Patent ID: 9652723
Date: 2017-05-16
CPC Classifications: G05B,G06F,G06N

Claim:
1. A non-transitory computer-readable medium having stored thereon computer-readable instructions that when executed by a computing device cause the computing device to: receive historical electrical system data that includes a plurality of observations with a plurality of data points defined for each observation, wherein each data point of the plurality of data points is associated with a variable to define a plurality of variables; partition the received historical electrical system data into a training dataset and a validation dataset, wherein the validation dataset is different from the training dataset; receive an analysis type indicator defined by a user; compute a worth value for each of the plurality of variables; select highest worth variables from the plurality of variables based on the computed worth values, wherein a number of variables of the highest worth variables is greater than 200 based on the received analysis type indicator; select a first model based on the received analysis type indicator; train the selected first model using values from the training dataset of the selected highest worth variables to predict a probability of failure of a plurality of electrical transformers; validate the trained first model using the validation dataset to statistically assess a fit by the trained first model to the historical electrical system data; select a second model based on the received analysis type indicator, wherein the selected first model is a decision tree model and the selected second model is a neural network model; train the selected second model using values from the training dataset of the selected highest worth variables to predict the probability of failure of the plurality of electrical transformers; validate the trained second model using the validation dataset to statistically assess the fit by the trained second model to the historical electrical system data; compare the fit by the trained first model to the fit by the trained second model; select a probability of failure model as the validated first model or the validated second model based on the comparison; receive electrical system data for a transformer; execute the selected probability of failure model with the received electrical system data to compute a probability of failure of the transformer; and update a failure probability for the transformer based on the computed probability of failure.