International Business Machines Corporation

Dhaivat P Parikh

Arun Hampapur

Hongfei Li

Qing He

Zhiguo Li

Debarun Bhattacharjya

Buyue Qian

Dongping Fang

Geo-defect repair modeling with location uncertainty is provided. A method includes logically dividing a railroad network into segments each of a specified length. The method also includes identifying, via a computer processor, geo-defects and approximated locations of the geo-defects occurring at each inspection run for each of the segments. The method also includes calculating, via the computer processor, a rate of increase in amplitude of each of the geo-defects for each of the segments between inspection runs, determining a correlation of the geo-defects between the inspection runs as a function of the approximated locations, and predicting a deterioration rate for each of the geo-defects based on the calculating.

Asset failure prediction with location uncertainty

Geo-defect repair modeling is provided. A method includes logically dividing a railroad network according to spatial and temporal dimensions with respect to historical data collected. The spatial dimensions include line segments of a specified length and the temporal dimensions include inspection run data for inspections performed for each of the line segments over a period of time. The method also includes creating a track deterioration model from the historical data, identifying geo-defects occurring at each inspection run from the track deterioration model, calculating a track deterioration condition from the track deterioration model by analyzing quantified changes in the geo-defects measured at each inspection run, and calculating a derailment risk based on track conditions determined from the inspection run data and the track deterioration condition. The method further includes determining a repair decision for each of the geo-defects based on the derailment risk and costs associated with previous comparable repairs.

Railway track geometry defect modeling for predicting deterioration, derailment risk, and optimal repair

Predicting operational changes in a multi-detector environment includes generating, via a computer processing device, a factor matrix for each univariate time series data in a set of sparse time series data collected from data sources, identifying a subset of the time series data as a feature selection based on application of a loss function, and generating a predictive model from the subset of the time series data.

Large-scale multi-detector predictive modeling

An aspect of an online learning system includes collecting data, via a computer processing device, from a plurality of data sources including multiple disparate detectors, the data including at least one of historical alarm data, failures, maintenance records, and environment observations. The data is stored in tables each corresponding to a subject of measurement. The online learning system also includes identifying common fields shared across the tables, merging at least a portion of the data across the tables having the common fields, and creating an integrated data model based on results of the merging.

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Plano, TX, United States of America

Dhaivat P Parikh