This work focus on an unsupervised,data driven statistical approach to detect and monitor fatigue crack growth in lug joint samples using surface mounted piezoelectric sensors.Early and faithful detection of fatigue cracks in a lug joint can guide in taking preventive measures,thus avoiding any possible fatal structural failure.The lug joint samples used in this paper are prepared from an aluminum alloy plate with 6 mm thickness and are instrumented with a surface mounted piezoelectric sensor network.Experiments are conducted on three lug joints under constant fatigue loading.The fatigue loading was stopped every 1000 cycles after any small crack was spotted,and the piezoelectric signals corresponding to narrow-band actuations were acquired at every fatigue-loading-stopped cycle.The on-line damage state at any given fatigue cycle is estimated using a damage index approach as the dynamical properties of a structure change with the initiation of a new crack or the growth of an existing crack.Using the measurements performed on an intact lug joint as baseline,damage indices are evaluated from the frequency response of the lug joint with an unknown damage state.As the damage indices are evaluated,a Bayesian analysis is committed and a statistical metric is evaluated to identify damage state.
Yongming FENG Li ZHOU Hongming CAI Jinniu TAO Kezhong TANG
State Key Laboratory of Mechanics and Control of Mechanical Structures,Nanjing University of Aeronau State Key Laboratory of Mechanics and Control of Mechanical Structures,Nanjing University of Aeronau Department of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing,Jiangsu,21 The First Aeronautical Institute of Air Force,Xinyang 46400,Henan,P.R.China 95841 Troops,Peoples Liberation Army,Jiuquan 435018,Gansu,P.R.China