This paper presents a systematic investigation of the effect of sensor location on the data quality and subsequently, on the effectiveness of machine health monitoring. Based on an analysis of the signal propagation process from the defect location to the sensor, numerical simulations using finite element modeling were conducted on a bearing test bed to determine the signal strength at several representative sensor locations. The results showed that placing sensors closely to the machine component being monitored is critical to achieving high signal-to-noise ratio, thus improving the data quality. Using millimeter-sized piezoceramic plates, the obtained results were evaluated experimentally. A comparison with a set of commercial vibration sensors verified the developed structural dynamics-based sensor placement strategy. It further demonstrated that the proposed shock wave-based sensing technique provided an effective alternative to vibration measurement, while requiring less space for sensor installation.

A new design of distributed crack sensors based on the topological change of transmission line cables is presented for the condition assessment of reinforced concrete (RC) structures during and immediately after an earthquake event. This study is primarily focused on the performance of cable sensors under dynamic loading, particularly a feature that allows for some "memory" of the crack history of an RC member. This feature enables the post-earthquake condition assessment of structural members such as RC columns, in which the earthquake-induced cracks are closed immediately after an earthquake event due to gravity loads, and are visually undetectable. Factors affecting the onset of the feature were investigated experimentally with small-scale RC beams under cyclic loading. Test results indicated that both crack width and the number of loading cycles were instrumental in the onset of the memory feature of cable sensors. Practical issues related to dynamic acquisition with the sensors are discussed. The sensors were proven to be fatigue resistant from shake table tests of RC columns. The sensors continued to show useful performance after the columns can no longer support additional loads.

The paper intends to summarize some guidelines for future smart structure system application in military aircraft. This preview of system integration is based upon a review on approximately one and a half decades of application oriented aerospace related smart structures research. Achievements in the area of structural health monitoring, adaptive shape, adaptive load bearing devices and active vibration control have been reached, potentials have been identified, several feasibility studies have been performed and some smart technologies have been already implemented. However the realization of anticipated visions and previously initial timescales announced have been rather too optimistic. The current development shall be based on a more realistic basis including more emphasis on fundamental aircraft strength, stiffness, static and dynamic load and stability requirements of aircraft and interdisciplinary integration requirements and improvements of integrated actors, actuator systems and control systems including micro controllers.

Advanced signal processing techniques have been long introduced and widely used in structural health monitoring (SHM) and nondestructive evaluation (NDE). In our research, we applied several signal processing approaches for our embedded ultrasonic structural radar (EUSR) system to obtain improved damage detection results. The EUSR algorithm was developed to detect defects within a large area of a thin-plate specimen using a piezoelectric wafer active sensor (PWAS) array. In the EUSR, the discrete wavelet transform (DWT) was first applied for signal de-noising. Secondly, after constructing the EUSR data, the short-time Fourier transform (STFT) and continuous wavelet transform (CWT) were used for the time-frequency analysis. Then the results were compared thereafter. We eventually chose continuous wavelet transform to filter out from the original signal the component with the excitation signal's frequency. Third, cross correlation method and Hilbert transform were applied to A-scan signals to extract the time of flight (TOF) of the wave packets from the crack. Finally, the Hilbert transform was again applied to the EUSR data to extract the envelopes for final inspection result visualization. The EUSR system was implemented in LabVIEW. Several laboratory experiments have been conducted and have verified that, with the advanced signal processing approaches, the EUSR has enhanced damage detection ability.

Elastic and electromagnetic waves can be used to gather important information about particulate materials. To facilitate smart geophysical characterization of particulate materials, their fundamental properties are discussed and experimental procedures are presented for both elastic and electromagnetic waves. The first application is related to the characterization of particulate materials using shear waves, concentrating on changes in effective stress during consolidation, multi-phase phenomena with relation to capillarity, and microscale characteristics of particles. The second application involves electromagnetic waves, focusing on stratigraphy detection in layered soils, estimation of void ratio and its spatial distribution, and conduction in unsaturated soils. Experimental results suggest that shear waves allow studying particle contact phenomena and the evolution of interparticle forces, while electromagnetic waves give insight into the characteristics of the fluid phase and its spatial distribution.