The application of bispectral and trispectral analysis in condition monitoring is discussed. Higher-order spectral analysis of machine vibrations for the provision of diagnostic features is investigated. Experimental work is based on vibration data collected from a small test rig subjected to bearing faults. The direct use of the entire bispectrum or trispectrum to provide diagnostic features is investigated using a variety of classification algorithms including neural networks, and this is compared with simpler power spectral and statistical feature extraction algorithms. A more detailed investigation of the higher-order spectral structure of the signals is then undertaken. This provides features which can be estimated more easily in practice and could provide diagnostic information about the machines.
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