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

A computer-based method for monitoring and diagnosing a machine condition, comprising the steps of:

• (1) operating a machine under normal machine conditions;
• (2) collecting a first set of data from said machine, wherein said first set of data is indicative of the operation of said machine under said normal machine conditions;
• (3) selecting an AR order for said normal machine condition, and generating an AR model for said first set of data, wherein said AR model has a first order parameters (.phi..sub.i1), second order parameters (.phi..sub.i2), up to p order parameters (.phi..sub.ip) for i=1, 2, . . . , n data sets;
• (4) calculating an average value for said first order AR parameter through said pth order AR parameter from said AR models in order to define a normal model that is representative of said normal machine condition;
• (5) collecting a second set of data from a machine under diagnosis, wherein said second set of data is representative of an abnormal machine condition;
• (6) fitting said second set of data representative of said abnormal machine condition to said normal model to generate a fitted model, wherein said fitted model is an indicator of how closely said normal model fits said second set of data;
• (7) calculating forward and backward prediction errors to determine a .rho..sub.normalized.sup.fb value for said second set of data;
• (8) calculating an exponentially weighted moving average (EWMA) statistic based on said .rho..sub.normalized.sup.fb value, wherein said EWMA statistic is an indicator of the overall machine condition;
• (9) comparing said EWMA statistic to an upper control limit to determine if said machine under diagnosis is in a state of control or is a state of out-of-control, wherein if said EWMA statistic exceeds said upper control limit this is a signal that an abnormal machine condition exists in said machine under diagnosis.