… and other ponderings in 11th-dimensional space

Kadambe, Barajas et al., US Patent 7,899,761 Granted

Apr 4th, 2011 | By | Category: Patents

United States Patent 7,899,761
Kadambe, Barajas, et al. March 1, 2011

System and method for signal prediction.


Abstract
Disclosed herein are a system and method for trend prediction of signals in a time series using a Markov model. The method includes receiving a plurality of data series and input parameters, where the input parameters include a time step parameter, preprocessing the plurality of data series according to the input parameters, to form binned and classified data series, and processing the binned and classified data series. The processing includes initializing a Markov model for trend prediction, and training the Markov model for trend prediction of the binned and classified data series to form a trained Markov model. The method further includes deploying the trained Markov model for trend prediction, including outputting trend predictions. The method develops an architecture for the Markov model from the data series and the input parameters, and disposes the Markov model, having the architecture, for trend prediction.


Inventors: Kadambe; Shubha (Thousand Oaks, CA)Barajas; Leandro G. (Rochester Hills, MI), Cho; Youngkwan (Los Angeles, CA), Bandyopadhyay; Pulak (Rochester Hills, MI)
Assignee: GM Global Technology Operations LLC (Detroit, MI)
Appl. No.: 11/113,962
Filed: April 25, 2005

A system for identifying trends of fault occurrences in a manufacturing process, the system including computer-readable medium tangibly embodying computer-executable instructions for: receiving a plurality of data series and input parameters, the input parameters comprising a time step parameter, said data series including discrete data elements that identify the fault occurrences in the manufacturing process; preprocessing the plurality of data series according to the input parameters, to form binned and classified data series, where the discrete data elements in each of the data series are classified into a particular bin depending on when they occurred per the time parameter, wherein preprocessing the plurality of data series includes clustering the classified data series to iteratively arrange the data elements into clusters to provide a predetermined criteria, and wherein preprocessing the plurality of data series includes classifying the data elements that identify the fault occurrences according to frequency of occurrence, mean time to repair and/or duration of downtime and selecting the most frequently occurring fault occurrences and/or the fault occurrences resulting in the longest downtime duration; processing the binned and classified data series, the processing comprising: initializing a model for trend prediction including determining the number of known states in the model based on the data series and associating a state of the model for each class of data determined by the binned and classified data series; training the model for trend prediction of the binned and classified data series to form a trained model, said model being trained to predict trends of the data series by determining the probability of states of the data as classified and binned and the probability of transition of the data from state to state where the state probabilities are calculated for the data series by evaluating a probability in a training window, wherein training the model includes training the model to predict the frequency of occurrence, the mean time to repair and/or the downtime duration of the fault occurrences, using the model to predict frequency and/or duration of the fault occurrences during a testing period that immediately succeeds a training period of the model to identify trend predictions of the fault occurrences, and evaluating the accuracy of the trend predictions by comparing the trend predictions during the testing period with actual data obtained during the testing period; and deploying the trained model for trend prediction in the manufacturing process, the deploying comprising: outputting trend predictions that identify predictions of fault occurrences that may occur during the manufacturing process; and updating training of the model when new data is obtained during the manufacturing process.

 

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  1. Congratulations! Another patent! You´r a good example for Felipe And Cami! 🙂

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