Probabilistic structural risk assessment for fatigue management using structural health monitoring

Michael Shiao; Y-T. Justin Wu; Anindya Ghoshal; James Ayers; Dy Le

doi:10.1117/12.915660

4 April 2012 Probabilistic structural risk assessment for fatigue management using structural health monitoring

Michael Shiao, Y-T. Justin Wu, Anindya Ghoshal, James Ayers, Dy Le

Proceedings Volume 8347, Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2012; 834724 (2012) https://doi.org/10.1117/12.915660
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2012, San Diego, California, United States

Abstract

The primary goal of Army Prognostics & Diagnostics is to develop real-time state awareness technologies for primary structural components. In fatigue-critical structural maintenance, the probabilistic structural risk assessment (PSRA) methodology for fatigue life management using conventional nondestructive investigation (NDI) has been developed based on the assumption of independent inspection outcomes. When using the emerging structural health monitoring (SHM) systems with in situ sensors, however, the independent assumption no longer holds, and the existing PSRA methodology must be modified. The major issues currently under investigation are how to properly address the correlated inspection outcomes from the same sensors on the same components and how to quantify its effect in the SHM-based PSRA framework. This paper describes a new SHM-based PSRA framework with a proper modeling of correlations among multiple inspection outcomes of the same structural component. The framework and the associated probabilistic algorithms are based on the principles of fatigue damage progression, NDI reliability assessment and structural reliability methods. The core of this framework is an innovative, computationally efficient, probabilistic method RPI (Recursive Probability Integration) for damage tolerance and risk-based maintenance planning. RPI can incorporate a wide range of uncertainties including material properties, repair quality, crack growth related parameters, loads, and probability of detection. The RPI algorithm for SHM application is derived in detail. The effects of correlation strength and inspection frequency on the overall probability of missing all detections are also studied and discussed.

Citation Download Citation

Michael Shiao, Y-T. Justin Wu, Anindya Ghoshal, James Ayers, and Dy Le "Probabilistic structural risk assessment for fatigue management using structural health monitoring", Proc. SPIE 8347, Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2012, 834724 (4 April 2012); https://doi.org/10.1117/12.915660

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