47,227 research outputs found

    Stochastic RUL calculation enhanced with TDNN-based IGBT failure modeling

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    Power electronics are widely used in the transport and energy sectors. Hence, the reliability of these power electronic components is critical to reducing the maintenance cost of these assets. It is vital that the health of these components is monitored for increasing the safety and availability of a system. The aim of this paper is to develop a prognostic technique for estimating the remaining useful life (RUL) of power electronic components. There is a need for an efficient prognostic algorithm that is embeddable and able to support on-board real-time decision-making. A time delay neural network (TDNN) is used in the development of failure modes for an insulated gate bipolar transistor (IGBT). Initially, the time delay neural network is constructed from training IGBTs' ageing samples. A stochastic process is performed for the estimation results to compute the probability of the health state during the degradation process. The proposed TDNN fusion with a statistical approach benefits the probability distribution function by improving the accuracy of the results of the TDDN in RUL prediction. The RUL (i.e., mean and confidence bounds) is then calculated from the simulation of the estimated degradation states. The prognostic results are evaluated using root mean square error (RMSE) and relative accuracy (RA) prognostic evaluation metrics

    Validation of in situ applicable measuring techniques for analysis of the water adsorption by stone

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    As the water adsorbing behaviour (WAB) of stone is a key factor for most degradation processes, its analysis is a decisive aspect when monitoring deterioration and past conservation treatments, or when selecting a proper conservation treatment. In this study the performance of various non-destructive methods for measuring the WAB are compared, with the focus on the effect of the variable factors of the methods caused by their specific design. The methods under study are the contact-sponge method (CSM), the Karsten tube (KT) and the Mirowski pipe (MIR). Their performance is compared with the standardized capillary rise method (CR) and the results are analysed in relation to the open porosity of different lithotypes. Furthermore the effect of practical encumbrances which could limit the application of these methods was valuated. It was found that KT and CSM have complementary fields of investigation, where CSM is capable of measuring the initial water uptake of less porous materials with a high precision, while KT was found commodious for measuring longer contact times for more porous lithotypes. MIR showed too many discommodities, leading to unreliable results. To adequately compare the results of the different methods, the size of the contact area appears to be the most influential factor, whereas the contact material and pressure on the surface do not indicate a significant influence on the results. The study of these factors is currently being extended by visualization of the water adsorption process via X-ray and neutron radiography in combination with physico-mathematical models describing the WAB

    High cycle fatigue and ratcheting interaction of laser powder bed fusion stainless steel 316L:Fracture behaviour and stress-based modelling

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    Variations in the physical and mechanical properties of parts made by laser power bed fusion (L-PBF) could be affected by the choice of processing or post-processing strategies. This work examined the influence of build orientation and post-processing treatments (annealing or hot isostatic pressing) on the fatigue and fracture behaviours of L-PBF stainless steel 316L in the high cycle fatigue region, i.e. 104 – 106 cycles. Experimental results show that both factors introduce significant changes in the plastic deformation properties, which affect fatigue strength via the mechanism of fatigue-ratcheting interaction. Cyclic plasticity is characterised by hardening, which promotes mean stress insensitivity and improved fatigue resistance. Fatigue activities, involving the initiation of crack at defects and microstructural heterogeneities, are of greater relevance to the longer life region where the global deformation mode is elastic. As the simultaneous actions of ratcheting and fatigue generate complex nonlinear interactions between the alternating stress amplitude and mean stress, the fatigue properties could not be effectively predicted using traditional stress-based models. A modification to the Goodman relation was proposed to account for the added effects of cyclic plasticity and was demonstrated to produce good agreement with experimental results for both cyclic hardening and softening materials.EDB (Economic Devt. Board, S’pore)Accepted versio

    Expert Elicitation for Reliable System Design

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    This paper reviews the role of expert judgement to support reliability assessments within the systems engineering design process. Generic design processes are described to give the context and a discussion is given about the nature of the reliability assessments required in the different systems engineering phases. It is argued that, as far as meeting reliability requirements is concerned, the whole design process is more akin to a statistical control process than to a straightforward statistical problem of assessing an unknown distribution. This leads to features of the expert judgement problem in the design context which are substantially different from those seen, for example, in risk assessment. In particular, the role of experts in problem structuring and in developing failure mitigation options is much more prominent, and there is a need to take into account the reliability potential for future mitigation measures downstream in the system life cycle. An overview is given of the stakeholders typically involved in large scale systems engineering design projects, and this is used to argue the need for methods that expose potential judgemental biases in order to generate analyses that can be said to provide rational consensus about uncertainties. Finally, a number of key points are developed with the aim of moving toward a framework that provides a holistic method for tracking reliability assessment through the design process.Comment: This paper commented in: [arXiv:0708.0285], [arXiv:0708.0287], [arXiv:0708.0288]. Rejoinder in [arXiv:0708.0293]. Published at http://dx.doi.org/10.1214/088342306000000510 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

    3D FEM model development from 3D optical measurement technique applied to corroded steel bars

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    Understanding the mechanical effects of the corrosion pits on the steel surface requires an accurate definition of their geometry and distribution along the rebar. 3D optical measurement technique is used to obtain the outer geometry of artificially corroded bars tested under cyclic or monotonic loads. 3D FEM model development from the 3D scanning results were carried out in order to investigate the failure process and local effects on the pits, which are responsible of the variation of the mechanical properties in corroded steel reinforcement. In addition, a validation of a simplified model, which allows the mechanical steel properties determination given an estimated corrosion level, is presented. 3D models were convenient to observe and measure the local effects on the pits.Peer ReviewedPostprint (author's final draft

    Online Bearing Remaining Useful Life Prediction Based on a Novel Degradation Indicator and Convolutional Neural Networks

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    In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs are of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are often limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct health index. In this paper, a novel online data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNN) in predicting the RUL of bearings. More concretely, the raw vibrations of training bearings are first processed using the Hilbert-Huang transform (HHT) and a novel nonlinear degradation indicator is constructed as the label for learning. The CNN is then employed to identify the hidden pattern between the extracted degradation indicator and the vibration of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings' RULs are predicted by using a ϵ\epsilon-support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by transferring to bearings undergoing different operating conditions

    Review of Health Prognostics and Condition Monitoring of Electronic Components

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    To meet the specifications of low cost, highly reliable electronic devices, fault diagnosis techniques play an essential role. It is vital to find flaws at an early stage in design, components, material, or manufacturing during the initial phase. This review paper attempts to summarize past development and recent advances in the areas about green manufacturing, maintenance, remaining useful life (RUL) prediction, and like. The current state of the art in reliability research for electronic components, mainly includes failure mechanisms, condition monitoring, and residual lifetime evaluation is explored. A critical analysis of reliability studies to identify their relative merits and usefulness of the outcome of these studies' vis-a-vis green manufacturing is presented. The wide array of statistical, empirical, and intelligent tools and techniques used in the literature are then identified and mapped. Finally, the findings are summarized, and the central research gap is highlighted
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