Predict the results of a future scheduled inspection and update the state accordingly. The state prior to inspection is utilized to determine the likelihood of finding each particle, and the state after inspection consists of a combination of missed particles and repaired particles. The Probability of Crack Detection (PCD) results of this inspection are appended to the previously existing PCD results (if any).

1 2 3 4 5 6 7 | ```
inspection(obj,inspection.type=1)
## S3 method for class 'Sing'
inspection(obj,inspection.type=1)
## S3 method for class 'Mult'
inspection(obj,inspection.type=1)
## S3 method for class 'CD'
inspection(obj,inspection.type=1)
``` |

`obj` |
Object of class crackR |

`inspection.type` |
Integer, index of which Probability Of Detection (POD) function from the parameters to utilize for this inspection. |

The likelihood of finding each particle at a future inspection depends only
on the crack length at that time and the POD function (as specified by
inspection.type). The overall Probability of Crack Detection (PCD) is
found by taking a weighted average of the probability of detection for
each particle and the importance weights.

Suppose there is a 40% chance of finding a particular
particle. That particle will remain in the state, but to reflect the
possibility that it was not found, the weight is reduced to 60% of
the weight prior to inspection. The remaining weight will be used when
generating new repaired particles (by sampling from the repair flaw
size distribution). The total weight that is found for all particles
is the estimate of PCD for this inspection (optionally partitioned
into several crack length ranges using pod.threshold). After inspection, the weight of the
repaired particles will sum to PCD, and the weight of the missed
particles will sum to (1-PCD). Note also that the set of particles
will be re-sampled without replacement to reduce the particle count
back to parameters$Np.

Object of class crackR.

Keith Halbert <keith.a.halbert@gmail.com>

Halbert, K. "Estimation of Probability of Failure for Damage-Tolerant Aerospace Structures" PhD Thesis, Temple University Department of Statistics, Philadelphia, PA, Apr 2014

`crackRinit`

`analyze`

`calcInterval`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
set.seed(327)
data(cp7ext)
## initialize crackR object
cp7ext.init <- crackRinit(cp7ext)
## advance through time 6000 flights
cp7ext.before.insp <- calcInterval(cp7ext.init, interval.flights=6000)
## conduct inspection
cp7ext.after.insp <- inspection(cp7ext.before.insp)
## print inspection results
cp7ext.after.insp$results$pcd
``` |

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