Modelling and estimating the reliability of stochastic dynamical systems with Markovian switching

In this paper, a general framework for the modelling of physical phenomena with stochastic dynamical systems switched by jump Markov processes is given. A methodology of the associated estimation procedures is provided. A particular attention is paid to the estimation of the underlying jump process, which is not observable.As an application, a stochastic model is proposed for the fatigue crack growth problem. The estimation of the model parameters is made on a real crack growth data set. We are thus able to simulate some crack growth paths which are used for reliability analysis through Monte Carlo techniques.     

The transient characteristics of an underdamped periodic potential system excited by two different kinds of noise

In this paper, the transient characteristics of an underdamped periodic potential system excited by multiplicative Gaussian white noise and additive Lévy noise are studied in terms of the mean first-passage time(MFPT) and the probability density function(PDF) of the first-passage time. The second-order underdamped periodic potential system is equivalent to two first-order stochastic differential equations. The MFPT was obtained by averaging the response value of the first-passage time, and the PDF image of the first-passage time was drawn under different parameter values. It was found that the increase in damping coefficient and stability index will inhibit the particle crossing, while the increase of multiplicative noise intensity, additive noise intensity and skewness parameter will promote the particle crossing to a certain extent.     

非线性流滞阻尼器耗能结构随机地震响应和首超时间分析

对非线性流滞阻尼器耗能结构在Kanai-Tajimi谱地震激励下的随机响应及其随机失效时间和动力可靠性进行了系统研究。首先建立了结构的非线性运动方程;然后,基于随机平均法,将结构响应幅值近似为一维markov扩散过程,获得了扩散过程漂移系数和扩散系数的解析表达式;其次,利用扩散过程与FPK方程的对应关系,获得了幅值平稳概率密度函数和幅值任意阶矩的解析表达式;再次,利用幅值与结构位移和速度的相互转化关系,获得了结构位移与速度的平稳联合概率密度函数和位移、速度方差以及位移期望穿越率的解析表达式;最后,利用扩散过程的后向Kolmogrov方程,基于首超失效模型,建立了结构动力可靠性函数方程和结构随机失效时间统计矩方程,并利用一维扩散过程的边界分类性质,将统计矩方程的奇异定性边界条件转化为等价的定量边界条件,进而获得了失效时间任意阶统计矩的解析解,并利用此矩,对结构动力可靠性和失效时间概率分布函数进行了近似分析,给出了算例,从而建立了结构非线性随机地震响应及其随机失效时间和动力可靠性的分析方法。     

Linear filtering with Ornstein-Ulhenbeck process as noise

We consider a linear filtering model (with feedback) when the observation noise is an Ornstein-Ulhenbeck (OU) process with parameter β. The coefficients appearing in the model are all assumed to be bounded. In addition, the coefficients appearing in the observation equation are also assumed to be differentiable. We consider the general case when the OU noise is also correlated with the signal. Under these conditions, we derive the filtering equations for the optimal filter.     

A translation model for non-stationary, non-Gaussian random processes

A model for simulation of non-stationary, non-Gaussian processes based on non-linear translation of Gaussian random vectors is presented. This method is a generalization of traditional translation processes that includes the capability of simulating samples with spatially or temporally varying marginal probability density functions. A formal development of the properties of the resulting process includes joint probability density function, correlation distortion and lower and upper bounds that depend on the target marginal distributions. Examples indicate the possibility of exactly matching a wide range of marginal pdfs and second order moments through a simple interpolating algorithm. Furthermore, the application of the method in simulating statistically inhomogeneous random media is investigated, using the specific case of binary translation with stationary and non-stationary target correlations.     

Path integral solution for non-linear system enforced by Poisson White Noise

In this paper the response in terms of probability density function of non-linear systems under Poisson White Noise is considered. The problem is handled via path integral (PI) solution that may be considered as a step-by-step solution technique in terms of probability density function. First the extension of the PI to the case of Poisson White Noise is derived, then it is shown that at the limit when the time step becomes an infinitesimal quantity the Kolmogorov–Feller (K–F) equation is fully restored enforcing the validity of the approximations made in obtaining the conditional probability appearing in the Chapman Kolmogorov equation (starting point of the PI). Spectral counterpart of the PI, ruling the evolution of the characteristic function is also derived. It is also shown that using appropriately the PI for Poisson White Noise also the case of Normal White Noise be easily derived.     

First-passage solutions in fatigue crack propagation

The statistical characteristics of the time required by the crack size to reach a specified length are sought. This time is treated as the random variable time-to-failure and the analysis is cast into a first-passage time problem. The fatigue crack propagation growth equation is randomized by employing the pulse train stochastic process model. The resulting equation is stochastically averaged so that the crack size can be approximately modelled as Markov process. Choosing the appropriate transition density function for this process and setting the proper initial and boundary conditions it becomes possible to solve the associated forward Kolmogorov equation expressing the solution in the form of an infinite series. Next, the survival probability of a component, the cumulative distribution function and the probability density function of the first-passage time are determined in a series form as well. Corresponding expressions are also derived for its mean and mean square. Verification of the theoretical results is attempted through comparisons with actual experimental data and numerical simulation studies.     

Stochastic response analysis of the softening Duffing oscillator and ship capsizing probability determination via a numerical path integral approach

A numerical path integral approach is developed for determining the response and first-passage probability density functions (PDFs) of the softening Duffing oscillator under random excitation. Specifically, introducing a special form for the conditional response PDF and relying on a discrete version of the Chapman–Kolmogorov (C–K) equation, a rigorous study of the response amplitude process behavior is achieved. This is an approach which is novel compared to previous heuristic ones which assume response stationarity, and thus, neglect important aspects of the analysis such as the possible unbounded response behavior when the restoring force acquires negative values. Note that the softening Duffing oscillator with nonlinear damping has been widely used to model the nonlinear ship roll motion in beam seas. In this regard, the developed approach is applied for determining the capsizing probability of a ship model subject to non-white wave excitations. Comparisons with pertinent Monte Carlo simulation data demonstrate the reliability of the approach.     

Stochastic response determination of nonlinear oscillators with fractional derivatives elements via the Wiener path integral

A novel approximate analytical technique for determining the non-stationary response probability density function (PDF) of randomly excited linear and nonlinear oscillators endowed with fractional derivatives elements is developed. Specifically, the concept of the Wiener path integral in conjunction with a variational formulation is utilized to derive an approximate closed form solution for the system response non-stationary PDF. Notably, the determination of the non-stationary response PDF is accomplished without the need to advance the solution in short time steps as it is required by the existing alternative numerical path integral solution schemes which rely on a discrete version of the Chapman–Kolmogorov (C–K) equation. This is accomplished by circumventing the solution of the associated Euler–Lagrange equation ordinarily used in the path integral based procedures. The accuracy of the technique is demonstrated by pertinent Monte Carlo simulations.     

A Method of Estimating the Process Capability Index from the First Four Moments of Non‐normal Data

A method is presented to estimate the process capability index (PCI) for a set of non‐normal data from its first four moments. It is assumed that these four moments, i.e. mean, standard deviation, skewness, and kurtosis, are suitable to approximately characterize the data distribution properties. The probability density function of non‐normal data is expressed in Chebyshev–Hermite polynomials up to tenth order from the first four moments. An effective range, defined as the value for which a pre‐determined percentage of data falls within the range, is solved numerically from the derived cumulative distribution function. The PCI with a specified limit is hence obtained from the effective range. Compared with some other existing methods, the present method gives a more accurate PCI estimation and shows less sensitivity to sample size. A simple algebraic equation for the effective range, derived from the least‐square fitting to the numerically solved results, is also proposed for PCI estimation. Copyright © 2004 John Wiley & Sons, Ltd.     

水平地震下列车过桥的非平稳随机响应及其极值估计

推广虚拟激励法来研究水平地震作用时三维车桥耦合时变系统的非平稳随机振动以及最大值估计方法.水平地震动假设为均匀调制非平稳随机过程;轨道不平顺假设为多点异相位均匀调制非平稳随机激励.借助时变系统的虚拟激励法将水平地震和轨道不平顺随机激励分别转化为一系列确定性简谐激励,为了能更真实地模拟车轮作用力在时间域和空间域的连续变化...     

Importance sampling for reliability assessment of dynamic systems under general random process excitation

This paper develops a reliability assessment method for dynamic systems subjected to a general random process excitation. Safety assessment using direct Monte Carlo simulation is computationally expensive, particularly when estimating low probabilities of failure. The Girsanov transformation-based reliability assessment method is a computationally efficient approach intended for dynamic systems driven by Gaussian white noise, and this approach can be extended to random process inputs that can be represented as transformations of Gaussian white noise. In practice, dynamic systems may be subjected to inputs that may be better modeled as non-Gaussian and/or non-stationary random processes, which are not easily transformable to Gaussian white noise. We propose a computationally efficient scheme, based on importance sampling, which can be implemented directly on a general class of random processes — both Gaussian and non-Gaussian, and stationary and non-stationary. We demonstrate that this approach is in fact equivalent to Girsanov transformation when the uncertain inputs are Gaussian white noise processes. The proposed approach is demonstrated on a linear dynamic system driven by Gaussian white noise and Brownian bridge processes, a multi-physics aero-thermo-elastic model of a flexible panel subjected to hypersonic flow, and a nonlinear building frame subjected to non-stationary non-Gaussian random process excitation.     

基于局部平稳法的粘滞阻尼被动控制结构抗震可靠度分析

针对基于确定性激励的被动控制装置参数设计不具有普遍性的问题,提出了粘滞阻尼被动控制结构在一般非平稳随机地震动作用下抗震可靠度分析的局部平稳法。首先基于非平稳随机过程的局部平稳小波模型,提出了适用于临界阻尼比较大的粘滞阻尼被动控制结构的非平稳地震动输入-多自由度（受控）结构位移响应输出的功率谱关系。其次,根据超越过程的Markov过程假定及各阶响应谱矩,得到了受控结构层间位移的动力可靠度。数值分析结果表明：粘滞阻尼器在不同层间的配置,对受控结构的层间动力可靠度有显著影响。最后,以一个6层剪切型多自由度结构为例,对比了Monte Carlo模拟估计与本文所提方法计算的结构动力可靠度,验证了该方法的可靠性与高效性。     

First-passage failure of quasi linear systems subject to multi-time-delayed feedback control and wide-band random excitation

A procedure for studying the first-passage failure of quasi-linear systems subject to multi-time-delayed feedback control and wide-band random excitation is proposed. The stochastic averaging method for quasi-integrable Hamiltonian systems is first introduced. The backward Kolmogorov equation governing the conditional reliability function and a set of generalized Pontryagin equations governing the conditional moments of first-passage time are then established. The conditional reliability function, the conditional probability density and moments of first-passage time are obtained by solving the backward Kolmogorov equation and generalized Pontryagin equations with suitable initial and boundary conditions. An example is given to illustrate the proposed procedure and the results from digital simulation are obtained to verify the effectiveness of the proposed procedure. The effects of time delay in feedback control forces on the conditional reliability function, conditional probability density and moments of first-passage time are analyzed.     

Non-stationary dynamic structural response to thunderstorm outflows

Wind loads on structures are commonly described as stationary phenomena that occur in neutral atmospheric conditions at the synoptic scale with velocity profiles in equilibrium with the atmospheric boundary layer. Nevertheless, structural systems can be also affected by thunderstorm outflows, which are non-stationary local phenomena at the mesoscale that occur in convective conditions with totally different velocity profiles with respect to synoptic winds. This paper presents a non-stationary probabilistic model that describes the wind velocity fluctuations experienced during a thunderstorm in order to estimate its effects on the dynamic structural response. The model is first calibrated on a typical thunderstorm recorded in the north-west italian coast and it is used to generate virtual time histories in a Monte Carlo simulation approach. The potential influence of the wind load non-stationary features on the peak structural response is investigated using single degree of freedom parametric analysis and statistical estimation.     

Integro-differential Chapman-Kolmogorov equation for continuous-jump Markov processes and its use in problems of multi-component renewal impulse process excitations

In the present paper the applications of the integro-differential Chapman-Kolmogorov equation to the problems of pure-jump stochastic processes and continuous-jump response processes are discussed. The pure-jump processes considered herein are the counting Poisson process, a two-state jump process, and a multi-state jump process. The differential equations governing the Markov state probabilities are obtained from the degenerate, pure differential form, of the general, integro-differential Chapman-Kolmogorov equation, with the aid of the jump probability intensity functions. The continuous-jump response process is the response of a dynamic system to a multi-component renewal impulse process excitation. The excitation consists of a number of n statistically independent random trains of impulses, each of which is driven by an Erlang renewal process with parameters ν_j,k_j. Each of the impulse processes is characterized by an auxiliary zero-one jump stochastic process, which consists of k_j negative exponential distributed phases. The Markov states for the whole problem are determined by the coincidences of the phases of the individual jump processes. Thus the response probability distribution may be characterized by a joint probability density-discrete distribution of the state variables of the dynamic system and of the states of the pertinent Markov chain. The explicit integro-differential equations governing the joint probability density-discrete distribution of the response are obtained from the general forward integro-differential Chapman-Kolmogorov equation, after the determination of the jump probability intensity functions for the continuous-jump and pure-jump processes.     

Optimal bounded control of steady-state random vibrations

A SDOF system is considered, which is excited by a white-noise random force. The system's response is controlled by a force of bounded magnitude, with the aim of minimizing integral of the expected response energy over a given period of time. The integral to be minimized satisfies the Hamilton–Jacobi–Bellman (HJB) equation. An analytical solution of this PDE is obtained within a certain outer part of the phase plane. This solution is analyzed for large time intervals, which correspond to the limiting steady-state random vibration. The analysis shows the outer domain expanding onto the whole phase plane in the limit, implying that the simple dry-friction control law is the optimal one for steady-state response. The resulting value of the (unconditional) expected response energy, for the case of a stationary excitation, is also obtained. It matches with the corresponding result of energy balance analysis, as obtained by direct application of the SDE Calculus, as well as that of stochastic averaging for the case where the magnitude of dry friction force and intensity of excitation are both small. A general expression for mean absolute value of the response velocity is also obtained using the SDE calculus. Certain reliability predictions both for first-passage and fatigue-type failures are also derived for the optimally controlled system using the stochastic averaging method. These predictions are compared with their counterparts for the system with a linear velocity feedback and same r.m.s. response, thereby illustrating the price to be paid for the bounds on control force in terms of the reduced reliability of the system.     

A simple and efficient methodology to approximate a general non-Gaussian stationary stochastic process by a translation process

Some widely used methodologies for simulation of non-Gaussian processes rely on translation process theory which imposes certain compatibility conditions between the non-Gaussian power spectral density function (PSDF) and the non-Gaussian probability density function (PDF) of the process. In many practical applications, the non-Gaussian PSDF and PDF are assigned arbitrarily; therefore, in general they can be incompatible. Several techniques to approximate such incompatible non-Gaussian PSDF/PDF pairs with a compatible pair have been proposed that involve either some iterative scheme on simulated sample functions or some general optimization approach. Although some of these techniques produce satisfactory results, they can be time consuming because of their nature. In this paper, a new iterative methodology is developed that estimates a non-Gaussian PSDF that: (a) is compatible with the prescribed non-Gaussian PDF, and (b) closely approximates the prescribed incompatible non-Gaussian PSDF. The corresponding underlying Gaussian PSDF is also determined. The basic idea is to iteratively upgrade the underlying Gaussian PSDF using the directly computed (through translation process theory) non-Gaussian PSDF at each iteration, rather than through expensive ensemble averaging of PSDFs computed from generated non-Gaussian sample functions. The proposed iterative scheme possesses two major advantages: it is conceptually very simple and it converges extremely fast with minimal computational effort. Once the underlying Gaussian PSDF is determined, generation of non-Gaussian sample functions is straightforward without any need for iterations. Numerical examples are provided demonstrating the capabilities of the methodology.     

A note on the first-passage problem and VanMarcke’s approximation — short communication

Given a scalar, stationary, Markov process, this short communication presents a closed-form solution for the first-passage problem for a fixed threshold b. The derivation is based on binary processes and the general formula of Siegert [Siegert AJF. On the first-passage time probability problem. Physical Review 1951; 81:617–23]. The relation for the probability density function of the first-passage time is identical to the commonly used formula that was derived by VanMarcke [VanMarcke E. On the distribution of the first-passage time for normal stationary random processes. Journal of Applied Mechanics ASME 1975; 42:215–20] for Gaussian processes. The present derivation is based on more general conditions and reveals the criteria for the validity of the approximation. Properties of binary processes are also used to derive a hierarchy of upper bounds for any scalar process.     

First-passage failure of Duhem hysteretic systems

Mechanical and structural systems under dynamic loading always represent hysteresis behavior, which is a typical nonlinear phenomenon and lets the dynamic responses of the systems remarkably deviate from that of corresponding equivalent linear systems. The Duhem hysteretic model is versatile to cover most existing hysteresis models and to describe the hysteretic behavior more accurately, and the investigation and application of that are abundant. The combination of the equivalent nonlinearization technique, which transforms the hysteretic force into energy-depending damping and stiffness, and the stochastic averaging technique yields the best forecast for the dynamic responses. The first-passage failure of Duhem hysteretic systems, an important question in random vibration, however, remains open. The analysis of the backward Kolmogorov equation associated with the averaged It? stochastic differential equation generates the reliability function and probability density function, and the effects of system parameters on first-passage failure are discussed concisely. The present work will guide the parameter design of the Duhem materials to decrease the probability of first-passage failure and make the Duhem systems safer.