-based framework is proposed to predict the state of various assets across a railway system. The ageing of the bond in fibre-reinforced polymer (FRP)-strengthened concrete elements is investigated using machine learning. Different machine learning models are developed to characterise the long-term performance of the bond. The environmental ageing of composite materials is investigated by a micromechanics-based machine learning model. A mathematical framework is developed to automatically generate microstructures. The microstructures are analysed by the finite element (FE) method. The generated data is used to develop a machine learning model to study the degradation of the transverse performance of composites under humid conditions. Finally, a multiscale FE and machine learning framework is developed to expand the understanding of composite material ageing. A moisture diffusion analysis is performed to simulate the water uptake of composites under water immersion conditions. The results are downscaled to obtain micromodel stress fields. Numerical homogenisation is used to obtain the composite transverse behaviour. A machine learning model is developed based on the multiscale simulation results to model the ageing process of composites under water immersion. The frameworks developed in this thesis demonstrate how machine learning improves the analysis of ageing across multiple scales of infrastructure. The resulting understanding can help develop more efficient strategies for the rehabilitation of ageing infrastructure

    A failure probability assessment method for train derailments in railway yards based on IFFTA and NGBN

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    Derailment is one of the main hazards during train passes through railway turnouts (RTs) in classification yards. The complexity of the train-turnout system (TTS) and unfavorable operating conditions frequently cause freight wagons to derail at RTs. Secondary damages such as hazardous material spillage and train collisions can result in loss of life and property. Therefore, the primary goal is to assess the derailment risk and identify the root causes when trains pass through RTs in classification yards. To address this problem, this paper proposes a failure probability assessment approach that integrates intuitionistic fuzzy fault tree analysis (IFFTA) and Noisy or gate Bayesian network (NGBN) for quantifying the derailment risk at RTs. This method can handle the fact that the available information on the components of the TTS is imprecise, incomplete, and vague. The proposed methodology was tested through data analysis at Taiyuan North classification yard in China. The results demonstrate that the method can efficiently evaluate the derailment risk and identify key risk factors. To reduce the derailment risk at RTs and prevent secondary damage and injuries, measures such as optimizing turnout alignment, controlling impact between wagons, lubricating the rails, and regularly inspecting the turnout geometries can be implemented. By developing a risk-based model, this study connects theory with practice and provides insights that can help railway authorities better understand the impact of poor TTS conditions on train safety in classification yards

    Running Dynamics of Rail Vehicles

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    The investigation of rail vehicle running dynamics plays an important role in the more than 200 year development of railway vehicles and infrastructure. Currently, there are a number of new requirements for rail transport associated with the reduced environmental impact, energy consumption and wear, whilst increasing train speed and passenger comfort. Therefore, the running dynamics of rail vehicles is still a research topic that requires improved simulation tools and experimental procedures. The book focuses on the current research topics in railway vehicles running dynamics. Special attention is given to high-speed railway transport, acoustic and vibrational impact of railway transport to the surroundings, optimization of energy supply systems for railway transport, traction drives optimization and wear of wheels and rails
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