Acoustic emission (AE) is a well-known technique for monitoring onset and propagation of material damage. The technique has demonstrated utility in assessment of metallic and composite materials in applications ranging from civil structures to aerospace vehicles. While over the course of few decades AE hardware has changed dramatically with the sensors experiencing little changes. A traditional acoustic emission sensor solution utilizes a thickness resonance of the internal piezoelectric element which, coupled with internal amplification circuit, results in relatively large sensor footprint. Thin wafer piezoelectric sensors are small and unobtrusive, but they have seen limited AE applications due to low signalto-noise ratio and other operation difficulties. In this contribution, issues and possible solutions pertaining to the utility of thin wafer piezoelectrics as AE sensors are discussed. Results of AE monitoring of fatigue damage using thin wafer piezoelectric and conventional AE sensors are presented.
Increasing complexity of aerospace structures facilitates a growing need for structural health monitoring (SHM) systems capable of real-time active damage detection. A variety of sensing approaches have been demonstrated using embedded ultrasonic sensors such as piezoelectric wafer active sensors (PWAS) and magneto-elastic active sensors (MEAS). Common methodologies consider wave propagation (pitch-catch or pulse-echo) and standing wave (vibration or impedance) techniques with damage detection capabilities dependent upon structural geometry, material characteristics, distance to damage and damage size/orientation. While recent studies have employed damage detection and classification approaches that are dependent on cumulative statistics, this study explores the contribution of sensor parameters and experimental setup variability on the damage detection scheme. The impact of variability in PWAS and MEAS are considered on sensor use in ultrasonic and magneto-mechanical impedance damage detection. In order to isolate sensor parameters, measurements were conducted with PWAS in free-free boundary conditions. Variability of PWAS parameters was evaluated by measuring the sensors impedance response. An analytical model of PWAS was used to estimate sensor parameters and to determine their variability. Additionally, experiments using MEAS were performed that demonstrate variation of magneto-mechanical impedance during structural dynamic tests. From these experiments the importance of sensor setup is discussed and its contribution into the overall detection scheme is explored.
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