Machine performance monitoring and fault classification using an exponentially weighted moving average scheme
研究利用自回归模型和指数加权移动平均统计量，通过振动信号分析监测旋转机械的运行状态，判断是否正常或异常。
Performance monitoring is crucial in maintaining normal machine operating conditions for the continued production of high quality parts. The objective of the following research is to develop an effective performance monitoring technique to assess the condition of rotating machinery through vibration signature analysis. An autoregressive (AR) model is utilized to characterize normal vibration signals, the modified covariance method calculates the deviation of the current condition from a normal condition, and an exponentially weighted moving average (EWMA) statistic measures the current machine condition by signalling either a normal or an out-of-control condition, The following paper discusses the statistical techniques employed in performance monitoring. Preliminary studies show that these techniques provide accurate performance monitoring of the operating condition of rotating machinery using vibration signals.