Simulation of Recurring Automated Inspections on Probability-of-Fracture Estimates

Abstract

On-board sensors that can detect and size a crack in a structural component are being developed and will be deployed to enhance structural health monitoring and prognosis. This research examines the simulation of recurring automated inspections resulting from simulated on-board `crack' sensors, and their potential effect on reducing the probability-of-fracture of structural components. The concept of a probability-of-detection (POD) curve is used to characterize the performance of the sensor, as done for traditional inspections. However, we assert that, unlike the simulation of traditional infrequent inspections, recurring inspections for an automated system should be modeled as dependent with respect to the first inspection due to the largely repeatable aspects of the sensor and data collection system. This assertion has a large effect on the computed probability of detecting a crack and alleviates the substantial over prediction of sensor efficacy generated using the assumption of independent inspections for automated systems. Furthermore, it is demonstrated that the fundamental feature that determines the efficacy of a recurring automated on-board sensor is the probability of detecting a crack of critical size, i.e., the size that will cause fracture, and this feature is by and large separate from the shape of the POD curve. This information can be used to determine the required accuracy of an on-board automated inspection to achieve a specified reliability of a structural component. The methodology is demonstrated using fatigue and fracture of a representative titanium compressor disk from a gas turbine aircraft engine but is applicable to any structural system with recurring automated inspections.

Keywords

probability-of-detection probabilistic crack growth nondestructive inspection simulation recurring inspections automated inspections

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