System and Method for Calculating Remaining Useful Time of Objects
First Claim
1. A computer implemented method of generating a used life percentage that is capable of identifying the remaining life in a mechanical component such as a bearing, comprising:
- obtaining sensor data from the mechanical component and organizing the obtained sensor data into a defined matrix;
calculating correlation coefficients for each matrix column and ranking the columns according to the correlation coefficients;
inputting the ranked columns into a series of corresponding artificial neural networks and training each artificial neural network with its corresponding column;
identifying the remaining useful life based on the trained artificial neural networks;
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Abstract
An aspect of the present invention is to provide a system and method for predicting the remaining useful time of mechanical components such as bearings. Another aspect of the present invention is to provide a system and method for predicting the remaining useful time of bearings based on available condition monitoring data. Another aspect of the present invention is to provide a system and method for automatically deciding which columns of input information are the most significant for predicting the remaining useful life of bearings. Another aspect of the present invention is to provide a system and method for performing an analysis of both test bearings and training bearings and determining which training bearings are most similar to a given test bearing. Another aspect of the present invention is to provide a system and method for training an artificial neural network.
58 Citations
20 Claims
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1. A computer implemented method of generating a used life percentage that is capable of identifying the remaining life in a mechanical component such as a bearing, comprising:
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obtaining sensor data from the mechanical component and organizing the obtained sensor data into a defined matrix; calculating correlation coefficients for each matrix column and ranking the columns according to the correlation coefficients; inputting the ranked columns into a series of corresponding artificial neural networks and training each artificial neural network with its corresponding column; identifying the remaining useful life based on the trained artificial neural networks; - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A non-transitory storage device storing computer instructions that when executed by one or more processors cause the one or more processors to:
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obtain sensor data from the mechanical component and organizing the obtained sensor data into a defined matrix; calculate correlation coefficients for each matrix column and ranking the columns according to the correlation coefficients; input the ranked columns into a series of corresponding artificial neural networks and training each artificial neural network with its corresponding column; identify the remaining useful life based on the trained artificial neural networks; - View Dependent Claims (9, 10, 11, 12, 13)
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14. A system comprising:
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a mechanical component; one or more processors configured to perform operations comprising; obtaining sensor data from the mechanical component and organizing the obtained sensor data into a defined matrix; calculating correlation coefficients for each matrix column and ranking the columns according to the correlation coefficients; inputting the ranked columns into a series of corresponding artificial neural networks and training each artificial neural network with its corresponding column; identifying the remaining useful life based on the trained artificial neural networks; - View Dependent Claims (15, 16, 17, 18, 19, 20)
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Specification