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公开(公告)号：US11340602B2
公开(公告)日：2022-05-24
申请号：US15535909
申请日：2015-12-18
摘要： A method includes converting time-series data from a plurality of prognostic and health monitoring (PHM) sensors into frequency domain data. One or more portions of the frequency domain data are labeled as indicative of one or more target modes to form labeled target data. A model including a deep neural network is applied to the labeled target data. A result of applying the model is classified as one or more discretized PHM training indicators associated with the one or more target modes. The one or more discretized PHM training indicators are output.
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公开(公告)号：US10387803B2
公开(公告)日：2019-08-20
申请号：US15840132
申请日：2017-12-13
摘要： A sensor system may comprise a sensor; a processor in electronic communication with the sensor; and/or a tangible, non-transitory memory configured to communicate with the processor, the tangible, non-transitory memory having instructions stored thereon that, in response to execution by the processor, cause the processor to perform operations. The operations may comprise recording, by the sensor, a preliminary type data sample; and/or applying, by the processor, a mapping function having a plurality of tuned parameters to the preliminary type data sample, producing a desired type data output.
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公开(公告)号：US20190147283A1
公开(公告)日：2019-05-16
申请号：US16099485
申请日：2016-05-16
摘要： A method includes detecting at least one region of interest in a frame of image data. One or more patches of interest are detected in the frame of image data based on detecting the at least one region of interest. A model including a deep convolutional neural network is applied to the one or more patches of interest. Post-processing of a result of applying the model is performed to produce a post-processing result for the one or more patches of interest. A visual indication of a classification of defects in a structure is output based on the result of the post-processing.
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4.发明申请公开(公告)号：US20190096056A1
公开(公告)日：2019-03-28
申请号：US15714339
申请日：2017-09-25
CPC分类号： G06T7/0006 , G06K9/6202 , G06K9/6289 , G06T7/0004 , G06T7/001 , G06T7/12 , G06T7/13 , G06T7/174 , G06T2207/20076 , G06T2207/20081 , G06T2207/20084 , G06T2207/20152 , G06T2207/20221 , G06T2207/30136 , G06T2207/30164 , G06T2207/30242
摘要： A material characterization system includes an imaging unit, a material characterization controller, and an imaging unit controller. The electronic imaging unit generates a test image of a specimen composed of a material. The electronic material characterization controller determines values of a plurality of parameters and maps the parameters to corresponding ground truth labeled outputs. The mapped parameters are applied to at least one test image to predict a presence of at least one target attribute of the specimen in response to applying the learned parameters. The test image is convert to a selected output image format so as to generate a synthetic image including the predicted at least one attribute. The electronic imaging unit controller performs a material characterization analysis that characterizes the material of the specimen based on the predicted at least one attribute included in the synthetic image.
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公开(公告)号：US20180129974A1
公开(公告)日：2018-05-10
申请号：US15797035
申请日：2017-10-30
CPC分类号： G06N20/00 , G05B13/027 , G05B13/029 , G06N3/0454 , G06N3/08 , G06N5/022
摘要： Data indicative of a plurality of observations of an environment are received at a control system. Machine learning using deep reinforcement learning is applied to determine an action based on the observations. The deep reinforcement learning applies a convolutional neural network or a deep auto encoder to the observations and applies a training set to locate one or more regions having a higher reward. The action is applied to the environment. A reward token indicative of alignment between the action and a desired result is received. A policy parameter of the control system is updated based on the reward token. The updated policy parameter is applied to determine a subsequent action responsive to a subsequent observation.
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公开(公告)号：US11422546B2
公开(公告)日：2022-08-23
申请号：US15536713
申请日：2015-12-18
摘要： A method includes fusing multi-modal sensor data from a plurality of sensors having different modalities. At least one region of interest is detected in the multi-modal sensor data. One or more patches of interest are detected in the multi-modal sensor data based on detecting the at least one region of interest. A model that uses a deep convolutional neural network is applied to the one or more patches of interest. Post-processing of a result of applying the model is performed to produce a post-processing result for the one or more patches of interest. A perception indication of the post-processing result is output.
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公开(公告)号：US10409275B2
公开(公告)日：2019-09-10
申请号：US15297319
申请日：2016-10-19
IPC分类号： G05B23/02 , F01D21/00 , F01D25/16 , F01D25/18 , G01N33/28 , G05B13/02 , F16N29/00 , G06N20/00 , F16H57/04
摘要： A system and method for debris particle detection with adaptive learning are provided. The method includes receiving oil debris monitoring (ODM) sensor data from an oil debris monitor sensor and fleet data from a database, detecting a feature in the ODM sensor data, generating an anomaly detection signal based on detecting an anomaly by comparing the feature in the ODM sensor data to a limit defined by system information stored in the fleet data, selecting a maintenance action request based on the anomaly detection signal, and adjusting one or more of the feature, the anomaly, the limit, and the maintenance action request by applying an adaptive learning algorithm that uses the ODM sensor data, fleet data, and feedback from field maintenance of one or more engines that evolves over time.
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公开(公告)号：US20180217585A1
公开(公告)日：2018-08-02
申请号：US15535909
申请日：2015-12-18
CPC分类号： G05B23/0254 , G05B23/0294 , G06K9/00791 , G06K9/4628 , G06K9/6273 , G06K9/6289 , G06N3/0454 , G06N3/08 , G06T7/248
摘要： A method includes converting time-series data from a plurality of prognostic and health monitoring (PHM) sensors into frequency domain data. One or more portions of the frequency domain data are labeled as indicative of one or more target modes to form labeled target data. A model including a deep neural network is applied to the labeled target data. A result of applying the model is classified as one or more discretized PHM training indicators associated with the one or more target modes. The one or more discretized PHM training indicators are output.
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公开(公告)号：US20180023414A1
公开(公告)日：2018-01-25
申请号：US15218307
申请日：2016-07-25
发明人： Gregory S. Hagen , Yiqing Lin , Alexander I. Khibnik , Meggan Harris , Ozgur Erdinc , Michael J. Giering
CPC分类号： F01D21/003 , F02C7/06 , F05D2220/32 , F05D2260/98 , F16N29/04 , F16N2250/32 , G01N33/2858 , G01N33/2888
摘要： A method of detecting debris within a lubricant stream, the method includes generating data indicative of debris and sensor system functionality within a lubricant stream with a sensor system. The data is communicated to a controller. Features calculated from the data are indicative of a debris within the lubricant stream. The calculated features are compiled over time during operation. The compiled features are classified. An oil debris monitoring system for a turbine engine is also disclosed.
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公开(公告)号：US11485520B2
公开(公告)日：2022-11-01
申请号：US16104435
申请日：2018-08-17
摘要： A method for designing a material for an aircraft component includes training a neural network to correlate microstructural features of an alloy with material properties of the alloy by at least providing a set of images of the alloy to the neural network. Each of the images in the set of images has varied constituent compositions. The method further includes providing the neural network with a set of determined material properties corresponding to each image, associating the microstructural features of each image with the set of empirically determined data corresponding to the image, and determining non-linear relationships between the microstructural features and corresponding empirically determined material properties via a machine learning algorithm, receiving a set of desired material properties of the alloy for aircraft component, and determining a set of microstructural features capable of achieving the desired material properties of the alloy based on the determined non-linear relationships.
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