Plant malfunction diagnostic method
Patent 5023045 Issued on June 11, 1991. Estimated Expiration Date: 
February 7, 2010. Estimated Expiration Date is calculated based on simple USPTO term provisions. It does not account for terminal disclaimers, term adjustments, failure to pay maintenance fees, or other factors which might affect the term of a patent.
February 7, 2010. Estimated Expiration Date is calculated based on simple USPTO term provisions. It does not account for terminal disclaimers, term adjustments, failure to pay maintenance fees, or other factors which might affect the term of a patent.Patent ReferencesHybrid analog-digital associative neural network Power plant interactive display Process and apparatus for the automatic detection and extraction of features in images and displays Graded learning device and method State analog neural network and method of implementing same Patent #: 4947482 InventorsAssigneeApplicationNo. 476922 filed on 02/07/1990US Classes:376/215, By electronic signal processing circuitry (e.g., plural redundant circuits)376/216, Plural sensed different conditions or measured variables correlated376/217, Control programs700/48, Neural network700/51, Statistical process control (SPC)700/79, Having protection or reliability feature703/6, SIMULATING NONELECTRICAL DEVICE OR SYSTEM706/20, Classification or recognition706/914Process plantExaminersPrimary: Walsh, Donald P.Attorney, Agent or FirmInternational ClassG21C 007/36Foreign Application Priority Data1989-02-07 JPAbstractA plant malfunction diagnostic method is characterized by determining by simulation a change in a plant state variable, forming a pattern among plant state variables obtained by autoregressive analysis of the change in plant state variable, inserting the formed pattern among the plant state variables in a neural network, performing learning until a preset precision is obtained, and identifying the cause of the malfunction by inserting, in the neural network, a pattern which indicates the pattern among plant state variables formed by data gathered from the plant. This makes possible early identification of the cause of a malfunction. Plant rate of operation and safety are improved by allowing the operator to perform the appropriate recovery operation with a sufficient time margin.Other References
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