Method of Sequential Kernel Regression Modeling for Forecasting and Prognostics

A method for determining the future operational condition of an object, comprising:

• receiving reference data that indicates the normal operational state of the object, receiving input multi-dimensional pattern arrays, each input pattern array having a plurality of input vectors, each input vector representing a time point and having input values representing a plurality of parameters indicating the condition of the object obtained from one or more first sensors at any time, each input vector arranged and maintained in a predetermined and time ordered relationship with the others, and
• generating 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 similarity measure accounting for the predetermined and ordered time relationship, wherein the estimate values are in the form of an estimate matrix that 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, each estimate vector arranged and maintained in the predetermined and ordered time relationship with the others, the reference data being grouped in multi-dimensional training arrays including reference vectors, such that each reference vector in any training array is arranged and maintained in the predetermined and ordered time relationship with the others and such that the generation does not destroy any time information of the estimate matrix or the reference data;
• and
• determining a current outcome of the object based upon the inferred estimate values.