Data processing: artificial intelligence – Fuzzy logic hardware – Fuzzy inference processing
Reexamination Certificate
1998-02-20
2001-02-20
Hafiz, Tariq R. (Department: 2762)
Data processing: artificial intelligence
Fuzzy logic hardware
Fuzzy inference processing
C706S002000, C706S902000, C706S907000
Reexamination Certificate
active
06192352
ABSTRACT:
INTRODUCTION
The present invention in its most preferred embodiments relates to the detection of leaks in boiler tubes; more particularly it relates to the early detection of leaks in tubes of industrial type boilers to thereby allow the operators of such boilers, including utilities, to schedule a shut down for repair rather than suffer a forced outage when such leaks later become catastrophic; and still more particularly to such early detection of leaks to thereby significantly increase the chances of limiting damage to adjacent tubes in such boilers. The present new and improved system utilizes an approach different from those heretofore taken and taught in the prior art. As utilized herein, there is effected the monitoring of a set of tube leak sensitive variables, i.e. variables which exhibit significant changes whenever a leak occurs in a boiler tube. It will be appreciated, of course, that when a tube starts to leak, the output values of these sensitive variables start to change in response to that particular leak. In addition, in the approach utilized in the development of the instant invention including the methods, techniques, and system comprising same, the approach has been to correlate more than one sensitive variable to such leak. Accordingly, by relying on different sources of information about a leak and by correlating a number of leak sensitive variables, it has been found that the likelihood of early detection of such leaks is greatly enhanced. As will be appreciated from a more detailed description infra, in the technique comprising the instant invention, one of the first tasks was to find a functional map between the changes in a plurality of such sensitive variables and the occurrence of a tube leak, i.e. a multi-variable function whose parameters are the sensitive variables and whose output is the tube leak level. Of course, classical approximation methods might be used to find this map as, for instance, by using the Weierstrauss theorem wherein a continuous function can be approximated to an arbitrary degree of accuracy through the utilization of classical techniques employing, for instance, a polynomial. However, for the very complex situation related to tube leaks in boilers, there are several reasons why such classical approximation methods are not suitable including, for instance, that such technique requires one to assume a priori form of the map, i.e. the degree of the polynomial in order to approximate same. A further reason why such approximation methods are not suitable is that extensive computer simulations have shown that for high order polynomials, which would be the case in the present invention arena, the approximation of complex maps results in numerical instabilities which are encountered during the computation of the coefficients of the polynomial. Still another reason for not using such a classical approach is that it is fraught with the difficulties of being not easily implemented in computer hardware. On the other hand, the instant invention, in its simpler form, utilizes artificial neural networks (ANN) to identify the complex map. Since ANNs are known to be model free approximaters one does not need to assume a priori form of the map and further computer simulations are easily and effectively utilized in both computer hardware and software. Accordingly, the instant invention relates to the utilization of a plurality of ANNs to detect the presence of a tube leak as well as determine its location in the boiler. Further, the instant technique utilizes a decentralized architecture or structure for such networks. More specifically, a first ANN is utilized to make a relatively simple decision concerning the presence of a leak. This first ANN is trained on what is herein referred to as universal leak sensitive variables (ULSV), which are sensitive variables that respond to a leak in a boiler regardless of its location therein. Once such first ANN determines that there is indeed a leak in the boiler, the next process step in the practice of the instant invention is to utilize what is herein referred to as local leak sensitive variables (LLSV), rather than said ULSVs, which LLSVs are most sensitive for a given location both along a tube and across the cross section of the boiler. It has been found that there are a plurality of common sensitive variables for designated locations in a boiler and the present invention utilizes the most sensitive thereof for a given location wherein the presence of the leak is manifested by a change in the same subset of such LLSVs. Accordingly, a plurality of dedicated ANNS arc utilized in this second step to perform localized leak detection for the location of such common sensitive variables. Although ANNs are known to be universal approximaters, they utilize data driven approaches which translates into performance acceptable for boilers having similar characteristics. In other words, an ANN based system although quite improved over heretofore prior art methods for early detection of tubes requires that after it is trained it be utilized only on similar type boilers. Since a principle object of the instant invention is, at least in the more sophisticated embodiments thereof, to provide a high degree of portability of the instant system wherein is required a minimum of tuning of same when it is used and moved from one boiler to another, the more advanced embodiments of this invention utilize the integration of fuzzy logic with such ANNs whereby is utilized available input-output information about tube leaks to build a fuzzy map whose input is available, numerical, and linguistic tube leak information and whose output is characterization of the sensitive variables. In this more sophisticated approach, there is utilized inference engines to invert the resulting map and to render more accurate decisions about tube leaks in boilers. The decision making procedure utilized in the operation in these more sophisticated integrated systems has been found to be greatly implemented by the use of a set of “If Then” rules.
BACKGROUND OF THE INVENTION
The present invention relates generally to new, improved, and reliable systems, methods, and techniques for the detection of leaks in the tubes of industrial boilers, including those of the types used by utilities to produce steam for electric power production.
Boiler Tube Leak Detection
Because of heat, pressure, and wear over time, boiler tubes eventually begin to leak, i.e., the beginning of a “leak event.” When a boiler tube(s) starts to leak, steam which flashes over from the water escaping through the leak therein is lost to the boiler environment. In general, the amount of leaked water/steam may be small at the inception of a tube leak event. However, unless the tube is repaired, the leak will continue to grow, i.e., the tube leak rate increases with time until the tube eventually ruptures. Once such rupture occurs the utility operating such boiler is forced to shut it down immediately.
Boiler tube failures are a major cause of forced shut downs in fossil power plants. For example, approximately 41,000 tube failures occur every year in the United States alone. The cost of these failures proves to be quite expensive for utilities, exceeding $5 billion a year. [Lind, M. H., “Boiler Tube Leak Detection System,” Proceedings of the Third EPRI Incipient-Failure Detection Conference, EPRI CS-5395, March 1987]
In order to reduce the occurrences of such forced outages, early boiler tube leak detection is highly desirable. Early boiler tube leak detection would allow utilities to schedule a repair rather than to suffer a later forced outage. In addition, the earlier the detection, the better the chances are of limiting damage to adjacent tubes.
Artificial Neural Networks
Artificial neural networks (ANNs) are information-processing models inspired by the architecture of the human brain. ANNs are capable of learning and generalization and are model-free adaptive estimators of maps (relations between the input and the output of the ANN, or, as later referenced, an inference
Alouani Ali Tahar
Chang Peter Shih-Yung
Davis George
Hafiz Tariq R.
Olexy Peter D.
Tennessee Valley Authority
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