Abstract
The ability to perform accurate remnant life predictions is crucial to ensure the integrity of engineering components that experience fatigue loading during operation. This is conventionally achieved with periodic inspections, where results from non-destructive evaluation and estimation of the operating conditions are obtained to perform remnant life predictions using empirical crack growth laws. However, remnant life predictions made with this approach are very sensitive to their input parameters; uncertainty in each parameter would aggregate and result in great uncertainty in the final prediction. With the increasing viability of permanently-installed systems, it is proposed that the rate of damage growth can be used to more accurately and confidently gauge the integrity of an engineering component and perform remnant life predictions using the Failure Forecast Method. A statistical analysis of an example fatigue crack growth test was performed to compare the uncertainties of the remnant life predictions made using the conventional inspection approach and the proposed rate-based monitoring approach. It is shown that the Failure Forecast Method produces significantly more accurate and confident predictions compared to the inspection approach. The use of the Failure Forecast Method under non-constant amplitude loading conditions was also investigated. An equivalent cycles method is introduced to accommodate step changes in operating conditions. The effect of load interactions was also studied through a fatigue test with isolated overloads and a random variable amplitude loading test. Overall, the study has shown that the frequent data obtained from permanently installed monitoring systems provides new opportunities in remnant life estimates and potentially opens the way to increasing the intervals between outages and safely reducing conservatism in life predictions.
Highlights
Fatigue damage is considered to be one of the leading causes of failure in a range of engineering applications
Components found to contain defects are assessed to estimate whether the defect would propagate under its operating conditions, and if so, perform remnant life predictions (RLPs) using empirical crack growth laws
The fatigue crack growth rate behaviour has a characteristic form [15]. This behaviour was first noted by Voight [16], who subsequently developed the Failure Forecast Method (FFM), which utilises this characteristic form of damage accumulation rate to perform RLPs
Summary
Fatigue damage is considered to be one of the leading causes of failure in a range of engineering applications. Significant research is being conducted to develop technologies for on-line SHM of engineering components susceptible to fatigue damage These include ways of monitoring the operating conditions of components [10], ways of detecting defect initiations such as vibration response monitoring [11] and acoustic emissions monitoring [12], or ways to monitor crack growth [13]. The fatigue crack growth rate behaviour has a characteristic form [15] This behaviour was first noted by Voight [16], who subsequently developed the Failure Forecast Method (FFM), which utilises this characteristic form of damage accumulation rate to perform RLPs. Compared to conventional damage assessment methods, the FFM does not rely on assumptions of material properties, geometry, or operating conditions, but rather the observed response of the Figure 1: Plot of crack length against number of loading cycles for the fatigue experiment. The red crosses are analogous to data obtained via regular in-service inspections, while the blue dots represent what a PIMS can obtain
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