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Time variant system reliability problem¶

The objective is to evaluate the outcrossing rate from a safe to a failure domain in a time variant reliability problem.

We consider the following limit state function, defined as the difference between a degrading resistance $r(t) = R - bt$ and a time-varying load $S(t)$ :

..math:: begin{align*} g(t)= r(t) - S(t) = R - bt - S(t) quad forall t in [0,T] end{align*}

The failure domaine is defined by:

$g(t) \leq 0$

which means that the resistance at $t$ is less thant the stress at $t$ .

We propose the following probabilistic model:

$R$ is the initial resistance, and $R \sim \mathcal{N}(\mu_R, \sigma_R)$ ;
$b$ is the deterioration rate of the resistance; it is deterministic;
$S(\omega,t)$ is the time-varying stress, which is modeled by a stationary Gaussian process of mean value $\mu_S$ , standard deviation $\sigma_S$ and a squared exponential covariance model $C(s,t)$ .

The outcrossing rate from the safe to the failure domain at instant $t$ is defined by:

$\nu^+(t) = \lim_{\Delta t \rightarrow 0+} \dfrac{\mathbb{P}\{ g(t) \ge 0 \cap g(t+\Delta t) \leq 0\} }{\Delta t}$

For each $t$ , we note the random variable $Z_t = R - bt - S_t$ where $S_t = S(., t)$ .

To evaluate $\nu^+(t)$ , we need to consider the bivariate random vector $(Z_t, Z_{t+\Delta t})$ .

The event $\{ g(t) \geq 0 \cap g(t+\Delta t) <0\}$ writes as the intersection of both events :

$\mathcal{E}_t^1 = \{ Z_t \geq 0\}$ and
$\mathcal{E}_t^2 = \{ Z_{t+\Delta t} \leq 0\}$ .

The objective is to evaluate:

$\mathbb{P}\{\mathcal{E}_t^1 \cap \mathcal{E}_t^2\} \quad \forall t \in [0,T]$

1. Define some useful functions¶

We define the bivariate random vector $Y_t = (bt + S_t, b(t+\Delta t) + S_{t+\Delta t})$ . Here, $Y_t$ is a bivariate Normal random vector:

whith mean $[bt, b(t+\delta t)]$ and
whith covariance matrix $\Sigma$ defined by:

..math::: begin{align*} Sigma = left( begin{array}{cc} C(t, t) & C(t, t+Delta t) \ C(t, t+Delta t) & C(t+Delta t, t+Delta t) end{array} right) end{align*}

This function buils $Y_t =(Y_t^1, Y_t^2)$ .

from math import sqrt
from openturns.viewer import View
import openturns as ot


def buildNormal(b, t, mu_S, covariance, delta_t=1e-5):
    sigma = ot.CovarianceMatrix(2)
    sigma[0, 0] = covariance(t, t)[0, 0]
    sigma[0, 1] = covariance(t, t + delta_t)[0, 0]
    sigma[1, 1] = covariance(t + delta_t, t + delta_t)[0, 0]
    return ot.Normal([b * t + mu_S, b * (t + delta_t) + mu_S], sigma)

This function creates the trivariate random vector $(R, Y_t^1, Y_t^2)$ where $R$ is independent from $(Y_t^1, Y_t^2)$ . We need to create this random vector because both events $\mathcal{E}_t^1$ and $\mathcal{E}_t^2$ must be defined from the same random vector!

def buildCrossing(b, t, mu_S, covariance, R, delta_t=1e-5):
    normal = buildNormal(b, t, mu_S, covariance, delta_t)
    return ot.BlockIndependentDistribution([R, normal])

This function evaluates the probability using the Monte Carlo sampling. It defines the intersection event $\mathcal{E}_t^1 \cap \mathcal{E}_t^2$ .

def getXEvent(b, t, mu_S, covariance, R, delta_t):
    full = buildCrossing(b, t, mu_S, covariance, R, delta_t)
    X = ot.RandomVector(full)
    f1 = ot.SymbolicFunction(["R", "X1", "X2"], ["X1 - R"])
    e1 = ot.ThresholdEvent(ot.CompositeRandomVector(f1, X), ot.Less(), 0.0)
    f2 = ot.SymbolicFunction(["R", "X1", "X2"], ["X2 - R"])
    e2 = ot.ThresholdEvent(ot.CompositeRandomVector(f2, X), ot.GreaterOrEqual(), 0.0)
    event = ot.IntersectionEvent([e1, e2])
    return X, event

def computeCrossingProbability_MonteCarlo(
    b, t, mu_S, covariance, R, delta_t, n_block, n_iter, CoV
):
    X, event = getXEvent(b, t, mu_S, covariance, R, delta_t)
    algo = ot.ProbabilitySimulationAlgorithm(event, ot.MonteCarloExperiment())
    algo.setBlockSize(n_block)
    algo.setMaximumOuterSampling(n_iter)
    algo.setMaximumCoefficientOfVariation(CoV)
    algo.run()
    return algo.getResult().getProbabilityEstimate() / delta_t

This function evaluates the probability using the Low Discrepancy sampling.

def computeCrossingProbability_QMC(
    b, t, mu_S, covariance, R, delta_t, n_block, n_iter, CoV
):
    X, event = getXEvent(b, t, mu_S, covariance, R, delta_t)
    algo = ot.ProbabilitySimulationAlgorithm(
        event,
        ot.LowDiscrepancyExperiment(ot.SobolSequence(X.getDimension()), n_block, False),
    )
    algo.setBlockSize(n_block)
    algo.setMaximumOuterSampling(n_iter)
    algo.setMaximumCoefficientOfVariation(CoV)
    algo.run()
    return algo.getResult().getProbabilityEstimate() / delta_t

This function evaluates the probability using the FORM algorithm for event systems..

def computeCrossingProbability_FORM(b, t, mu_S, covariance, R, delta_t):
    X, event = getXEvent(b, t, mu_S, covariance, R, delta_t)
    algo = ot.SystemFORM(ot.SQP(), event, X.getMean())
    algo.run()
    return algo.getResult().getEventProbability() / delta_t

2. Evaluate the probability¶

First, fix some parameters: $(\mu_R, \sigma_R, \mu_S, \sigma_S, \Delta t, T, b)$ and the covariance model which is the Squared Exponential model. Be careful to the parameter $\Delta t$ which is of great importance: if it is too small, the simulation methods have problems to converge because the correlation rate is too high between the instants $t$ and $t+\Delta t$ . We advice to take $\Delta t \simeq 10^{-1}$ .

mu_S = 3.0
sigma_S = 0.5
ll = 10

b = 0.01

mu_R = 5.0
sigma_R = 0.3
R = ot.Normal(mu_R, sigma_R)

covariance = ot.SquaredExponential([ll / sqrt(2)], [sigma_S])

t0 = 0.0
t1 = 50.0
N = 26

# Get all the time steps t
times = ot.RegularGrid(t0, (t1 - t0) / (N - 1.0), N).getVertices()

delta_t = 1e-1

Use all the methods previously described:

Monte Carlo: values in values_MC
Low discrepancy suites: values in values_QMC
FORM: values in values_FORM

values_MC = list()
values_QMC = list()
values_FORM = list()

for tick in times:
    values_MC.append(
        computeCrossingProbability_MonteCarlo(
            b, tick[0], mu_S, covariance, R, delta_t, 2**12, 2**3, 1e-2
        )
    )
    values_QMC.append(
        computeCrossingProbability_QMC(
            b, tick[0], mu_S, covariance, R, delta_t, 2**12, 2**3, 1e-2
        )
    )
    values_FORM.append(
        computeCrossingProbability_FORM(b, tick[0], mu_S, covariance, R, delta_t)
    )

print("Values MC = ", values_MC)
print("Values QMC = ", values_QMC)
print("Values FORM = ", values_FORM)

Values MC =  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.00030517578125, 0.0, 0.0006103515625, 0.0, 0.0, 0.0, 0.0006103515625, 0.0, 0.0006103515625, 0.0006103515625, 0.00030517578125, 0.00030517578125, 0.0, 0.0006103515625, 0.0006103515625, 0.00030517578125, 0.001220703125, 0.00091552734375, 0.0006103515625, 0.001220703125]
Values QMC =  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0006103515625, 0.0, 0.0006103515625, 0.0, 0.0, 0.0, 0.00030517578125, 0.00030517578125, 0.0, 0.00030517578125, 0.0, 0.00091552734375, 0.0006103515625, 0.0, 0.00030517578125, 0.0, 0.0006103515625, 0.001220703125, 0.00091552734375, 0.0006103515625]
Values FORM =  [6.407247215635151e-05, 7.202731335077214e-05, 8.087457564322269e-05, 9.070185003762527e-05, 0.00010160352566792341, 0.00011368175043642132, 0.0001270463113567539, 0.00014181490999748653, 0.00015811435561321604, 0.00017607979141239492, 0.00019585595855828544, 0.0002175971122863016, 0.0002414674411439194, 0.0002676410529682008, 0.0002963031348934401, 0.0003276489827287258, 0.0003618851406896481, 0.0003992284220557085, 0.00043990704747756264, 0.0004841609222492271, 0.0005322401306591526, 0.0005844062178788379, 0.0006409303353549256, 0.0007020945630748503, 0.0007681919142532408, 0.0008395236026959429]

Draw the graphs!

g = ot.Graph()
g.setAxes(True)
g.setGrid(True)
c = ot.Curve(times, [[p] for p in values_MC])
g.add(c)
c = ot.Curve(times, [[p] for p in values_QMC])
g.add(c)
c = ot.Curve(times, [[p] for p in values_FORM])
g.add(c)
g.setLegends(["MC", "QMC", "FORM"])
g.setColors(["red", "blue", "black"])
g.setLegendPosition("topleft")
g.setXTitle("t")
g.setYTitle("Outcrossing rate")
view = View(g)
view.ShowAll()

Total running time of the script: ( 0 minutes 1.787 seconds)

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Time variant system reliability problem¶

1. Define some useful functions¶

2. Evaluate the probability¶