Abstract

An acoustic signature recognition and identification system receives signals from a sensor placed on a designated piece of equipment. The acoustic data is digitized and processed, via a Fast Fourier Transform routine, to create a spectrogram image of frequency versus time. The spectrogram image is then normalized to permit acoustic pattern recognition regardless of the surrounding environment or magnitude of the acoustic signal. A feature extractor then detects, tracks and characterizes the lines which form the spectrogram. Specifically, the lines are detected via a KY process that is applied to each pixel in the line. A blob coloring process then groups spatially connected pixels into a single signal object. The harmonic content of the lines is then determined and compared with stored templates of known acoustic signatures to ascertain the type of machinery. An alert is then generated in response to the recognized and identified machinery.

Other References

• Ballard, D.H. and Brown, C.M., Computer Vision, p. 151, Prentice-Hall, 1982
• Russo, A.P., "Constrained Neural Networks for Recognition of Passive Sonar Signals Using Shape," IEEE Conference on Neural Networks in Ocean Engineering, Washinton, DC, pp. 69-76, Aug., 199