Clustering and classification techniques for the analysis of vibration signatures
Israel E. Alguindigue, Anna Loskiewicz-Buczak, Robert E. Uhrig · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
A methodology is proposed for the clustering and classification of vibration signatures in the frequency domain. The technique is based on the technologies of neural networks and fuzzy clustering and it is especially suited for the problem of vibration analysis because it permits the incorporation of specific knowledge about the domain in a very simple manner, and because the system learns from actual process data. The system uses the backpropagation algorithm for classification of compressed signatures, where compression is used as a mechanism for noise removal and automatic feature extraction. The clustering system uses the Fuzzy C algorithm with a matrix of weights for the calculation of distances between patterns and centroids. The matrix is used to assign factors of importance to frequencies in the spectrum which are known to be related to particular defects. The two aspects of the analysis (clustering and classification) are complementary because in many cases the exact operating state of a machine cannot be assessed, and clustering may unveil classes of operating states that would not be discovered otherwise. Accurate results were obtained from testing the system on rolling element bearing data.