Abstract: An apparatus and associated method are utilized for monitoring an operation of a system characterized by operational parameters. A non-parametric empirical model generates estimates of parameter values in response to receiving a query vector of monitored parameters for a model characterizing the system. A distance estimation engine (a) determines robust distances between the query vector and each of a set of predetermined historical vectors for the non-parametric empirical model based on an implementation of an elemental kernel function; (b) determines weights for the monitored parameters based on the robust distances; and (c) combining the weights with the predetermined historical vectors to make predictions for the system.

Abstract: A system for assessing the health of a mechanism includes a processor for receiving observation data from at least one sensor, the processor including: a detector receptive to the observation data and capable of identifying whether the mechanism is operating in a normal or degraded mode; a diagnoser to identify a type of fault from at least one symptom pattern; and a prognoser capable of calculating a remaining useful life (RUL) of the mechanism, wherein the prognoser includes a population prognoser for calculating the RUL based on a duration of use of the mechanism, a cause prognoser for calculating the RUL based on causal data, and an effect prognoser for calculating the RUL based on effect data generated from the fault. A method and computer program product for assessing the health of a downhole tool is also disclosed.

Abstract: A method for modeling the behavior of a visitor to an e-commerce location includes the steps of automatically obtaining one or more visitor characteristic values, and automatically developing a model of the visitor's behavior according to a nonlinear state estimation technique (NSET). The method also includes then automatically estimating a set of visitor behavior characteristic values with said model that model said visitor's behavior.

Abstract: A system for assessing the health of a mechanism includes a processor for receiving observation data from at least one sensor, the processor including: a detector receptive to the observation data and capable of identifying whether the mechanism is operating in a normal or degraded mode; a diagnoser to identify a type of fault from at least one symptom pattern; and a prognoser capable of calculating a remaining useful life (RUL) of the mechanism, wherein the prognoser includes a population prognoser for calculating the RUL based on a duration of use of the mechanism, a cause prognoser for calculating the RUL based on causal data, and an effect prognoser for calculating the RUL based on effect data generated from the fault. A method and computer program product for assessing the health of a downhole tool is also disclosed.

Abstract: An apparatus and associated method are utilized for monitoring an operation of a system characterized by operational parameters. A non-parametric empirical model generates estimates of parameter values in response to receiving a query vector of monitored parameters for a model characterizing the system. A distance estimation engine (a) determines robust distances between the query vector and each of a set of predetermined historical vectors for the non-parametric empirical model based on an implementation of an elemental kernel function; (b) determines weights for the monitored parameters based on the robust distances; and (c) combining the weights with the predetermined historical vectors to make predictions for the system.

Abstract: An NSET method and apparatus for modeling and monitoring the status of a system is disclosed. The NSET employs a nonlinear similarity operator in place of linear matrix multiplication, to estimate a set of sensor data based on learned reference data, responsive to receiving a set of actual sensor data. Regularization is used in the generation of the estimate. The estimated data values and the actual sensor data are differenced to produce residuals, which are statistically tested with a SPRT to detect anomalies. Cluster centers may be used to represent learned reference data. The detection of anomalies can be used advantageously for sensor calibration verification.