Abstract: Machine health can be monitored using multiple sensors. For example, a computing device can determine a target sensor to monitor from among multiple sensors associated with the machine. The computing device can determine magnitude values for a particular component of a time series associated with the target sensor. The computing device can generate a dataset including the magnitude values for the particular component of the time series and the sensor measurements from the multiple sensors. The computing device can generate a model using the dataset. The computing device can then receive additional sensor-measurements from the multiple sensors and use the model to determine a predicted magnitude-value for the particular component of the time series based on the additional sensor-measurements. The computing device can use the predicted magnitude-value to identify an anomaly with the machine.

Abstract: A computing device predicts a probability of a transformer failure. An analysis type indicator defined by a user is received. A worth value for each of a plurality of variables is computed. Highest worth variables from the plurality of variables are selected based on the computed worth values. A number of variables of the highest worth variables is limited to a predetermined number based on the received analysis type indicator. A first model and a second model are also selected based on the received analysis type indicator. Historical electrical system data is partitioned into a training dataset and a validation dataset that are used to train and validate, respectively, the first model and the second model. A probability of failure model is selected as the first model or the second model based on a comparison between a fit of each model.

Abstract: A computing device predicts a probability of a transformer failure. An analysis type indicator defined by a user is received. A worth value for each of a plurality of variables is computed. Highest worth variables from the plurality of variables are selected based on the computed worth values. A number of variables of the highest worth variables is limited to a predetermined number based on the received analysis type indicator. A first model and a second model are also selected based on the received analysis type indicator. Historical electrical system data is partitioned into a training dataset and a validation dataset that are used to train and validate, respectively, the first model and the second model. A probability of failure model is selected as the first model or the second model based on a comparison between a fit of each model.