System health monitoring

Well designed large systems include many instrument taking data. These data are used in a variety of ways. They are used to control the system and its components, to monitor system and component health, and often for historical or financial purposes. This paper discusses a new method of using data from low level instrumentation to monitor system and component health. The method uses the covariance of instrument outputs to calculate a measure of system change. The method involves no complicated modeling since it is not a parameter estimation algorithm. The method is iterative and can be implemented on a computer in real time. Examples are presented for a metal lathe and a high efficiency particulate air (HEPA) filter. It is shown that the proposed method is quite sensitive to system changes such as wear out and failure. The method is useful for low level system diagnostics and fault detection