Abstract: Monitoring dynamic units that operate in complex, dynamic environments, is provided in order to classify and track unit behavior over time. When domain knowledge is available, feature-based models may be used to capture the essential state information of the units. When domain knowledge is not available, raw data is relied upon to perform this task. By analyzing logs of event messages (without having access to their data dictionary), embodiments allow the identification of anomalies (novelties). Specifically, a Normalized Compression Distance (such as one based on Kolmogorov Complexity) may be applied to logs of event messages. By analyzing the similarity and differences of the event message logs, units are identified that did not experience any abnormality (and locate regions of normal operations) and units that departed from such regions. Of particular interest is the detection and identification of units' epidemics, which is defined as sustained/increasing numbers of anomalies over time.

Abstract: A method and system for creating healthy operating envelope from only data samples obtained during normal operation/behavior of dynamic systems is provided. This method determines healthy operating envelope by clustering a stream of discrete event code sequences from the underlying system under normal operation condition only. The method is unsupervised, that is, requiring no prior knowledge of event code patterns corresponding to different operation conditions. Such created envelope can be used for fault detection and health monitoring of dynamic systems.

Abstract: A method for analyzing vibration including: acquiring a vibration signal; isolating a vibration signal event in the acquired signal; determining a frequency of a damped sinusoid of the vibration signal event, wherein the damped sinusoid characterizes the vibration signal event, and using the characteristic damped sinusoid to identify an occurrence of the vibration signal event in another vibration signal.

Abstract: A method for analyzing vibration including: acquiring a vibration signal; isolating a vibration signal event in the acquired signal; determining a frequency of a damped sinusoid of the vibration signal event, wherein the damped sinusoid characterizes the vibration signal event, and using the characteristic damped sinusoid to identify an occurrence of the vibration signal event in another vibration signal.

Abstract: A method and system for performing model-based multi-objective asset optimization and decision-making is provided. The method includes building at least two predictive models for an asset. The building includes categorizing operational historical data via at least one of: controllable variables, uncontrollable variables, output objectives, and constraints. The building also includes selecting at least two output objectives or constraints, and identifying at least one controllable or uncontrollable variable suitable for achieving the at least two output objectives or constraints. The method also includes validating each predictive model and performing multi-objective optimization using the predictive models. The multi-objective optimization includes specifying search constraints and applying a multi-objective optimization algorithm. The method further includes generating a Pareto Frontier, and selecting a Pareto optimal input-output vector.

Abstract: A method and system for performing multi-objective predictive modeling, monitoring, and update for an asset is provided. The method includes determining a status of each of at least two predictive models for an asset as a result of monitoring predicted performance values. The status of each predictive model includes at least one of: acceptable performance values, validating model, and unacceptable performance values. Based upon the status of each predictive model, the method includes performing at least one of: terminating use of the at least two predictive models for the asset, generating an alert for the asset of the status of the at least two predictive models, and updating the at least two predictive models based upon the status of the at least two predictive models.

Abstract: Methods, systems, and computer program products are provided for implementing condition monitoring activities. Systems include a processor in communication with a machine being monitored. The processor receives signals output by the machine via a signal conversion element associated with the machine. Systems also include a display device in communication with the processor for providing signatures of the signals received from the signal conversion element. Systems further include a means for identifying, isolating, and capturing a signature from the signatures presented on the display device. The systems also include a means for digitizing and recording the signature as an event kernel, normalizing the event kernel by performing a mean removal, and normalizing the energy to unity on results of the mean removal. Systems further include a storage device for storing normalized event kernels.

Abstract: A method and system for designing a neural network classifier and such a neural network for an automated insurance underwriting system and/or its quality assurance is described. While the design method is demonstrated for quality assurance of automated insurance underwriting, it is broadly applicable to diverse decision-making applications in business, commercial, and manufacturing processes. Specifically, multi-class classification problems are solved by decomposing a multi-class classifier into multiple binary-classifiers, which reduces the complexity of the neural network structure, thus reducing the training time and improving the classification performance. Furthermore, the invention also describes a method to incorporate the domain knowledge into the neural network classifier. Both methods work to improve the performance of the classifier.

Abstract: A system, process and computer program product for underwriting a financial risk instrument application represented by at least one risk attribute is provided. Decision engines examine the at least one risk attribute associated with the financial risk instrument application and assign the application to one of a predetermined set of risk classes. A fusion engine compares the risk classes assigned by each of the decision engines and fuses the assigned risk classes into an aggregated result representative of the risk of the financial risk instrument application. The fusion engine includes a first multi-classifier fusion module that uses an associative function to fuse the assigned risk classes into a first aggregated result and a second multi-classifier fusion that uses a non-associative function to fuse the assigned risk classes into a second aggregated result.

Abstract: A method and system for automating the decision-making process used in underwriting of insurance applications is described. While this approach is demonstrated for insurance underwriting, it is broadly applicable to diverse decision-making applications in business, commercial, and manufacturing processes. A structured methodology is used based on a multi-model parallel network of multivariate adaptive regression splines (“MARS”) models to identify the relevant set of variables and their parameters, and build a framework capable of providing automated decisions. The parameters of the MARS-based decision system are estimated from a database consisting of a set of applications with reference decisions against each. Cross-validation and development/hold-out combined with re-sampling techniques are used to build a robust set of models that minimize the error between the automated system's decision and the expert human underwriter.

Abstract: A method and system for fusing a collection of classifiers used for an automated insurance underwriting system and/or its quality assurance is described. Specifically, the outputs of a collection of classifiers are fused. The fusion of the data will typically result in some amount of consensus and some amount of conflict among the classifiers. The consensus will be measured and used to estimate a degree of confidence in the fused decisions. Based on the decision and degree of confidence of the fusion and the decision and degree of confidence of the production decision engine, a comparison module may then be used to identify cases for audit, cases for augmenting the training/test sets for re-tuning production decision engine, cases for review, or may simply trigger a record of its occurrence for tracking purposes. The fusion can compensate for the potential correlation among the classifiers.

Abstract: A technique is provided for reducing motion-related artifacts present in CT images attributable to the dynamic nature of the imaged tissue or the time-varying concentration of a contrast agent. A region of interest that encompasses a structure of diagnostic significance is selected. For projections acquired by the various rows of detectors in a multi-slice CT imaging system, the portions of the projections attributable to the projection of the region of interest are averaged for each view angle. The projections containing these averaged values are then combined with the projections which do not encompass the region of interest. The combined projection set may be reconstructed to form a CT image of the dynamic tissue. In addition, a smoothing step may be performed to interpolate projection values around the region of interest to smooth the visual transition to the unaveraged portions of the image.

Abstract: A technique is provided for minimizing or eliminating motion-related artifacts present in CT images due to the dynamic nature of the imaged tissue or the time-varying concentration of a contrast agent used for imaging. The technique allows for the selection of a region of interest that may encompass a structure of diagnostic interest. For projections acquired by the various rows of detectors in a multi-slice CT imaging system, the portions of the projections attributable to the projection of the region of interest within the detector are averaged for each view angle. The projections containing these averaged values are then combined with the projections which do not encompass the region of interest and the combined projection set may be reconstructed to form a CT image of the dynamic tissue. In addition, a smoothing step may be performed to interpolate projection values around the region of interest to smooth the visual transition to the unaveraged portions of the image.