Intelligent modelling of process and tool health
First Claim
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1. A method of predicting the health of a plurality of tools based on temporally ordered input data representing parameters indicative of tool health, the method comprising the steps of:
- using a sliding time window to partition the input data into temporally displaced data sets;
creating intermediate neural networks for subsets of the data in the data sets;
using non-linear regression to determine, based on the data sets, a set of predictive values relating to tool health at a future time; and
determining a tool-health metric based on one or more of the predictive values.
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Abstract
The health of a tool is predicted based on temporally ordered input data representing parameters indicative of tool health. A sliding time window is used to partition input data into temporally displaced data sets. Non-linear regression models determine, based on the data sets, a set of predictive values relating to tool health at a future time. A tool-health metric is then determined based on one or more of the predictive values.
31 Citations
21 Claims
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1. A method of predicting the health of a plurality of tools based on temporally ordered input data representing parameters indicative of tool health, the method comprising the steps of:
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using a sliding time window to partition the input data into temporally displaced data sets; creating intermediate neural networks for subsets of the data in the data sets; using non-linear regression to determine, based on the data sets, a set of predictive values relating to tool health at a future time; and determining a tool-health metric based on one or more of the predictive values. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A system for predicting the health of a plurality of tools based on temporally ordered input data representing parameters indicative of tool health, the system comprising:
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a data module for receiving the input data; and an analysis module for (i) partitioning the input data into temporally displaced data sets, (ii) creating intermediate neural networks for subsets of the data in the data sets;
(iii) using non-linear regression to determine a set of predictive values relating to tool health at a future time, and (iv) determining a tool-health metric based on one or more of the predictive values. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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21. A system for predicting the health of multiple tools based on temporally ordered input data representing parameters indicative of tool health, the system comprising:
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means for receiving input data; means for partitioning the input data into temporally displaced data sets; means for creating intermediate neural networks for subsets of the data in the data sets; means for using a non-linear regression model to determine a set of predictive values relating to tool health at a future time; and means for determining a tool-health metric based on the set of predictive values.
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Specification