Crack Identification in Solid Rocket Motors Through the Neyman–Pearson Detection Theory

Abstract

Solid rocket motors (SRMs) are prone to bore cracking due to material degradation mechanisms and temperature changes that occur during storage and service life, and therefore early damage detection is of crucial importance. Structural health monitoring (SHM) strategies aim at measuring the load redistribution caused by a crack through embedded strain sensors. By acknowledging the existence of uncertainties, both in the material and measurement systems, this work employs a Neyman–Pearson detector that treats the crack identification problem as a binary statistical pattern recognition one. A typical SRM geometry, at its healthy state and with a bore crack of variable size, is considered in a probabilistic computational setting. A surrogate model is trained with synthetic data generated from a physics-based finite element model and then used for uncertainty propagation. Detection is first treated as a deterministic signal within noise, and next as an uncertain signal described by a probabilistic distribution. The system’s architecture is assessed through a procedure for arriving at the optimal number and location for sensor placement in conjunction with the SHM’s detection performance. The latter is described by receiver operating characteristic curves.

Structural Health Monitoring of Solid Rocket Motors: From Destructive Testing to Perspectives of Photonic-Based Sensing

“…At the same time, they make it possible to detect subtle changes that could go unnoticed through traditional analysis methods. Scientific approaches published in the field of SHM depict the necessity to study uncertainties as a crucial aspect affecting the interpretation of sensor data in the route to diagnosing the structural integrity of a material; however, only a restricted number of studies explicitly refer to SRM models [83,84].…”

On the Detection of Thickness Loss in Ship Hull Structures Through Strain Sensing

“…On account of their prevalence and importance to structural safety, fatigue cracks have been a focal point of SHM research [6] with works dealing with all different levels of the SHM hierarchy proposed by Rytter [7]; namely, damage detection [8,9,10], localization [11,12,13], quantification [14,15,16] and prognosis [17,18,19]. Naturally, the prognostic aspect of SHM is of particular interest when it comes to fatigue crack growth [20,21], as its focus is to obtain probabilistic predictions of the evolution of structural deterioration.…”