Method and algorithm for predicting leak rates
원문보기
IPC분류정보
국가/구분
United States(US) Patent
등록
국제특허분류(IPC7판)
G01F-001/00
출원번호
US-0668116
(2000-09-22)
발명자
/ 주소
Arnold, William Allen
출원인 / 주소
The Goodyear Tire & Rubber Company
대리인 / 주소
Wheeler, David EO'Planick, Richard B
인용정보
피인용 횟수 :
15인용 특허 :
2
초록▼
A method and algorithm for predicting the fluid loss rate of a system, uses data for the leak rate from the system for a substitute fluid that leaks at a different rate than the system fluid. In the illustrated embodiment, the system is a pneumatic tire and the substitute fluid is helium gas. The he
A method and algorithm for predicting the fluid loss rate of a system, uses data for the leak rate from the system for a substitute fluid that leaks at a different rate than the system fluid. In the illustrated embodiment, the system is a pneumatic tire and the substitute fluid is helium gas. The helium leak rate data is used in a Fortran program to predict the air loss rate from the tire by determining the leak rate factor f` for helium, as compared to air, and using `f` to calculate the air loss rate.
대표청구항▼
A method and algorithm for predicting the fluid loss rate of a system, uses data for the leak rate from the system for a substitute fluid that leaks at a different rate than the system fluid. In the illustrated embodiment, the system is a pneumatic tire and the substitute fluid is helium gas. The he
A method and algorithm for predicting the fluid loss rate of a system, uses data for the leak rate from the system for a substitute fluid that leaks at a different rate than the system fluid. In the illustrated embodiment, the system is a pneumatic tire and the substitute fluid is helium gas. The helium leak rate data is used in a Fortran program to predict the air loss rate from the tire by determining the leak rate factor f` for helium, as compared to air, and using `f` to calculate the air loss rate. transformations of the plurality of measurements. 17. The system according to claim 16, wherein the break sensitivity data comprise principal components in determining web breakage from the plurality of measurements. 18. The system according to claim 17, wherein the break sensitivity data further comprise a principal component first derivative value, a principal component second derivative value and a principal component difference from steady-state value. 19. The system according to claim 18, further comprising a fault isolator responsive to the break predictor for determining the paper machine variables affecting the predicted web break sensitivity. 20. The system according to claim 19, wherein the fault isolator comprises a classification and regression tree model having a set of rules linking paper machine variables to the predicted web break sensitivity. 21. The system according to claim 20, wherein the fault isolator identifies the paper machine variables that are root causes for the predicted web break sensitivity. 22. The system according to claim 13, wherein the plurality of measurements contained in the paper mill database are generated from various processes occurring within the paper mill. 23. The system according to claim 13, wherein the paper mill database comprises a raw materials database, a preprocess database, a paper machine database, an operation shift database and a maintenance schedule database. 24. A method for predicting a web break in a paper machine located about a paper mill, comprising: obtaining a plurality of measurements from the paper mill, each of the plurality of measurements relating to a predetermined paper machine variable; processing each of the plurality of measurements into break sensitivity data; and predicting a web break sensitivity within the paper machine from the plurality of processed measurements. 25. The method according to claim 24, wherein processing each of the plurality of measurements comprises time-based transformations of the plurality of measurements. 26. The method according to claim 24, wherein processing each of the plurality of measurements comprises generating principal components in determining web breakage from the plurality of measurements. 27. The method according to claim 25, wherein processing each of the plurality of measurements comprises generating a principal component first derivative value, a principal component second derivative value and a principal component difference from steady-state value. 28. The method according to claim 24, wherein predicting the web break sensitivity comprises processing the break sensitivity data using an inductive reasoning tool. 29. The method according to claim 24, wherein predicting the web break sensitivity comprises processing the break sensitivity data using a classification and regression tree model. 30. The method according to claim 29, further comprising training the classification and regression tree model with historical web break data to learn how to predict web break sensitivity. 31. The method according to claim 30, further comprising testing the trained classification and regression tree model with the historical break data to test how well the model predicts web break sensitivity. 32. The method according to claim 30, wherein the training comprises preprocessing the historical web break data. 33. The method according to claim 32, wherein the preprocessing comprises: reducing the quantity of the historical web break data; reducing the number of variables contained in the historical web break data; transforming the values of the historical web break data; extracting features that affect web break sensitivity from the historical web break data; and generating the classification and regression tree model to predict a web break sensitivity from the extracted features. 34. The method according to claim 33, wherein reducing the quantity of historical web break data includes selecting data associated
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