Artificial nerve system for structural monitoring
William N. Martin, Anindya Ghoshal, Mannur J. Sundaresan, G.L. Lebby, Mark J. Schulz, Promod R. Pratap · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2002
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 N2 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.