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AbstractA new fuzzy-logic-based methodology for on-line signal trend identification is introduced. The methodology may be used for detecting the onset of nuclear power plant (NPP) transients at the earliest possible time and could be of great benefit to diagnostic, maintenance, and performance-monitoring programs. Although signal trend identification is complicated by the presence of noise, fuzzy methods can help capture important features of on-line signals, integrate the information included in these features, and classify incoming NPP signals into increasing, decreasing, and steady-state trend categories. A computer program named PROTREN is developed and tested for the purpose of verifying this methodology using NPP and simulation data. The results indicate that the new fuzzy-logic-based methodology is capable of detecting transients accurately, it identifies trends reliably and does not misinterpret a steady-state signal as a transient one.

AbstractA novel first-principles-based diagnostic system called PRODIAG is proposed for on-line detection and identification of faulty components during incipient off-normal process conditions. The concepts of qualitative physics reasoning and function-oriented diagnostics are employed in the design of PRODIAG and result in two unique capabilities not found in other plant-level diagnostic systems. First, PRODIAG is fully portable as it requires only modification of the input files containing the appropriate process schematics information to be able to diagnose single-component failures in different processes/plants. Second, PRODIAG detects unanticipated faults. Hence, it does not require the prespecification and formulation of rules to cover every conceivable fault scenario, and unlike traditional approaches, it is not likely to misdiagnose unforeseen events. PRODIAG’s approach is to map process symptoms into component faults through a three-step mapping procedure with a knowledge base containing three distinct types of information: qualitative macroscopic balance equation rules, functional classification of process components, and the process piping and instrumentation diagram. The concepts introduced in the proposed diagnostic system are described, and an illustrative example shows how they are used in plant-level diagnostics.

AbstractThe unique capabilities of the first-principles-based PRODIAG diagnostic system to identify unanticipated process component faults and to be ported across different processes/plants through modification of only input data files are demonstrated in two validation tests. The Braidwood Nuclear Power Plant full-scope operator training simulator is used to generate transient data for two plant systems used in the validation tests. The first test consists of a blind test performed with 39 simulated transients of 20 distinct types in the Braidwood chemical and volume control system. Of the 39 transients, 37 are correctly identified with varying precision within the first 40 s into the transient while the remaining two transients are not identified. The second validation test consists of a double-blind test performed with 14 simulated transients in the Braidwood component coolant water system. In addition to having no prior knowledge of the identity of the transients, in the double-blind test we also had no prior information regarding the identity of the component faults that the simulator was capable of modeling. All 14 transient events are correctly identified with varying precision within the first 30 s into the transient. The test results provide enough evidence to successfully confirm the unique capabilities of the plant-level PRODIAG diagnostic system.