<p>Prognostics and health management (PHM) techniques have been widely studied in recent years to increase system reliability, availability, safety and to reduce the maintenance cost of engineering assets. In the PHM technique, a key issue is to design robust and accurate prognosis method of the damage in the system structure for decision making. In engineering structures, fatigue cracking is one of the most widespread problems affecting structural safety. Prognosis of fatigue crack growth for determining the structural remaining useful life (RUL) is of great significance, however, a challenging task due to uncertainties arising from various sources such as intrinsic material properties, loading, and environmental factors. Traditional analytical and numerical simulation methods that only focus on deterministic aspects may have large errors to deal with the prognosis problem of fatigue crack growth affected by uncertainties. This problem could be potentially solved with the development of structural health monitoring (SHM) techniques. The SHM technique adopts sensors permanently attached to the structure to acquire signals related to the crack state. Then the crack size can be continuously diagnosed with advanced signal processing and damage diagnosis algorithms. Based on these on-line crack observations accurate prognosis of fatigue crack growth can be achieved. In this context, the authors propose the on-line fatigue crack growth prognosis method by combing the guided wave-based SHM technique and the particle filter (PF) algorithm. The guided wave-based SHM uses piezoelectric transducers for exciting and acquiring guided waves in the structure, where the guided waves’ changes show the crack growth. Therefore, the guided wave-based SHM is deemed as one of the most appealing methods due to its merits including sensitivity to small damage and the ability to monitor a region. Besides, the PF algorithm is a robust Bayesian filtering method that can be used for state and parameter estimation. In this prognosis method, once a new guided wave-based SHM observation is collected on-line, it is fused in the PF framework for sequentially estimating the crack state and crack growth model’s parameters. Based on the posterior estimations, the prognosis of the fatigue crack growth can be carried out. Besides, real fatigue tests of typical structures are performed for validating the proposed method, in which the guided wave-based SHM is carried out for crack monitoring. The validation of the proposed on-line prognosis method shows some deficiency of the basic prognosis framework. Further, the authors propose some improvements to deal with these problems, including modifying the PF algorithm and on-line correcting the SHM model.</p>

Fatigue Crack Growth Prognosis With the Particle Filter and On-Line Guided Wave Structural Monitoring Data

Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2021/volume/85543

ASME 2021 International Mechanical Engineering Congress and Exposition (IMECE2021)

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