Patents by Inventor Xuefei Guan

Xuefei Guan has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 10352794
    Abstract: A method dynamically reconstructing a stress and strain field of a turbine blade includes providing a set of response measurements from at least one location on a turbine blade, band-pass filtering the set of response measurements based on an upper frequency limit and a lower frequency limit, determining an upper envelope and a lower envelope of the set of response measurements from local minima and local maxima of the set of response measurements, calculating a candidate intrinsic mode function (IMF) from the upper envelope and the lower envelope of the set of response measurements, providing an N×N mode shape matrix for the turbine blade, where N is the number of degrees of freedom of the turbine blade, when the candidate IMF is an actual IMF, and calculating a response for another location on the turbine blade from the actual IMF and mode shapes in the mode shape matrix.
    Type: Grant
    Filed: September 17, 2013
    Date of Patent: July 16, 2019
    Assignee: Siemens Energy, Inc.
    Inventors: Xuefei Guan, Jingdan Zhang, Shaohua Kevin Zhou, Nancy H. Ulerich, Nam Eung Kim, Nikolai R. Tevs
  • Patent number: 9835596
    Abstract: A method and software system for flaw identification, grouping and sizing for fatigue life assessment for rotors used in turbines and generators. The method includes providing ultrasonic data of a plurality of rotor slices and providing volume reconstruction of the ultrasonic data. The method also includes providing in-slice identification, grouping and sizing of flaw indications in the rotor based on the volume reconstruction. Further, the method includes providing inter-slice identification, grouping and sizing of the flaw indications based on the in-slice flaw indications and providing flaw location and size information. The method can be used in both phased-array and A-scan inspections.
    Type: Grant
    Filed: January 3, 2014
    Date of Patent: December 5, 2017
    Assignee: Siemens Energy, Inc.
    Inventors: Xuefei Guan, Jingdan Zhang, Shaohua Kevin Zhou, El Mahjoub Rasselkorde, Waheed A. Abbasi, Steve H. Radke, Chin-Sheng Lee, Ashley L. Lewis
  • Patent number: 9792555
    Abstract: A method for probabilistic fatigue life prediction using nondestructive testing data considering uncertainties from nondestructive examination (NDE) data and fatigue model parameters. The method utilizes uncertainty quantification models for detection, sizing, fatigue model parameters and inputs. A probability of detection model is developed based on a log-linear model coupling an actual flaw size with a nondestructive examination (NDE) reported size. A distribution of the actual flaw size is derived for both NDE data without flaw indications and NDE data with flaw indications by using probabilistic modeling and Bayes theorem. A turbine rotor example with real world NDE inspection data is presented to demonstrate the overall methodology.
    Type: Grant
    Filed: December 16, 2013
    Date of Patent: October 17, 2017
    Assignee: Siemens Energy, Inc.
    Inventors: Xuefei Guan, Jingdan Zhang, Shaohua Kevin Zhou, Kai Kadau, Yan Guo, El Mahjoub Rasselkorde, Waheed A. Abbasi, Chin-Sheng Lee, Ashley L. Lewis, Steve H. Radke
  • Patent number: 9658192
    Abstract: In a general methodology for insulation defect identification in a generator core, a Chattock coil is used to measure magnetic potential difference between teeth. Physical knowledge and empirical knowledge is combined in a model to predict insulation damage location and severity. Measurements are taken at multiple excitation frequencies to solve for multiple characteristics of the defect.
    Type: Grant
    Filed: January 16, 2013
    Date of Patent: May 23, 2017
    Assignees: Siemens Corporation, Siemens Energy, Inc.
    Inventors: Xuefei Guan, Jingdan Zhang, Shaohua Kevin Zhou, Mark W. Fischer, Waheed A. Abbasi, Scott A. Karstetter, Christopher John William Adams
  • Patent number: 9639637
    Abstract: A method for predicting fatigue crack growth in materials includes providing a prior distribution obtained using response measures from one or more target components using a fatigue crack growth model as a constraint function, receiving new crack length measurements, providing a posterior distribution obtained using the new crack length measurements, and sampling the posterior distribution to obtain crack length measurement predictions.
    Type: Grant
    Filed: August 30, 2013
    Date of Patent: May 2, 2017
    Assignee: Siemens Aktiengesellschaft
    Inventors: Xuefei Guan, Jingdan Zhang, Shaohua Kevin Zhou
  • Patent number: 9541530
    Abstract: A method of fatigue life prediction including: calculating a critical crack size of an object of interest; identifying a first flaw in ultrasound data of the object of interest; determining that the first flaw interacts with a second flaw, the first flaw is to be merged with the second flaw, or the first flaw is isolated; calculating an initial crack size based on the determination; and calculating an increase in the initial crack size due to fatigue and creep to determine a number of load cycles until the initial crack size reaches the critical crack size.
    Type: Grant
    Filed: January 17, 2013
    Date of Patent: January 10, 2017
    Assignee: Siemens Energy, Inc.
    Inventors: Xuefei Guan, Hui Zhen, Jingdan Zhang, Shaohua Kevin Zhou, Ashley L. Lewis, Steve H. Radke, Chin-Sheng Lee
  • Publication number: 20140229149
    Abstract: A method for probabilistic fatigue life prediction using nondestructive testing data considering uncertainties from nondestructive examination (NDE) data and fatigue model parameters. The method utilizes uncertainty quantification models for detection, sizing, fatigue model parameters and inputs. A probability of detection model is developed based on a log-linear model coupling an actual flaw size with a nondestructive examination (NDE) reported size. A distribution of the actual flaw size is derived for both NDE data without flaw indications and NDE data with flaw indications by using probabilistic modeling and Bayes theorem. A turbine rotor example with real world NDE inspection data is presented to demonstrate the overall methodology.
    Type: Application
    Filed: December 16, 2013
    Publication date: August 14, 2014
    Applicants: SIEMENS CORPORATION, SIEMENS ENERGY, INC., SIEMENS AKTIENGESELLSCHAFT
    Inventors: Xuefei Guan, Jingdan Zhang, Shaohua Kevin Zhou, Kai Kadau, Yan Guo, El Mahjoub Rasselkorde, Waheed A. Abbasi, Chin-Sheng Lee, Ashley L. Lewis, Steve H. Radke
  • Publication number: 20140200853
    Abstract: A method and software system for flaw identification, grouping and sizing for fatigue life assessment for rotors used in turbines and generators. The method includes providing ultrasonic data of a plurality of rotor slices and providing volume reconstruction of the ultrasonic data. The method also includes providing in-slice identification, grouping and sizing of flaw indications in the rotor based on the volume reconstruction. Further, the method includes providing inter-slice identification, grouping and sizing of the flaw indications based on the in-slice flaw indications and providing flaw location and size information. The method can be used in both phased-array and A-scan inspections.
    Type: Application
    Filed: January 3, 2014
    Publication date: July 17, 2014
    Applicants: SIEMENS ENERGY, INC., SIEMENS CORPORATION
    Inventors: Xuefei Guan, Jingdan Zhang, Shaohua Kevin Zhou, El Mahjoub Rasselkorde, Waheed A. Abbasi, Steve H. Radke, Chin-Sheng Lee, Ashley L. Lewis
  • Publication number: 20140100827
    Abstract: A method for predicting fatigue crack growth in materials includes providing a prior distribution obtained using response measures from one or more target components using a fatigue crack growth model as a constraint function, receiving new crack length measurements, providing a posterior distribution obtained using the new crack length measurements, and sampling the posterior distribution to obtain crack length measurement predictions.
    Type: Application
    Filed: August 30, 2013
    Publication date: April 10, 2014
    Applicant: SIEMENS CORPORATION
    Inventors: Xuefei Guan, Jingdan Zhang, Shaohua Kevin Zhou
  • Publication number: 20140100798
    Abstract: A method dynamically reconstructing a stress and strain field of a turbine blade includes providing a set of response measurements from at least one location on a turbine blade, band-pass filtering the set of response measurements based on an upper frequency limit and a lower frequency limit, determining an upper envelope and a lower envelope of the set of response measurements from local minima and local maxima of the set of response measurements, calculating a candidate intrinsic mode function (IMF) from the upper envelope and the lower envelope of the set of response measurements, providing an N×N mode shape matrix for the turbine blade, where N is the number of degrees of freedom of the turbine blade, when the candidate IMF is an actual IMF, and calculating a response for another location on the turbine blade from the actual IMF and mode shapes in the mode shape matrix.
    Type: Application
    Filed: September 17, 2013
    Publication date: April 10, 2014
    Applicants: SIEMENS ENERGY, INC., SIEMENS CORPORATION
    Inventors: Xuefei Guan, Jingdan Zhang, Shaohua Kevin Zhou, Nancy H. Ulerich, Nam Eung Kim, Nikolai R. Tevs
  • Publication number: 20130268214
    Abstract: A method for probabilistically predicting fatigue life in materials includes sampling a random variable for an actual equivalent initial flaw size (EIFS), generating random variables for parameters (ln C, m) of a fatigue crack growth equation ? a ? N = C ? ( ? ? ? K ) m from a multivariate distribution, and solving the fatigue crack growth equation using these random variables. The reported EIFS data is obtained by ultrasonically scanning a target object, recording echo signals from the target object, and converting echo signal amplitudes to equivalent reflector sizes using previously recorded values from a scanned calibration block. The equivalent reflector sizes comprise the reported EIFS data.
    Type: Application
    Filed: April 2, 2013
    Publication date: October 10, 2013
    Applicants: Siemens Aktiengesellschaft, Siemens Corporation
    Inventors: Xuefei Guan, Jingdan Zhang, Kai Kadau, Shaohua Kevin Zhou
  • Publication number: 20130191041
    Abstract: In a general methodology for insulation defect identification in a generator core, a Chattock coil is used to measure magnetic potential difference between teeth. Physical knowledge and empirical knowledge is combined in a model to predict insulation damage location and severity. Measurements are taken at multiple excitation frequencies to solve for multiple characteristics of the defect.
    Type: Application
    Filed: January 16, 2013
    Publication date: July 25, 2013
    Applicants: Siemens Energy, Inc., Siemens Corporation
    Inventors: Xuefei Guan, Jingdan Zhang, Shaohua Kevin Zhou, Mark W. Fischer, Waheed A. Abbasi, Scott A. Karstetter, Christopher John William Adams
  • Publication number: 20130191039
    Abstract: A method of fatigue life prediction including: calculating a critical crack size of an object of interest; identifying a first flaw in ultrasound data of the object of interest; determining that the first flaw interacts with a second flaw, the first flaw is to be merged with the second flaw, or the first flaw is isolated; calculating an initial crack size based on the determination; and calculating an increase in the initial crack size due to fatigue and creep to determine a number of load cycles until the initial crack size reaches the critical crack size.
    Type: Application
    Filed: January 17, 2013
    Publication date: July 25, 2013
    Applicants: Siemens Energy, Inc., Siemens Corporation
    Inventors: Xuefei Guan, Hui Zhen, Jingdan Zhang, Shaohua Kevin Zhou, Ashley L. Lewis, Steve H. Radke, Chin-Sheng Lee