Plant malfunction diagnostic method
First Claim
1. A plant malfunction diagnostic method comprising:
- a step of determining by simulation a change in a plant state variable at the time of a malfunction and forming a pattern among plant state variables obtained by autoregressive analysis of the change in plant state variable;
a step of inserting the formed pattern among the plant state variables in a neural network comprising an input layer, a middle layer and an output layer, and performing learning within a preset precision to decide a model of the neural network as well as connection weight between processing elements (neurons) in the input layer and middle layer and between the middle layer and the output layer; and
a step of identifying the cause of the malfunction by inputting a pattern, which indicates the pattern among plant state variables formed from the plant state variable, after detection of an actual plant malfunction.
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Abstract
A 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.
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Citations
2 Claims
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1. A plant malfunction diagnostic method comprising:
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a step of determining by simulation a change in a plant state variable at the time of a malfunction and forming a pattern among plant state variables obtained by autoregressive analysis of the change in plant state variable; a step of inserting the formed pattern among the plant state variables in a neural network comprising an input layer, a middle layer and an output layer, and performing learning within a preset precision to decide a model of the neural network as well as connection weight between processing elements (neurons) in the input layer and middle layer and between the middle layer and the output layer; and a step of identifying the cause of the malfunction by inputting a pattern, which indicates the pattern among plant state variables formed from the plant state variable, after detection of an actual plant malfunction. - View Dependent Claims (2)
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Specification