Patent Number: 054104920
Section: summary

The present invention is concerned generally with a system and method for reliably processing a data base of information having nonwhite noise characteristics. More particularly, the invention is concerned with a system and method for removal of nonwhite noise elements, such as serially correlated noise, from an incoming steam of data or from an existing data base. This method allows use of reliable data, or providing a validated data set, from an industrial process and/or sensors which monitor the process. Such a system further allows accumulation of characteristic faulty data arising from nonwhite noise which can be useful in understanding the varieties and characteristics of noise which cause false alarm conditions. Conventional parameter-surveillance schemes are sensitive only to gross changes in the mean value of a process, or to large steps or spikes that exceed some threshold limit check. These conventional methods can pass through data containing bad information characterized by large numbers of false alarms (if thresholds are set too close to normal operating levels) or a large number of missed (or delayed) alarms (if the thresholds are set too expansively). Such a system gives rise to a faulted data base of information for researchers or for engineers analyzing the status of an industrial process. Moreover, most conventional methods cannot perceive the onset of a process disturbance or sensor deviation which gives rise to a signal below the threshold level for an alarm condition. In another conventional monitoring method, the Sequential Probability Ratio Test ("SPRT") has found wide application as a signal validation tool in the nuclear reactor industry. Two features of the SPRT technique make it attractive for parameter surveillance and fault detection: (1) early annunciation of the onset of a disturbance in noisy process variables, and (2) the SPRT technique has user-specifiable false-alarm and missed-alarm probabilities. One important drawback of the SPRT technique that has limited its adaptation to a broader range of nuclear applications is the fact that its mathematical formalism is founded upon an assumption that the signals it is monitoring are purely Gaussian, independent (white noise) random variables. It is therefore an object of the invention to provide an improved method and system for processing data base information having nonwhite noise present. It is another object of the invention to provide a novel method and system for statistically filtering industrial process signals having virtually any form of noise signal. It is a further object of the invention to provide an improved method and system for operating on an industrial process signal data base to remove unwanted serially correlated noise signals. It is still an additional object of the invention to provide a novel method and system utilizing a data base formed from a pair of signals to generate a difference function to be analyzed for anomalous data. It is still a further object of the invention to provide an improved method and system for collecting a reliable data base from at least one sensor for providing a real signal characteristic of a process and a predicted sensor signal allowing formation of a difference signal between the predicted and real signal for subsequent analysis and accumulation of data free from nonwhite noise contamination. It is also an object of the invention to provide a novel method and system wherein a difference function is formed from a data base of two sensor signals, and/or pairs of signals and nonwhite noise is removed enabling collection of reliable data. It is yet an additional object of the invention to provide an improved method and system utilizing variable pairs of sensors for accumulating reliable data both for sensor degradation and industrial processes.