Abstract: Embodiments of the disclosure can include MapReduce systems and methods with integral mapper and reducer compute runtime environments. An example system with an integral reducer compute runtime environment can include mappers and reducers executable on a computer cluster. The mappers can be operable to receive raw input data and generate first input data based on the raw input data. The mappers can be operable to generate first result data based on the first input data. Based on the first result data, the mappers can be operable to generate (K, V) pairs. The reducers can be operable to receive the (K, V) pairs and generate second input data based on the (K, V) pairs. The reducers can be operable to transmit the second input data to integral compute runtime environment being run within the reducers and operable to generate second result data based on the second input data. Based on the second result data, the reducers can be operable to generate output data.

Abstract: A technique is disclosed for evaluating and monitoring performance of a heat exchanger system. Operating parameters of the system are monitored and fouling factors for heat transfer surfaces of the exchanger are determined. Trending of fouling may be performed over time based upon the fouling factors, and a model of fouling may be selected from known sets of models, or a model may be developed or refined. Fluid treatment, such as water treatment regimes may be taken into account in evaluation of fouling. An automated knowledge based analysis algorithm may diagnose possible caused of fouling based upon sensed and observed parameters and conditions. Corrective actions may be suggested and the system controlled to reduce, avoid or correct for detected fouling.

Abstract: A method for diagnosing faults in a particular device within a fleet of devices is provided. The method comprises receiving performance data related to one or more parameters associated with a fleet of devices and processing the performance data to detect one or more trend shifts in the one or more parameters. The method then comprises detrending the one or more parameters to derive noise-adjusted performance data related to a particular parameter associated with a particular device. The method further comprises generating a fleet-based diagnostic model based on trend patterns and data characteristics associated with the fleet of devices. The fleet-based diagnostic model comprises one or more fuzzy rules defining one or more expected trend shift data ranges for the one or more parameters associated with the fleet of devices.

Abstract: A method for diagnosing and classifying faults in a system is provided. The method comprises acquiring operational data for at least one of a system, one or more subsystems of the system or one or more components of the one or more subsystems. Then, the method comprises analyzing the operational data using one or more diagnostic models. Each diagnostic model uses the operational data to determine a probability of fault associated with at least one of the one or more components or the one or more subsystems. Finally, the method comprises deriving an overall probability of fault for at least one of the system, the one or more subsystems, or the one or more components using the one or more probabilities of fault determined by the one or more diagnostic models and one or more hierarchical relationships between the subsystems and components of the system.

Abstract: A technique is provided for selecting among a plurality of service models for addressing serviceable events and faults in a complex machine system. Indicators established for each of the models serve as the basis for comparison to input data acquired from the complex system or from manual or semi-automated data acquisition methods. Based upon the comparisons, and upon flexible selection criteria, one or more of the service models is selected to most efficiently address the most likely root cause of the serviceable event or fault.

Abstract: A technique is provided for designing and evaluating service models for components, functions, subsystems and field replaceable units in a complex machine system. At a component or item level, each model identifies various items, failure modes, and so forth which may be the root cause of anticipated serviceable events or faults. The design tools permit numerous interfaces to be used in the design of service models, and in the evaluation of the degree to which the models address detectability and isolation capabilities for the root causes of serviceable events and faults.

Abstract: A method for predicting a time to failure of a component in a system is presented. The method comprises obtaining a set of data measurements related to the component. The set of data measurements are representative of a plurality of parameters including a plurality of leading parameters. The method comprises generating a prediction model based upon the leading parameters considered in combination. The prediction model is then used to predict the time to failure of the component based on a set of real-time measurements, wherein the plurality of parameters are processed to predict the time to failure for the component. Finally, a confidence level for the predicted time to failure is determined based upon the plurality of parameters.

Abstract: Optimizing storage and retrieval of monitoring data. In one aspect of this disclosure, there is a system, method and computer readable medium that stores instructions for instructing a computer system, to optimize storage and retrieval of data. In this embodiment, a transfer manager component acquires the data from an archive and assigns predetermined storage values to specified parameters that form the data structure of the acquired data. A database stores the data acquired by the transfer manager component in accordance with the predetermined storage values. A middle tier component extracts the data in the database and interpolates the data in accordance with the predetermined storage values.

Abstract: A technique is provided for selecting among a plurality of service models for addressing serviceable events and faults in a complex machine system. Indicators established for each of the models serve as the basis for comparison to input data acquired from the complex system or from manual or semi-automated data acquisition methods. Based upon the comparisons, and upon flexible selection criteria, one or more of the service models is selected to most efficiently address the most likely root cause of the serviceable event or fault.

Abstract: A technique is provided for designing and evaluating service models for components, functions, subsystems and field replaceable units in a complex machine system. At a component or item level, each model identifies various items, failure modes, and so forth which may be the root cause of anticipated serviceable events or faults. The design tools permit numerous interfaces to be used in the design of service models, and in the evaluation of the degree to which the models address detectability and isolation capabilities for the root causes of serviceable events and faults.

Abstract: An apparatus for deciding credit, the apparatus comprising: a customer identification module adapted for generating customer identification data from salesperson input data; a customer financial data acquisition module adapted for transmitting the customer identification data to a financial data provider and receiving customer financial data from the financial data provider using a communications network; and a credit limit calculator adapted for calculating a credit limit from the customer financial data and from a reasoning model structure using Example Based Evidential Reasoning and for transmitting the credit limit to a credit provider using the communications network.

Abstract: Optimizing storage and retrieval of monitoring data. In one aspect of this disclosure, there is a system, method and computer readable medium that stores instructions for instructing a computer system, to optimize storage and retrieval of data. In this embodiment, a transfer manager component acquires the data from an archive and assigns predetermined storage values to specified parameters that form the data structure of the acquired data. A database stores the data acquired by the transfer manager component in accordance with the predetermined storage values. A middle tier component extracts the data in the database and interpolates the data in accordance with the predetermined storage values.