Structural health monitoring (SHM) is being widely evaluated by the aerospace industry as a method to improve the safety and reliability of aircraft structures and also reduce operational cost. Built-in sensor networks on an aircraft structure can provide crucial information regarding the condition, damage state and/or service environment of the structure. Among the various types of transducers used for SHM, piezoelectric materials are widely used because they can be employed as either actuators or sensors due to their piezoelectric effect and vice versa. This paper provides a brief overview of piezoelectric transducer-based SHM system technology developed for aircraft applications in the past two decades. The requirements for practical implementation and use of structural health monitoring systems in aircraft application are then introduced. State-of-the-art techniques for solving some practical issues, such as sensor network integration, scalability to large structures, reliability and effect of environmental conditions, robust damage detection and quantification are discussed. Development trend of SHM technology is also discussed.

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A procedure for the synthesis of three-dimensional hexakisimidazolium cage compounds has been developed. The reaction of the trigonal trisimidazolium salts H L(PF ) , decorated with three N-olefinic pendants, and silver oxide yielded trinuclear trisilver(I) hexacarbene molecular cylinders of the type [Ag L ] with the olefinic pendants from the two different tricarbene ligands arranged in three pairs. Subsequent UV irradiation gave three cyclobutane links between the two tris-NHC ligands in three [2+2] cycloaddition reactions, thereby generating a three-dimensional hexakis-NHC ligand. Removal of the metal ions resulted in the formation of three-dimensional hexakisimidazolium cages with a large internal cavity.

Probability-based diagnostic imaging, as one of the damage identification methods using ultrasonic guided waves, has been attracting increasing attention by researchers in the community of structural health monitoring. However, the probability-based diagnostic imaging algorithm’s influencing parameters, including the selection of certain damage index and frequency, the network of sensing paths, and the size of the effective elliptical distribution area, are empirically determined. This experience dependency limits the application of the method to identify damages in real-world practices. Therefore, it is important to clarify the influences of the above-mentioned various factors on the damage identification. However, the complexity of these factors makes it almost impossible to interpret the influencing mechanisms directly. Thus, a fusion image approach of multiple frequencies is employed to eliminate the influence of different frequencies, while a histogram-based method is proposed to evaluate the reliability of the fusion result. Meanwhile, a unit weight distribution function, considering both the network of sensing paths and the size of the effective elliptical distribution area, is presented in the analysis. Then, the influencing mechanisms are studied and discussed in detail, and a methodology is proposed to optimize the network and the scaling parameter which controls the affected zone.

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