Abstract: A flexible prognostics and health management (PHM) device is disclosed. Sensor data is generated that quantifies real-time conditions of components in a machine based on sensor signals generated by sensors. A PHM system loads a PHM module that implements externally invokable functions. The PHM system receives an analytics script. The analytics script comprises interpretable commands and predictive learning model parameter values associated with a predictive learning model. Some of the interpretable commands are configured to invoke some of the externally invokable functions with at least some of the predictive learning model parameter values. The PHM module invokes a command interpreter module to interpret an interpretable command and the predictive learning model parameter values and generate first executable bytecodes. The first executable bytecodes execute to generate a notification identifying a predicted future condition of a first component for presentation on a display device.

Abstract: Deriving a clean timing signal from a waveform is disclosed. A sensor-of-interest (SOI) sample set and a waveform sample set that correspond to the SOI sample set in time is collected. The waveform sample set is partitioned into a plurality of waveform sample subsets, and the SOI sample set is partitioned into a plurality of SOI sample subsets, each SOI sample subset corresponding to one of the plurality of waveform sample subsets. A plurality of waveform sample subset angular speeds is determined, wherein each waveform sample subset angular speed corresponds to a different waveform sample subset. An aggregate mean angular speed based on the plurality of waveform sample subset angular speeds is determined. Each SOI sample subset is resampled to the aggregate mean angular speed based on the corresponding waveform sample subset angular speed to generate a plurality of resampled SOI subsets.

Abstract: Mechanisms for generating an analysis result about a machine are provided. A device generates a first health management (HM) analysis result regarding a machine based on real-time first sensor information received during a first period of time and on a first version HM analytic model. The device provides, to an off-board device, a plurality of sensor information comprising the real-time first sensor information and that is generated during the first period of time. The device receives a second version HM analytic model that is based at least in part on the plurality of sensor information and fault information that identifies actual faults that have occurred on the machine. The device generates a second HM analysis result regarding the machine based on real-time second sensor information received during a second period of time and on the second version HM analytic model.

Abstract: A flexible condition-based maintenance (CBM) device is disclosed. A CBM device receives a plurality of sensor signals generated by corresponding sensors. The sensor signals indicate real-time conditions of components. The sensor signals are processed to generate sensor data that quantifies the real-time conditions of the components. The CBM device loads a CBM module that implements a plurality of externally invokable functions. The CBM device receives an analytics script. The first analytics script comprises a plurality of interpretable commands, and some of the plurality of interpretable commands are configured to invoke some of the plurality of externally invokable functions. The CBM module invokes a command interpreter module to interpret at least one interpretable command and generate command executable bytecodes. The command executable bytecodes are executed to generate a notification identifying a real-time condition of a component for presentation on a display device.

Abstract: Quantification of a condition of a selected item using a neural network is disclosed. A device defines a good hypertube in a neural state space based on good item state points obtained from one or more items that exhibit desired operating characteristics, and a bad hypertube in the neural state space based on bad item state points obtained from one or more items that exhibit undesirable operating characteristics. A current item state hyperpoint is determined in the neural state space based on a current item state point of the selected item. A condition of the selected item is quantified as a function of a location of the current item state hyperpoint with respect to at least a portion of the good hypertube and with respect to at least a portion of the bad hypertube.

Abstract: A flexible condition-based maintenance (CBM) device is disclosed. A CBM device receives a plurality of sensor signals generated by corresponding sensors. The sensor signals indicate real-time conditions of components. The sensor signals are processed to generate sensor data that quantifies the real-time conditions of the components. The CBM device loads a CBM module that implements a plurality of externally invokable functions. The CBM device receives an analytics script. The first analytics script comprises a plurality of interpretable commands, and some of the plurality of interpretable commands are configured to invoke some of the plurality of externally invokable functions. The CBM module invokes a command interpreter module to interpret at least one interpretable command and generate command executable bytecodes. The command executable bytecodes are executed to generate a notification identifying a real-time condition of a component for presentation on a display device.

Abstract: A flexible prognostics and health management (PHM) device is disclosed. Sensor data is generated that quantifies real-time conditions of components in a machine based on sensor signals generated by sensors. A PHM system loads a PHM module that implements externally invokable functions. The PHM system receives an analytics script. The analytics script comprises interpretable commands and predictive learning model parameter values associated with a predictive learning model. Some of the interpretable commands are configured to invoke some of the externally invokable functions with at least some of the predictive learning model parameter values. The PHM module invokes a command interpreter module to interpret an interpretable command and the predictive learning model parameter values and generate first executable bytecodes. The first executable bytecodes execute to generate a notification identifying a predicted future condition of a first component for presentation on a display device.

Abstract: Systems and methods are disclosed for more accurately estimating a remaining useful life (RUL) of an item. The RUL of the item is estimated by determining probability values throughout a time period. Each probability value quantifies a probability that a failure event of the item will occur by a time in a time segment of the time period. Based on the probability values, the particular time segment in which the failure event is most likely to occur is determined. Once the particular time segment is determined, the presentation of a visual representation of at least a portion of the time period can be effected. The visual representation indicates that the failure event of the item is most likely to occur during the particular time segment.

Abstract: A system includes a computer implemented Bayesian diagnostic system. The diagnostic system includes an inferencing engine and a conditional probability table that forms the basis for Bayesian inferences once the diagnostic system is trained. Each inference includes a diagnosis and associated probability of the diagnosis. A confidence generator receives the inferences, and generates a confidence measure for each inference.

Abstract: A method and device for extracting information from data representing a signal is disclosed. A set of data comprising a plurality of measurements of the signal generated at a first sampling rate is received from a sensor. A subset of the plurality of measurements is selected. A plurality of feature variables, each of which corresponds to a particular feature in a set of features that may be present in the signal are determined by deriving an underdetermined system of equations based on a selected basis function, the subset of the plurality of measurements, and the plurality of feature variables and corresponding features. The underdetermined system of equations is solved to determine a value for each feature variable using a non-linear optimization technique to minimize an L1 norm of the set of features. Feature information is stored in a storage medium.

Abstract: Deriving a clean timing signal from a waveform is disclosed. A sensor-of-interest (SOI) sample set and a waveform sample set that correspond to the SOI sample set in time is collected. The waveform sample set is partitioned into a plurality of waveform sample subsets, and the SOI sample set is partitioned into a plurality of SOI sample subsets, each SOI sample subset corresponding to one of the plurality of waveform sample subsets. A plurality of waveform sample subset angular speeds is determined, wherein each waveform sample subset angular speed corresponds to a different waveform sample subset. An aggregate mean angular speed based on the plurality of waveform sample subset angular speeds is determined. Each SOI sample subset is resampled to the aggregate mean angular speed based on the corresponding waveform sample subset angular speed to generate a plurality of resampled SOI subsets.

Abstract: Estimating a remaining useful life (RUL) of an apparatus is disclosed. A computer device may obtain a priori RUL data of an apparatus. The a priori RUL data identifies a priori RULs values of the apparatus as a function of time. Buckets are then defined in the a priori RUL data, wherein each of the buckets corresponds to a different set of the a priori RUL values in the a priori RUL data. An operational event indicator may then be obtained for the apparatus that indicates a current operational event of the apparatus. The RUL of an apparatus is estimated by determining probability values throughout a time period. Probability values are then determined based on the operational event indicator where each probability value quantifies a probability that a current RUL value of the first apparatus is within one of the buckets.

Abstract: The present invention provides methods and systems for automatically managing conference calls. More specifically, a user may join a conference call by calling a universal conference call number. The user is identified and connected with a specific conference call number without requiring the user to remember the specific conference call number.

Abstract: Mechanisms for predicting a remaining useful life of a cutter head of a milling machine that includes a plurality of flutes are disclosed. Features are extracted from reference data associated with a plurality of reference cutter heads. The reference data includes reference vibration data and reference wear data. At least two Neural Network predictive models are trained in parallel for predicting the remaining life of a new cutter head based upon the extracted features. Operational data associated with the new cutter head is obtained. The operational data includes operational vibration data and operational wear data. Features extracted from the operational data are input into an optimal predictive model of the at least two Neural Network predictive models. A remaining useful life of the new cutter head is estimated by the optimal predictive model.

Abstract: Mechanisms for generating an analysis result about a machine are provided. A device generates a first health management (HM) analysis result regarding a machine based on real-time first sensor information received during a first period of time and on a first version HM analytic model. The device provides, to an off-board device, a plurality of sensor information comprising the real-time first sensor information and that is generated during the first period of time. The device receives a second version HM analytic model that is based at least in part on the plurality of sensor information and fault information that identifies actual faults that have occurred on the machine. The device generates a second HM analysis result regarding the machine based on real-time second sensor information received during a second period of time and on the second version HM analytic model.

Abstract: The present invention provides a system and method for providing an announcement to a plurality of communication devices when a subset of those communication devices are currently engaged in a call. More specifically, an announcement may be provided to different communication devices depending upon the location of the communication device.

Abstract: A conference participant attempting to log into a conference that has been rescheduled is informed that the conference has been rescheduled to such-and-such date and/or time. If the login attempt is before or during the conference, the participant is offered information pertaining to the conference. If the login attempt is after the conference has ended, the participant is offered the information plus a recording of the conference. An identifier is associated with the conference and used by the host and the participants to uniquely identify the conference.

Abstract: A method and system for reducing a number of links in a Bayesian network. A first modified Bayesian network based on a primary Bayesian network that has a plurality of links is generated, wherein the first modified Bayesian network does not include a first subset of the plurality of links. A second modified Bayesian network based on the primary Bayesian network is generated, wherein the second modified Bayesian network does not include a second subset of the plurality of links. The first modified Bayesian network and the second modified Bayesian network are validated using a first data set to generate a first validation result and a second validation result. The primary Bayesian network is substituted with the first modified Bayesian network or the second modified Bayesian network based on the first validation result and the second validation result.

Abstract: The present invention, in one embodiment, is directed to an agent available notifier 250 that (a) receives a request from the first customer, but not from the second customer, to be notified when the first contact is eligible for servicing by a selected resource so that the first customer is free to perform a desired activity and (b) in response to the first customer's request, provides the first customer with a selected notification that the first contact is eligible for servicing, wherein the second customer is not provided with the selected notification when the second contact is eligible for servicing.

Abstract: Real-time condition-based analysis is performed on a machine for providing diagnostic and prognostic outputs indicative of machine status includes a signal processor for receiving signals from sensors adapted for measuring machine performance parameters. The signal processor conditions and shapes at least some of the received signals into an input form for a neural network. A fuzzy adaptive resonance theory neural network receives at least some of the conditioned and shaped signals, and detects and classifies a state of the machine based upon the received conditioned and shaped signals, and upon a predetermined ontology of machine states, diagnostics, and prognostics. The neural network can also determine from the machine state a health status thereof, which can comprise an anomaly, and output a signal representative of the determined health status. A Bayesian intelligence network receives the machine state from the neural network and determines a fault probability at a future time.