504 research outputs found

    Methodology of tolerance synthesis using bond graph

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    International audienceThis paper presents a methodology of parametric tolerance synthesis with respect to output aleatory uncertainty specifications. It relies on density function propagation through the inverse model. The resulting parameter density function is then used to synthesize a confidence interval suitable for sizing purpose. As an illustration, parametric tolerance synthesis on a DC motor rotating a load is processed

    IMPORTANCE MEASURE OF PROBABILISTIC COMMON CAUSE FAILURES UNDER SYSTEM HYBRID UNCERTAINTY BASED ON BAYESIAN NETWORK

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    When dealing with modern complex systems, the relationship existing between components can lead to the appearance of various dependencies between component failures, where multiple items of the system fail simultaneously in unpredictable fashions. These probabilistic common cause failures affect greatly the performance of these critical systems. In this paper a novel methodology is developed to quantify the importance of common cause failures when hybrid uncertainties are presented in systems. First, the probabilistic common cause failures are modeled with Bayesian networks and are incorporated into the system exploiting the α factor model. Then, probability-boxes (bound analysis method) are introduced to model the hybrid uncertainties and quantify the effect of uncertainties on system reliability. Furthermore, an extended Birnbaum importance measure is defined to identify the critical common cause failure events and coupling impact factors when uncertainties are expressed by probability-boxes. Finally, the effectiveness of the method is demonstrated through a numerical example.W przypadku nowoczesnych systemów złożonych, relacje zachodzące między komponentami mogą prowadzić do pojawienia się różnych zależności między ich uszkodzeniami, a tym samym do sytuacji w których kilka składowych systemu ulega uszkodzeniu jednocześnie w nieprzewidywalny sposób. Tego typu probabilistyczne uszkodzenia wywołane wspólną przyczyną (PCCF) mają ogromny wpływ na wydajność tych kluczowych systemów. W przedstawionym artykule opracowano nową metodę szacowania ważności PCFF w sytuacjach, gdy w systemie występują niepewności hybrydowe. W pierwszej kolejności, PCFF zamodelowano za pomocą sieci bayesowskich i włączono do systemu wykorzystującego model współczynnika α. Następnie, wprowadzono przedziały prawdopodobieństwa, tzw. probability boxes (bound analysis method), w celu zamodelowania niepewności hybrydowych i kwantyfikacji wpływu tych niepewności na niezawodność systemu. Ponadto zdefiniowano rozszerzoną miarę ważności Birnbauma, która pozwala zidentyfikować krytyczne zdarzenia PCCF oraz czynniki, które je wywołały, w przypadkach, gdy niepewności wyrażone są za pomocą probability boxes. Skuteczność metody wykazano na przykładzie numerycznym

    РАСЧЕТ НАДЕЖНОСТИ БЕССТУПЕНЧАТОГО МЕХАНИЗМА ПОВОРОТА ПРОМЫШЛЕННОГО ТРАКТОРА СО СЛЕДЯЩЕЙ СИСТЕМОЙ УПРАВЛЕНИЯ

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    The authors of the article determined the level of reliability of the stepless differential rotation mechanism with a tracking control system (SDRMTCS), achieved at the stage of its design and manufacture. Based on the results of the analysis of the structure of this mechanism, the concept of a failure of the SDRMTCS is defined. A mathematical model of reliability SDRMTCS as restoring the products obtained graph States based on a number of assumptions, including the assumption of stationarity, the lack of follow-through and the ordinary process of functioning of the product. When determining the parameters of the mathematical model of the SDRMTCS, statistical data on the failure rates and failure times of the component parts of the product, obtained during the testing of analog devices, were used. The product readiness function is determined by solving the system of Kolmogorov differential equations under the given initial conditions.  The readiness function is calculated taking into account the loads that occur during the operation of the SDRMTCS as part of a mobile object, by introducing a correction factor for the failure rates of the elements of the product. Using the availability function, the value of the failure rate (failure rate) and the value of the service life (durability indicator) are determined. Both reliability indicators meet the requirements of the task. The peculiarity of the above calculations of the reliability of the SDRMTCS is that they focus on constructive, rather than on production and operational failures. This helped to ensure literacy adopted at the design stage SDRMTCS decisions, including decisions on the harmonization and standardization of parts, assemblies, decisions about the use of highly reliable components and assemblies. This is justified not only by increasing the reliability of the product, but also by reducing its cost.Авторами статьи определен уровень надежности бесступенчатого дифференциального механизма поворота со следящей системой управления (БДМПсССУ), достигнутый на стадии его проектирования и изготовления. По результатам анализа структуры данного механизма определено понятие отказа БДМПсССУ. Математическая модель надежности БДМПсССУ как восстанавливаемого изделия получена по графу состояний с учетом ряда допущений, основным из которых является допущение о стационарности, отсутствии последействия и ординарности процесса функционирования изделия. При определении параметров математической модели БДМПсССУ использованы статистические данные об интенсивностях отказов и наработок на отказ составных частей изделия, полученные в процессе испытаний приборов-аналогов. Решением системы дифференциальных уравнений Колмогорова при заданных начальных условиях определена функция готовности изделия. Функция готовности рассчитана с учетом нагрузок, которые возникают при эксплуатации БДМПсССУ в составе подвижного объекта, введением поправочного коэффициента для интенсивностей отказов элементов изделия. С использованием функции готовности определены величина наработки на отказ (показатель безотказности) и величина срока службы (показателя долговечности). Оба показателя надежности удовлетворяют требованиям задания. Особенность приведенных расчетов надежности БДМПсССУ в том, что в них сделан акцент на конструктивные, а не на производственные и эксплуатационные отказы. Это позволило убедиться в грамотности принятых на этапе проектирования БДМПсССУ решений, в том числе решения об унификации и стандартизации деталей, узлов, решения об использовании высоконадежных элементов и узлов. Это оправдано не только повышением надежности изделия, но и сокращением его стоимост

    Optimizing the Safety Margins Governing a Deterministic Design Process while Considering the Effects of a Future Test and Redesign on Epistemic Model Uncertainty

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    At the initial design stage, engineers often rely on low-fidelity models that have high uncertainty. Model uncertainty is reducible and is classified as epistemic uncertainty; uncertainty due to variability is irreducible and classified as aleatory uncertainty. In a deterministic safety-margin-based design approach, uncertainty is implicitly compensated for by using fixed conservative values in place of aleatory variables and ensuring the design satisfies a safety-margin with respect to design constraints. After an initial design is selected, testing (e.g. physical experiment or high-fidelity simulation) is performed to reduce epistemic uncertainty and ensure the design achieves the targeted levels of safety. Testing is used to calibrate low-fidelity models and prescribe redesign when tests are not passed. After calibration, reduced epistemic model uncertainty can be leveraged through redesign to restore safety or improve design performance; however, redesign may be associated with substantial costs or delays. In this work, the possible effects of a future test and redesign are considered while the initial design is optimized using only a low-fidelity model. The goal is to develop a general method for the integrated optimization of the design, testing, and redesign process that allows for the tradeoff between the risk of future redesign and the associated performance and reliability benefits. This is accomplished by formulating the design, testing, and redesign process in terms of safety-margins and optimizing these margins based on expected performance, expected probability of failure, and probability of redesign. The first objective of this study is to determine how the degree of conservativeness in the initial design relates to the expected design performance after a test and possible redesign. The second objective is to develop a general method for modeling epistemic model uncertainty and calibration when simulating a possible future test and redesign. The third objective is to apply the method of simulating a future test and redesign to a sounding rocket design example

    Integrating IVHM and Asset Design

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    Integrated Vehicle Health Management (IVHM) describes a set of capabilities that enable effective and efficient maintenance and operation of the target vehicle. It accounts for the collection of data, conducting analysis, and supporting the decision-making process for sustainment and operation. The design of IVHM systems endeavours to account for all causes of failure in a disciplined, systems engineering, manner. With industry striving to reduce through-life cost, IVHM is a powerful tool to give forewarning of impending failure and hence control over the outcome. Benefits have been realised from this approach across a number of different sectors but, hindering our ability to realise further benefit from this maturing technology, is the fact that IVHM is still treated as added on to the design of the asset, rather than being a sub-system in its own right, fully integrated with the asset design. The elevation and integration of IVHM in this way will enable architectures to be chosen that accommodate health ready sub-systems from the supply chain and design trade-offs to be made, to name but two major benefits. Barriers to IVHM being integrated with the asset design are examined in this paper. The paper presents progress in overcoming them, and suggests potential solutions for those that remain. It addresses the IVHM system design from a systems engineering perspective and the integration with the asset design will be described within an industrial design process

    Integrating IVHM and asset design

    Get PDF
    Integrated Vehicle Health Management (IVHM) describes a set of capabilities that enable effective and efficient maintenance and operation of the target vehicle. It accounts for the collecting of data, conducting analysis, and supporting the decision-making process for sustainment and operation. The design of IVHM systems endeavours to account for all causes of failure in a disciplined, systems engineering, manner. With industry striving to reduce through-life cost, IVHM is a powerful tool to give forewarning of impending failure and hence control over the outcome. Benefits have been realised from this approach across a number of different sectors but, hindering our ability to realise further benefit from this maturing technology, is the fact that IVHM is still treated as added on to the design of the asset, rather than being a sub-system in its own right, fully integrated with the asset design. The elevation and integration of IVHM in this way will enable architectures to be chosen that accommodate health ready sub-systems from the supply chain and design trade-offs to be made, to name but two major benefits. Barriers to IVHM being integrated with the asset design are examined in this paper. The paper presents progress in overcoming them, and suggests potential solutions for those that remain. It addresses the IVHM system design from a systems engineering perspective and the integration with the asset design will be described within an industrial design process

    RBI Optimization of Offshore Wind Turbines

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    Bayesian convolutional neural networks for RUL prognostics of solenoid valves with uncertainty estimations

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    Solenoid valves (SV) are essential components of industrial systems and therefore widely used. As they suffer from high failure rates in the field, fault prognosis of these assets plays a major role for improving their maintenance and reliability. In this work, Bayesian convolutional neural networks are used to predict the remaining useful life (RUL) of SVs, by training them on the valve's current signatures. Predictive performance is further improved upon by using salient physical features obtained from an electromechanical model as the network's training input. Results show that our designed network architecture produces well-calibrated uncertainty estimations of the RUL predictive distributions, which is an important concern in prognostic decision-making

    Response statistics and failure probability determination of nonlinear stochastic structural dynamical systems

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    Novel approximation techniques are proposed for the analysis and evaluation of nonlinear dynamical systems in the field of stochastic dynamics. Efficient determination of response statistics and reliability estimates for nonlinear systems remains challenging, especially those with singular matrices or endowed with fractional derivative elements. This thesis addresses the challenges of three main topics. The first topic relates to the determination of response statistics of multi-degree-of-freedom nonlinear systems with singular matrices subject to combined deterministic and stochastic excitations. Notably, singular matrices can appear in the governing equations of motion of engineering systems for various reasons, such as due to a redundant coordinates modeling or due to modeling with additional constraint equations. Moreover, it is common for nonlinear systems to experience both stochastic and deterministic excitations simultaneously. In this context, first, a novel solution framework is developed for determining the response of such systems subject to combined deterministic and stochastic excitation of the stationary kind. This is achieved by using the harmonic balance method and the generalized statistical linearization method. An over-determined system of equations is generated and solved by resorting to generalized matrix inverse theory. Subsequently, the developed framework is appropriately extended to systems subject to a mixture of deterministic and stochastic excitations of the non-stationary kind. The generalized statistical linearization method is used to handle the nonlinear subsystem subject to non-stationary stochastic excitation, which, in conjunction with a state space formulation, forms a matrix differential equation governing the stochastic response. Then, the developed equations are solved by numerical methods. The accuracy for the proposed techniques has been demonstrated by considering nonlinear structural systems with redundant coordinates modeling, as well as a piezoelectric vibration energy harvesting device have been employed in the relevant application part. The second topic relates to code-compliant stochastic dynamic analysis of nonlinear structural systems with fractional derivative elements. First, a novel approximation method is proposed to efficiently determine the peak response of nonlinear structural systems with fractional derivative elements subject to excitation compatible with a given seismic design spectrum. The proposed methods involve deriving an excitation evolutionary power spectrum that matches the design spectrum in a stochastic sense. The peak response is approximated by utilizing equivalent linear elements, in conjunction with code-compliant design spectra, hopefully rendering it favorable to engineers of practice. Nonlinear structural systems endowed with fractional derivative terms in the governing equations of motion have been considered. A particular attribute pertains to utilizing localized time-dependent equivalent linear elements, which is superior to classical approaches utilizing standard time-invariant statistical linearization method. Then, the approximation method is extended to perform stochastic incremental dynamical analysis for nonlinear structural systems with fractional derivative elements exposed to stochastic excitations aligned with contemporary aseismic codes. The proposed method is achieved by resorting to the combination of stochastic averaging and statistical linearization methods, resulting in an efficient and comprehensive way to obtain the response displacement probability density function. A stochastic incremental dynamical analysis surface is generated instead of the traditional curves, leading to a reliable higher order statistics of the system response. Lastly, the problem of the first excursion probability of nonlinear dynamic systems subject to imprecisely defined stochastic Gaussian loads is considered. This involves solving a nested double-loop problem, generally intractable without resorting to surrogate modeling schemes. To overcome these challenges, this thesis first proposes a generalized operator norm framework based on statistical linearization method. Its efficiency is achieved by breaking the double loop and determining the values of the epistemic uncertain parameters that produce bounds on the probability of failure a priori. The proposed framework can significantly reduce the computational burden and provide a reliable estimate of the probability of failure

    Uncertainty representation and quantification for a nonlinear rotor/stator system with mixed uncertainties

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    A rotor-to-stator coupled system usually exhibits complicated dynamic behaviors due to its nonlinear nature. Moreover, the inherent uncertainty (aleatory uncertainty) and many undetermined factors either brought by manufacturing process or due to the lack of knowledge (epistemic uncertainty) make the analysis of system response a challenging task. Existing studies on rotor uncertainties are mostly focused on the stochastic variables, yet pay less attention to other forms of uncertain variables such as intervals. However, some physical parameters (e.g. friction coefficient) can be hardly assigned one specific probability distribution and often available in interval forms. To deal with this, the concept of likelihood is extended from classical discrete point value to interval variable in the presence of mixed uncertainties. A likelihood-based approach is carried out for the mixed uncertainties representation and quantification. In addition, a new single loop sampling algorithm is developed to reduce the computation cost. This framework could be applied in the field of industry manufacturing and mounting, especially take effect in risk assessment and product maintaining. A series of numerical cases are demonstrated for validation and comparison
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