Method of sequential kernel regression modeling for forecasting and prognostics
First Claim
1. A method for determining the future operational condition of an object, comprising:
- obtaining input matrices of the input matrices having a plurality of input vectors, each of the plurality of input vectors representing a time point and having input values representing a plurality of parameters indicating the current condition of the object obtained from one or more first sensors at the time point, each input vector arranged and maintained in a predetermined and ordered time relationship with the others; and
obtaining reference matrices that indicate the normal operational state of the object, one or more of the reference matrices being generated from reference data, each of the plurality of reference matrices having a same number of input vectors as input matrix, wherein each input vector represents a time point and has input values representing the same parameters as those in input matrix, each input vector arranged and maintained in the predetermined and ordered time relationship with the others as those in the input matrix;
generating, by at least one processor, estimate values based on a calculation that uses one of the input matrices and one or more of the reference matrices to determine a matrix similarity measure between the input values and reference data, the matrix similarity measure accounting for the predetermined and ordered time relationship, wherein the estimate values are in the form of an estimate matrix that is calculated using the input matrix, the reference matrix, and a time weight value matrix with a plurality of contribution elements, each contribution element representing a similarity contribution at a discrete moment in time, the estimate matrix includes at least one estimate vector of inferred estimate values for at least one future point in time or a plurality of second sensors being different from the one or more first sensors, wherein estimate matrices include at least one additional value that represents parameters which are not represented by the input matrix and which indicate the condition of the object, each estimate vector arranged and maintained in the predetermined and ordered time relationship with the others; and
using the inferred estimate values to determine a future condition of the object.
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Abstract
A method for determining the future operational condition of an object includes obtaining reference data that indicates the normal operational state of the object, and obtaining input pattern arrays. Each input pattern array has a plurality of input vectors, while each input vector represents a time point and has input values representing a plurality of parameters indicating the current condition of the object. At least one processor generates estimate values based on a calculation that uses an input pattern array and the reference data to determine a similarity measure between the input values and reference data. The estimate values, in the form of an estimate matrix, include at least one estimate vector of inferred estimate values, and represents at least one time point that is not represented by the input vectors. The inferred estimate values are used to determine a future condition of the object.
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Citations
17 Claims
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1. A method for determining the future operational condition of an object, comprising:
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obtaining input matrices of the input matrices having a plurality of input vectors, each of the plurality of input vectors representing a time point and having input values representing a plurality of parameters indicating the current condition of the object obtained from one or more first sensors at the time point, each input vector arranged and maintained in a predetermined and ordered time relationship with the others; and obtaining reference matrices that indicate the normal operational state of the object, one or more of the reference matrices being generated from reference data, each of the plurality of reference matrices having a same number of input vectors as input matrix, wherein each input vector represents a time point and has input values representing the same parameters as those in input matrix, each input vector arranged and maintained in the predetermined and ordered time relationship with the others as those in the input matrix; generating, by at least one processor, estimate values based on a calculation that uses one of the input matrices and one or more of the reference matrices to determine a matrix similarity measure between the input values and reference data, the matrix similarity measure accounting for the predetermined and ordered time relationship, wherein the estimate values are in the form of an estimate matrix that is calculated using the input matrix, the reference matrix, and a time weight value matrix with a plurality of contribution elements, each contribution element representing a similarity contribution at a discrete moment in time, the estimate matrix includes at least one estimate vector of inferred estimate values for at least one future point in time or a plurality of second sensors being different from the one or more first sensors, wherein estimate matrices include at least one additional value that represents parameters which are not represented by the input matrix and which indicate the condition of the object, each estimate vector arranged and maintained in the predetermined and ordered time relationship with the others; and using the inferred estimate values to determine a future condition of the object. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
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Specification