General Electric Company

Deniz Senturk

Christina Ann LaComb

John Erik Hershey

Brian Joseph McManus

Brock Estel Osborn

Bethany Hoogs

Snehil Gambhir

Peter Arnold Kalish

Roger W Hoerl

Edward James Lewandowski

Howard Daniel Koontz

Murat Doganaksoy

Radu Eugen Neagu

A technique for detecting anomalous values in a small set of financial metrics makes use of context data that is determined based upon the characteristics of the target company being evaluated. Context data is selected to represent the historical values of the financial metric for the target company or the simultaneous performance of peer companies. Using the context data, an anomaly score for the financial metric is calculated representing the degree to which the value of the financial metric is an outlier among the context data. This can be done using an exceptional statistical technique. The anomaly score can be used to evaluate the risks associated with business transactions related to the target company.

Methods and systems for anomaly detection in small datasets

Electrical data processing techniques are described for performing business analysis based on datasets that are incomplete (e.g., contain censored data) and/or based on datasets that are derived from a stage-based business operation. A first technique offsets the effects of error caused by the incomplete dataset by performing a trending operation followed by a de-trending operation. A second technique provides a model containing multiple sub-models, where the output of one sub-model serves as the input to another sub-model in recursive fashion. A third technique determines when a specified event is likely to occur with respect to a given asset by first discriminating whether the event is very unlikely to occur; if the asset does not meet this initial test, it is further processed by a second sub-model, which determines the probability that the specified event will occur for each of a specified series of time intervals.

Techniques for performing business analysis based on incomplete and/or stage-based data

An apparatus for estimating DC motor brush wear comprising: a wear element calculator adapted to calculate a plurality of wear elements from at least one environmental variable; a wear element multiplier adapted to multiply the wear elements by respective ones of a plurality of wear coefficients to yield a plurality of weighted wear elements; and a summer adapted to sum the weighted wear elements to yield a brush wear estimate.

Method and apparatus for estimating DC motor brush wear

Growing community of inventors

Niskayuna, NY, United States of America

Deniz Senturk