Data processing: measuring – calibrating – or testing – Measurement system – Performance or efficiency evaluation
Reexamination Certificate
1999-08-12
2001-03-13
Hoff, Marc S. (Department: 2857)
Data processing: measuring, calibrating, or testing
Measurement system
Performance or efficiency evaluation
C702S179000, C702S185000, C376S216000, C376S217000
Reexamination Certificate
active
06202038
ABSTRACT:
The present invention is related generally to a method and system for performing high sensitivity surveillance of various processes. More particularly the invention is related to a method and system for carrying out surveillance of any number of input signals and one or more sensors. In certain embodiments high sensitivity surveillance is performed utilizing a regression sequential probability ratio test involving two input signals which need not be redundant sensor signals, nor have similar noise distributions nor even involve signals from the same variable. In another form of the invention a bounded angle ratio test is utilized to carry out ultrasensitive surveillance.
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 suffer from either 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). 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 or an alarm condition. Most methods also do not account for the relationship between a measurement by one sensor relative to another sensor measurement.
Another conventional methodology is a sequential probability ratio test (SPRT) which was originally developed in the 1940s for applications involving the testing of manufactured devices to determine the level of defects. These applications, before the advent of computers, were for manufactured items that could be counted manually. As an example, a company manufacturing toasters might sell a shipment of toasters under the stipulation that if greater than 8% of the toasters were defective, the entire lot of toasters would be rejected and replaced for free; and if less than 8% of the toasters were defective, the entire lot would be accepted by the company receiving them. Before the SPRT test was devised, the purchasing company would have to test most or all items in a shipment of toasters being received. For the toaster example, testing would continue until at least 92% of the toasters were confirmed to be good, or until at least 8% of the toasters were identified to be defective.
In 1948 Abraham Wald devised a morelrigorous SPRT technique, which provided a formula by which the testing for defective manufactured items could be terminated earlier, and sometimes much earlier, while still attaining the terms of the procurement contract with any desired confidence level. In the foregoing example involving toasters, if the purchasing company were receiving 100 toasters and four of the first eight toasters tested were found to be defective, it is intuitively quite likely that the entire lot is going to be rejected and that testing could be terminated. Instead of going by intuition, however, Wald developed a simple, quantitative formula that would enable one to calculate, after each successive toaster is tested, the probability that the entire lot is going to be accepted or rejected. As soon as enough toasters are tested so that this probability reaches a pre-determined level, say 99.9% certainty, then a decision would be made and the testing could cease.
In the 1980s, other researchers began exploring the adaptation of Wald's SPRT test for an entirely new application, namely, surveillance of digitized computer signals. Now, instead of monitoring manufactured hardware units, the SPRT methodology was adapted for testing the validity of packets of information streaming from real-time physical processes. See, for example, U.S. Pat. Nos. 5,223,207; 5,410,492; 5,586,066 and 5,629,872.
These types of SPRT based surveillance systems have been finding many beneficial uses in a variety of application domains for signal validation and for sensor and equipment operability surveillance. As recited hereinbefore, conventional parameter-surveillance schemes are sensitive only to gross changes in the process mean, or to large steps or spikes that exceed some threshold limit check. These conventional methods suffer from either large false alarm rates (if thresholds are set too close) or large missed (or delayed) alarm rates (if the threshold are set too wide). The SPRT methodology therefore has provided a superior surveillance tool because it is sensitive not only to disturbances in the signal mean, but also to very subtle changes in the statistical quality (variance, skewness, bias) of the monitored signals.
A SPRT-based system provides a human operator with very early annunciation of the onset of process anomalies, thereby enabling him to terminate or avoid events which might challenge safety guidelines for equipment-availability goals and, in many cases, to schedule corrective actions (sensor replacement or recalibration; component adjustment, alignment, or rebalancing; etc.) to be performed during a scheduled plant outage. When the noise distributions on the signals are gaussian and white, and when the signals under surveillance are uncorrelated, it can be mathematically proven that the SPRT methodology provides the earliest possible annunciation of the onset of subtle anomalous patterns in noisy process variables. For sudden, gross failures of sensors or system components the SPRT methodology would annunciate the disturbance at the same time as a conventional threshold limit check. However, for slow degradation that evolves over a long time period (gradual decalibration bias in a sensor, wearout or buildup of a radial rub in rotating machinery, build-in of a radiation source in the presence of a noisy background signal, etc), the SPRT methodology can alert the operator of the incipience or onset of the disturbance long before it would be apparent to visual inspection of strip chart or CRT signal traces, and well before conventional threshold limit checks would be tripped.
Another feature of the SPRT technique that distinguishes it from conventional Methods is that it has built-in quantitative false-alarm and missed-alarm. probabilities. This is important in the context of safety-critical and mission-critical applications, because it makes it possible to apply formal reliability analysis methods to an overall expert system comprising many SPRT modules that are simultaneously monitoring a variety of plant variables.
A variety of SPRT-based online surveillance and diagnosis systems have been developed for applications in utilities, manufacturing, robotics, transportation, aerospace and health monitoring. Most applications to date, however, have been limited to systems involving two or more redundant sensors, or two or more pieces of equipment deployed in parallel with identical sensors for each device. This limitation in applicability of SPRT surveillance tools arises because the conventional SPRT equation requires exactly two input signals, and both of these signals have to possess identical noise properties.
It is therefore an object of the invention to provide an improved method and system for surveillance of a wide variety of industrial, financial, physical and biological systems.
It is another object of the invention to provide a novel method and system utilizing an improved SPRT system allowing surveillance of any number of input signals with or without sensor redundancy.
It is a further object of the invention to provide an improved method and system utilizing another improved SPRT type of system employing two input signals which need not come from redundant sensors, nor have similar noise distributions nor originate from the same physical variable but should have some degree of cross correlation.
It is still another object of the system to provide a novel method and system selectively employing an improved SPRT methodology which monitors a system providing only a single signal and/or an improved SPRT methodology employing two or more input signals having cross correlation depending on the current status of rela
Gross Kenneth C.
Jarman Kristin K.
Wegerich Stephan W.
ARCH Development Corporation
Bui Bryan
Foley & Lardner
Hoff Marc S.
Rechtin Michael D.
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