Gaussian mixture model classifiers for machine monitoring

We describe a statistical pattern-recognition approach to machine monitoring. The approach comprises a classification scheme using Gaussian mixture models (GMMs) that classifies features based on a time-frequency representation using the wavelet transform. The GMM trained with the EM algorithm has comparable flexibility with the multilayered perceptron in modeling nonstationary, multimodal machine signal characteristics, but has significantly fewer parameters to train. Also, using an example set of machine signals we show that the wavelet transform is particularly appropriate for capturing the time-frequency properties of transients of varying time constants and harmonic content. The benefits of both the GMM classifier and wavelet representation are manifested in superior classification performance and much lower computational complexity, as well as better robustness to finite-sample effects