Machine performance monitoring and fault classification using an exponentially weighted moving average scheme

Abstract

The present invention provides an accurate machine monitoring technique based on vibration analysis. An AR parametric model is generated to characterize a normal machine condition. Subsequently, data is collected from a machine during operation. This data is fit to the AR parametric model, and an Exponentially Weighted Moving Average (EWMA) statistic is derived therefrom. The EWMA statistic is able to identify whether the machine is in a normal state ("in control") or in an abnormal state ("out of control"). Additionally, an EWMA control chart is generated that distinguishes between normal and abnormal conditions, and between different abnormal conditions. As a result, once the EWMA statistic is generated, it is compared to the EWMA chart for determination of the specific fault that is ailing the machine.