Abstract
This paper presents an experimental study for detecting fatigue cracks near a hole in an aluminium coupon under complex environment effects using guided waves and a Bayesian statistical inference method. Experimental set-up is established to simulate the combined conditions of temperature, load and vibration. PZT transducers are mounted on the coupon to excite and receive guided waves, and random samples of guided wave signals under different environmental conditions before and after cracking are recorded. After features of the guided wave signals in the frequency domain are extracted, a Bayesian interval hypothesis testing is employed to assess the feature differences of the two states to make inference that whether fatigue cracks exist or not and give a confidence level. Experimental results have demonstrated the effectiveness of the proposed method.