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

Abstract

Prognostics and health management (PHM) techniques have been widely studied in recent years to increase reliability, availability, safety, and reducing maintenance costs of safe-critical systems, like aircraft and power plants. In these systems, fatigue cracking is still one of the most widespread problems affecting structural safety. However, it is difficult to determine the structure’s fatigue life of an individual system due to uncertainties arising from various sources such as intrinsic material properties, loading, and environmental factors. Even fatigue lives of the same specimens under laboratory tests have large dispersion. To deal with this problem, this paper introduces a fatigue crack growth prognosis method with the particle filter (PF) and on-line guided wave structural health monitoring (SHM) data. The guided wave-based SHM technique is adopted for on-line monitoring the presence and size of the fatigue crack. Besides, the monitored data is sequentially combined for correcting a physical fatigue crack growth model within the PF algorithm. Finally, the data of the fatigue tests of the hole-edge crack is used for demonstrating the proposed method.