Nearest Neighbor-Time Series Analysis Classification of Faults in Rotating Machinery

Abstract

A unified nearest neighbor-time series analysis approach to the problem of the classification of faults in rotating machinery is developed. The procedure has an optimum minimum probability of misclassification property for normally distributed time series and near optimum misclassification properties otherwise. Examples of the classification of acceleration, pressure, and torque sensor data from stationary, locally stationary, and covariance stationary time series with mean value time functions are considered. Estimates of the probability of misclassification are computed for each situation. The underlying assumptions and properties of the nearest neighbor time series classification procedure and signature analysis procedures are compared.