Real-Time Fatigue Crack Growth Rate Estimation Methodology for Structural Health Monitoring of Ships

Fatigue experimental setups for studying the crack growth propagation in a high-strength low-alloy DMR 249A ship steel were arranged by loading the specimen with the real sea-state conditions value of 4 for the application of structural health monitoring of ships. The experimental setup consists of a fatigue loading machine, acoustic emission (AE) sensors, and AE nodes for preprocessing of data. In this investigation, a methodology to identify the crack propagation phenomenon in a specimen independent of the AE parameters has been developed. This methodology proves beneficial in identifying the noise and crack information in ship steel by creating the phase portraits of the time domain signal and indicating the same onto the phase portraits. A polynomial regression-based model for estimating the crack growth rate (CGR) in the material has been developed by introducing a new parameter mean of box count (MBC).

Automated summary

Fatigue experimental setups were used to study crack growth propagation in high-strength low-alloy DMR 249A ship steel under real sea-state conditions. A methodology independent of acoustic emission (AE) parameters was developed to identify crack propagation phenomenon in the specimen. This study also introduced a polynomial regression-based model for estimating the crack growth rate (CGR) in the material.