Development of an artificial neural network damage detection module for a structural health monitoring system
Aircraft, wind turbines, or space stations are expected to remain in service well beyond their designed performance lifetime. Consequently, maintenance is an important issue for aircraft or aerospace structures. This is accomplished through inspecting for damage at scheduled times and replacing damaged parts before failure. Ground inspections of aircraft, even using simple nondestructive testing techniques, generally require the aircraft be pulled from operation so that its components can be inspected for damage. Structural components are replaced if sufficient damage is found. Research is underway to develop a structural health monitoring (SHM) system as a means to improve the current maintenance routine. This system would consist of an array of sensors and associated analysis codes which would scan for damage in-flight and perform real-time damage analysis of an aircraft’s structure. If damage is recognized long before failure occurs, then a damage tolerance and prognostic assessment could be implemented, allowing for a determination of the remaining life of components. The current method of inspecting aircraft, consisting of ground inspections for damage after a set number of flight hours, works well from an aircraft safety point of view. However, an in-flight SHM system would allow for better use of components, as specific lifetimes could be determined; and, could be less costly, since an SHM system could be embedded into the aircraft structure, thereby reducing or eliminating the need to tear down the aircraft to scan for damage during the ground inspection and would ultimately lead to fewer required ground inspections. General theory for material mechanics, fracture mechanics, waveform theory, and artificial neural networks are presented in this paper. Among these, a simple triangulation method is developed to locate a crack tip position and a procedure of combining the theory of fracture mechanics with waveform theories is introduced. These components were used collectively in a series of experiments to investigate the possibility of using them in a future SHM system. Flat aluminum panels, similar in thickness to those found in many aerospace structures, were subjected to increasing static loading during laboratory tests. As the load increased, a designed crack in the panel increased in size, releasing strain waves into the material. These waves were then detected by acoustic emission sensors, and artificial neural networks were implemented to analyze the received strain waves. From a feed-forward neural network, the crack length was approximated. Next, similar aluminum panels were placed in a simply supported beam configuration with ultrasonic actuators attached at various positions. These actuators created multiple point source locations, which was received by multiple acoustic emission sensors. The location of the source was calculated by both triangulation method and an vi artificial neural network. A theory of plastic zone interference with strain waves released from crack tip extension was introduced and shown as a possibility during the analysis of a final experiment. Sensors placed behind the crack front were observed to detect waves with smaller amplitudes than the sensors placed in locations in front of the crack during crack extension due to increasing, pseudo-static applied load. The effect of the acoustic emission sensor placement relative to crack tip growth detections was determined to be possibly integrated with an artificial neural network in future research. Experiments were conducted to determine the crack length and location, using artificial neural network analyses of acoustic emission signals. Artificial neural networks were developed which were trained with an existing dataset of crack properties. These neural networks were applied to new situations that were not part of the training dataset. The approximated crack growth of the artificial neural networks was around 10% shorter than the actual measured crack growth length for an extension of around 0.8 in. Finally, some SHM analysis systems were proposed, based on the conclusions made in the experiments. The artificial neural networks performed well at approximating both the crack extension and location, using acoustic emission detections. These artificial neural networks in combination with an acoustic emission system are reasonable candidate for the initial stages of a feasible structural health monitoring system to determine the location and severity of structural damage within an aerospace structure.
Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Aerospace Engineering