Health monitoring of aerospace structures can be done passively by listening for acoustic waves generated by cracks, impact damage and delaminations, or actively by propagating diagnostic stress waves and interpreting the parameters that characterize the wave travel. This paper investigates modeling of flexural wave propagation in a plate and the design of sensors to detect damage in plates based on stress wave parameters. To increase understanding of the actual physical process of wave propagation, a simple model is developed to simulate wave propagation in a plate with boundaries. The waves can be simulated by applied forces and moments in the model either to represent passive damage growth or active wave generation using piezoceramic actuators. For active wave generation, the model considers a piezoceramic patch bonded perfectly to a quasi-isotropic glass-epoxy composite plate. Distributed sensors are used on the plate and are modeled as being constructed using active fiber composite and piezoceramic materials. For active wave generation, a moment impulse is generated by the actuation of a piezoceramic patch. The waves generated from the patch are detected by the distributed sensor. For passive sensing of acoustic waves, a step function is used to simulate an acoustic emission from a propagating damage. The resulting acoustic wave is measured by the distributed sensor and produces micro-strains in the sensor nodes. The strains produce a single voltage signal output from the distributed sensor. Computational simulations and animations of acoustic wave propagation in a plate are discussed in the paper. A new method to locate the source of an acoustic emission using the time history of the dominant lower frequency components of the flexural wave mode detected by continuous sensors is also presented.
Recent structural health monitoring techniques have focused on developing global sensor systems that can detect damage on large structures. The approach presented here uses a piezoelectric sensor array system that mimics the biological nervous system architecture to measure acoustic emissions and dynamic strains in structures. The advantage of this approach is that the number of channels of data acquisition used for an N-by-N sensor array can be reduced from N<SUP>2</SUP> to 2N. For large arrays the number of data acquisition channels is tremendously reduced. When transient damage events occur on the structure, the array output time histories can be recorded and the location of the excitation can be accurately determined using combinatorial logic. A trade-off is the difficulty of extracting individual sensor time histories from the array outputs without a neural network or a regressive technique. Only the sums of the sensor strains of each row and column can be exactly calculated using the voltage outputs of the array. The array approach allows efficient use of data acquisition instrumentation for structural health monitoring. Applications for the sensor array include crack and delamination detection, dynamic strain measurement, impact detection, and localization of damage on large complex structures.
This is an overview paper that discusses the concept of an embeddable structural health monitoring system for use in composite and heterogeneous material systems. The sensor system is formed by integrating groups of autonomous unit cells into a structure, much like neurons in biological systems. Each unit cell consists of an embedded processor and a group of distributed sensors that gives the structure the ability to sense damage. In addition, each unit cell periodically updates a central processor on the status of health in its neighborhood. This micro-architectured synthetic nervous system has an advanced sensing capability based on new continuous sensor technology. This technology uses a plurality of serially connected piezoceramic nodes to form a distributed sensor capable of measuring waves generated in structures by damage events, including impact and crack propagation. Simulations show that the neural system can detect faint acoustic waves in large plates. An experiment demonstrates the use of a simple neural system that was able to measure simulated acoustic emissions that were not clearly recognizable by a single conventional piezoceramic sensor.