General Electric Company

Novity, Inc.

United States of America As Represented by the Administrator of Nasa

Palo Alto Research Center Inc.

Lockheed Martin Corporation

Kai F Goebel

Weizhong Yan

Piero Patrone Bonissone

Neil Holger White Eklund

Naresh Sundaram Iyer

Feng Xue

John Erik Hershey

Bhaskar Saha

Anil Varma

Hai Qiu

David Lacey Doel

Peter Michael Edic

Vivek Venugopal Badami

Daniel Lynn Larner

Charles Erklin Seeley

Pierino Gianni Bonanni

Rajesh Venkat Subbu

Charles Terrance Hatch

James Kenneth Aragones

Catherine Mary Graichen

Maria Iatrou

Malcolm John Ashby

Erdogan Cesmeli

Gary Paul Moscarino

Matthew Allen Nelson

Ron Zvi Stern

Randal T Rausch

Evgeniy Bart

Serdar Uckun

Abhinav Saxena

Jose R Celaya

Dean Kimball Frederick

Michael Robert Dometita

Krishna M Kalyanam

A system and method utilize a first rotation sensor that provides a first signal indicative of a first rotation of a first end of a rotating coupling. The rotating coupling has an articulating member mechanically coupling the first end to a second end. A second rotation sensor provides a second signal indicative of a second rotation of the second end of the rotating coupling. A processor is coupled to the first and second rotation sensors and is operable to determine a relationship between the first and second rotations based on the first and second signals. Based on the relationship, the processor estimates at least one of a degradation of and a remaining useful life of the rotating coupling.

System and method for determining degradation of rotating coupling using rotation sensors

A flexible device mounting kit allows to securely attach a sensor or another device to an arbitrary surface, including a surface that is very uneven. The kit includes a scaffolding assembly which includes a scaffolding guide and scaffolding teeth attached to the guide in a way that allows some of the teeth to move relative to the guide when the bottom of the teeth is pressed against uneven surface. When the assembly is pressed against the surface, the positions of the teeth adjust, forming, together with the surface, a cavity into which a gluing compound can be filled. A device mount to which a sensor (or another device) can be attached is pressed into the gluing compound before the gluing compound solidifies. As the gluing compound securely connects the sensor mount to the surface, the device can be securely placed within the mount regardless of how uneven the surface is.

Flexible device mounting kit

One embodiment provides a system for processing natural-language entries. The system obtains a plurality of historical natural-language entries associated with a first domain and pre-processes the historical natural-language entries to obtain a set of generic terms and a set of domain-specific terms. The system trains a machine learning model in the first domain using the plurality of historical natural-language entries associated with the first domain. The training comprises learning weight values of one or more generic terms, a weight value of a respective generic term indicating likelihood that the generic term is related to a trigger event. The system generalizes the machine learning model trained in the first domain, thereby allowing the model to be applied to a second domain. The system processes a natural-language entry associated with the second domain using the generalized machine learning model to determine whether the natural-language entry associated with the second domain corresponds to the trigger event.

Information extraction from maintenance logs

A condition of an asset including one or more subsystems connected in series is monitored. Each subsystem includes one or more components connected in parallel. The asset has one or more jobs. A probability of the asset surviving a predetermined amount of time is determined based on the monitoring and one or more shared resources. The one or more shared resources are configured to be shared between the subsystems. A model is established using a threshold based heuristic maintenance policy. The model is configured to maximize a number of successful jobs that the asset is able to complete based on the determined probability. The one or more shared resources are allocated to the one or more subsystems based on the model.

Threshold based selective maintenance of series-parallel systems

A system for predicting remaining useful life of a component implements a set of estimation models that generate future damage estimates for the component. The system detects damage to the component and estimates the magnitude of the current damaged. An error processor estimates the between each future damage estimate and the magnitude of current damage. A weight calculator calculates weights for the future damage estimates, wherein each weight is inversely proportional to the error. A fusion processor applies the weights respectively to future damage estimates of the estimators and combines the weighted future damage estimates.

Weight assignment for fusion of prognostic estimators

Systems and methods include monitoring a health of at least one asset. A remaining useful life (RUL) of the at least one asset is estimated based on the monitoring. The RUL of the asset is categorized into categories comprising shorter than a time to complete a current mission and longer than the time to complete the current mission. One or more remedial actions are automatically performed during the current mission if the RUL is categorized as being less than the time to complete the current mission. The remedial actions comprise one or more of initiating a fail-safe mode, adapting a controller of the one or more assets, reconfiguration of the system, and adjusting the current mission of the one or more assets. Maintenance is scheduled for after the current mission of the at least one asset if the RUL is categorized as being greater than or equal to the time to complete the current mission.

Prognostics driven decision making

The prognostic tool disclosed here decomposes the problem of estimating the remaining useful life (RUL) of a component or sub-system into two separate regression problems: the feature-to-damage mapping and the operational conditions-to-damage-rate mapping. These maps are initially generated in off-line mode. One or more regression algorithms are used to generate each of these maps from measurements (and features derived from these), operational conditions, and ground truth information. This decomposition technique allows for the explicit quantification and management of different sources of uncertainty present in the process. Next, the maps are used in an on-line mode where run-time data (sensor measurements and operational conditions) are used in conjunction with the maps generated in off-line mode to estimate both current damage state as well as future damage accumulation. Remaining life is computed by subtracting the instance when the extrapolated damage reaches the failure threshold from the instance when the prediction is made.

Decomposition technique for remaining useful life prediction

This invention develops a mathematical model to describe battery behavior during individual discharge cycles as well as over its cycle life. The basis for the form of the model has been linked to the internal processes of the battery and validated using experimental data. Effects of temperature and load current have also been incorporated into the model. Subsequently, the model has been used in a Particle Filtering framework to make predictions of remaining useful life for individual discharge cycles as well as for cycle life. The prediction performance was found to be satisfactory as measured by performance metrics customized for prognostics for a sample case. The work presented here provides initial steps towards a comprehensive health management solution for energy storage devices.

Model-based prognostics for batteries which estimates useful life and uses a probability density function

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.

System and method for defining normal operating regions and identifying anomalous behavior of units within a fleet, operating in a complex, dynamic environment

A method to estimate damage propagation is disclosed. The method includes making available a set of input parameters to a computational model, executing the computational model with defined changes within a range of an input parameter of the set of input parameters to define a range of at least one modeled output, receiving at least one signal responsive to and representative of a respective one of an actual sensor output, and estimating damage propagation based upon a correlation of the received signal to the modeled output.

System and method for damage propagation estimation

A method for multi-objective fault accommodation using predictive modeling is disclosed. The method includes using a simulated machine that simulates a faulted actual machine, and using a simulated controller that simulates an actual controller. A multi-objective optimization process is performed, based on specified control settings for the simulated controller and specified operational scenarios for the simulated machine controlled by the simulated controller, to generate a Pareto frontier-based solution space relating performance of the simulated machine to settings of the simulated controller, including adjustment to the operational scenarios to represent a fault condition of the simulated machine. Control settings of the actual controller are adjusted, represented by the simulated controller, for controlling the actual machine, represented by the simulated machine, in response to a fault condition of the actual machine, based on the Pareto frontier-based solution space, to maximize desirable operational conditions and minimize undesirable operational conditions while operating the actual machine in a region of the solution space defined by the Pareto frontier.

Method and system for fault accommodation of machines

A method to predict remaining life of a target is disclosed. The method includes receiving information regarding a behavior of the target, and identifying from a database at least one piece of equipment having similarities to the target. The method further includes retrieving from the database data prior to an end of the equipment useful life, the data having a relationship to the behavior, evaluating a similarity of the relationship, predicting the remaining life of the target based upon the similarity, and generating a signal corresponding to the predicted remaining equipment life.

System and method for equipment remaining life estimation

A system and method for monitoring the vibrations of a machine that includes a reflective patch affixed to the machine and a vibration detection unit including an optics module. The optics module may be positioned remotely from the machine such that the optics module transmits an electromagnetic beam to the reflective patch and reflected from the reflective patch to the optics module. The optics module demodulates the electromagnetic beam to determine the vibration of the machine.

Systems and methods for remote monitoring of vibrations in machines

A method to reduce uncertainty bounds of predicting a remaining life of a probe using a set of diverse models is disclosed. The method includes generating an estimated remaining life output by each model of the set of diverse models, aggregating each of the respective estimated remaining life outputs via a fusion model, and in response to the aggregating, predicting the remaining life, the predicting having reduced uncertainty bounds based on the aggregating. The method further includes generating a signal corresponding to the predicted remaining life of the probe.

A method to predict equipment life is disclosed. The method includes making available a set of input parameters, and defining a model of a health of the equipment as a function of the set of input parameters. The method continues with receiving at least one signal representative of a respective one of an actual sensor output relating to an actual operation attribute margin of the equipment, predicting a remaining useful equipment life based upon a sequence of outputs of the model of the health of the equipment, and generating a signal corresponding to the remaining useful equipment life.

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Mountain View, CA, United States of America

Kai F Goebel