Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: An aspect of scalable rule logicalization for asset health management includes aggregating data, via a computer processing device, from data sources, extracting a set of features from the data, projecting the features to a lower dimensional space, generating a prediction based on the projecting, logicalizing a decision boundary for the prediction, and estimating a confidence level of the prediction based on the decision boundary.

Abstract: A method, system and computer program product for detecting anomalies in a metering system. In one embodiment, data representing usage of a defined commodity are collected from meters, and the data collected over a given time period are analyzed to identify any of the meters showing at least one defined type of anomalous usage pattern. For each of the meters showing an anomalous usage patterns, an anomaly score is determined for the usage pattern shown, and the anomaly scores are used to rank the meters. In one embodiment, the collected data are analyzed to identify any of the meters showing one or more of a group of types of anomalous patterns including meter-no-reading, meter-by-pass, and meter-silting patterns. Embodiments of the invention utilize time series techniques and data analysis on meter reading data. Further, embodiments of the invention require no additional installation of equipment or sensors.

Abstract: Historical high-spatial-resolution temperature data and dynamic temperature sensor measurement data may be used to predict temperature. A first formulation may be derived based on the historical high-spatial-resolution temperature data for determining a temperature at any point in 3-dimensional space. The dynamic temperature sensor measurement data may be calibrated based on the historical high-spatial-resolution temperature data at a corresponding historical time. Sensor temperature data at a plurality of sensor locations may be predicted for a future time based on the calibrated dynamic temperature sensor measurement data. A three-dimensional temperature spatial distribution associated with the future time may be generated based on the forecasted sensor temperature data and the first formulation.

Abstract: A system, method and computer program product for mining a rule including spatial information and non-spatial information by using a SAR (Spatial Association Rule) mining tool. The computing system is configured to construct an expanded spatial predicate transaction table for reference spatial objects and a generalized taxonomy for task-relevant spatial objects. The computing system is configured to run the SAR mining tool with the constructed expanded spatial predicate transaction and the generalized taxonomy. The computing system outputs, from the SAR mining tool, a set of generalized spatial association rules for the reference spatial objects. The generalized spatial association rule includes the spatial information and non-spatial information, associated with both the reference spatial objects and the task-relevant spatial objects.

Abstract: A method for determining a maintenance schedule of geographically dispersed physical assets includes receiving asset data including infrastructure relationships between the assets, modeling failure risk of the assets based on spatial, temporal and network relationships, and producing the maintenance schedule according to a combination of the risk model, asset data, maintenance, and external operation constraints. The maintenance schedule may be corrective and/or strategic.

Abstract: An approach for infrastructure asset management is provided. This approach comprises an end-to-end analytics driven maintenance approach that can take data about physical assets and additional external data, and apply advanced analytics to the data to generate business insight, foresight and planning information. Specifically, this approach uses a maintenance analysis tool, which is configured to: receive data about a set of physical assets of an infrastructure, and analyze the data about the set of physical assets to predict maintenance requirements for each of the set of physical assets. The maintenance analysis tool further comprises an output component configured to generate a maintenance plan based on the predicted maintenance requirements for each of the set of physical assets.

Abstract: A method for determining a maintenance schedule of geographically dispersed physical assets includes receiving asset data including infrastructure relationships between the assets, modeling failure risk of the assets based on spatial, temporal and network relationships, and producing the maintenance schedule according to a combination of the risk model, asset data, maintenance, and external operation constraints. The maintenance schedule may be corrective and/or strategic.

Abstract: Methods, systems and apparatus for determining a proposed cost for use of sensor resources and selecting a method of processing of sensed data are presented. Such a method includes the steps of: predicting a state of an environment, obtaining at least one criterion related to the use of the sensor resources comprising one or more data sensors, determining the proposed cost for use of the sensor resources, acquiring the sensed data from the one or more data sensors, determining a characteristic of the sensed data, and selecting the method of processing the sensed data according to the determined characteristic and the predicted state. The at least one criterion is based upon the predicted state. The determining of the proposed cost is based on the at least one criterion. One or more of steps are implemented on the processor device.