The nondestructive inspection interval is highly related with both system reliability and maintenance burden. Conventional inspection interval decision criteria based on the deterministic crack propagation analysis could require too much frequent inspection or sometimes occur structural failure owing to the rapid crack propagation than expected. The stochastic crack growth analysis method was proposed to compensate for the shortcomings of the deterministic analysis. This research studied the crack growth of aircraft landing gear components based on the equivalent initial flaw size distribution algorithm, and then we assessed failure risk. The calculated risk was validated using Monte-Carlo simulation, and finally, the optimum inspection interval was proposed to satisfy the US Airforce risk management criteria.

The reliability of a jet engine compressor rotor blade containing a fatigue crack has been assessed based on the eddy current inspection (ECI) response of both the actual rotor blade and bolt hole specimens containing cracks of known lengths. The detection threshold and the probability of detection (POD) curve have been determined. A dynamic Bayesian network (DBN) model was used to quantify uncertainties. The model encompasses a realistic ECI response model, so that it is possible to consider all relevant inspection data types. Factors which contribute the most to the variation of crack length have been determined by sensitivity analysis and have been calibrated using the field inspection data. Part of the inspection data was used to validate the calibrated model, and a Bayes factor of 9.93 which corresponds to a confidence level of 91% has been obtained. Based on the control level for the reliability index βctrl = 3, and the reliability indices calculated from the calibrated model, the recommended interval for the first inspection has been determined as 1600 hrs. This interval is smaller than the current interval which is 3200 hrs.

No abstract

No abstract

The predetection evolution of stress corrosion cracking has been examined as a necessary preliminary to effective detection of such cracks. Anodic dissolution (AD) and hydrogen embrittlement (HE) have been considered to calculate the stress corrosion crack (SCC) growth in AA7050-T6 for a surface-breaking crack with blunt tip in an aqueous environment. Since these processes are not completely deterministic, several advanced statistical methods have been used to introduce probabilistic considerations. Based on the data from designed computer experiments, the computer code developed by the authors (Lee et al., 2015, “A Comprehensive Analysis of the Growth Rate of Stress Corrosion Cracks,” Proc. R. Soc. A, 471(2178), p. 20140703) to conduct deterministic stress corrosion crack growth analysis has been represented by metamodels using Gaussian process regression. Through sensitivity analysis, important variables which need to be calibrated have been identified. The dynamic Bayesian network (DBN) model and Monte Carlo simulation (MCS) have been utilized to quantify uncertainties. Statistical parameters of input variables have been obtained by a machine learning technique. The calibrated model has been validated using Bayesian hypothesis testing. Since the DBN model yields a probability of detection (POD) comparable to the probability based on binary validation data, the probabilistic model with calibrated parameters is expected to well represent the growth of a stress corrosion crack. The results also show that the reliability largely depends on the accuracy of flaw detection methods and on the critical crack length.