CA2992297C - Machine learning of physical conditions based on abstract relations and sparse labels - Google Patents

Machine learning of physical conditions based on abstract relations and sparse labels Download PDF

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Publication number
CA2992297C
CA2992297C CA2992297A CA2992297A CA2992297C CA 2992297 C CA2992297 C CA 2992297C CA 2992297 A CA2992297 A CA 2992297A CA 2992297 A CA2992297 A CA 2992297A CA 2992297 C CA2992297 C CA 2992297C
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feature vectors
signal data
processors
classification
clusters
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French (fr)
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CA2992297A1 (en
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Mohammad H. Firooz
Nikunj R. Mehta
Greg Olsen
Peter Nicholas PRITCHARD
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Falkonry Inc
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Falkonry Inc
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0283—Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0205—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric not using a model or a simulator of the controlled system
    • G05B13/0255—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric not using a model or a simulator of the controlled system the criterion being a time-optimal performance criterion
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0205—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric not using a model or a simulator of the controlled system
    • G05B13/026—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric not using a model or a simulator of the controlled system using a predictor
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0275—Fault isolation and identification, e.g. classify fault; estimate cause or root of failure
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
    • G06F3/0484—Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/20—Ensemble learning

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  • Engineering & Computer Science (AREA)
  • Software Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Medical Informatics (AREA)
  • General Engineering & Computer Science (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Data Mining & Analysis (AREA)
  • Automation & Control Theory (AREA)
  • Health & Medical Sciences (AREA)
  • Computational Linguistics (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
  • Testing And Monitoring For Control Systems (AREA)
CA2992297A 2015-07-16 2016-07-15 Machine learning of physical conditions based on abstract relations and sparse labels Active CA2992297C (en)

Applications Claiming Priority (5)

Application Number Priority Date Filing Date Title
US201562193449P 2015-07-16 2015-07-16
US62/193,449 2015-07-16
US15/195,873 2016-06-28
US15/195,873 US10552762B2 (en) 2015-07-16 2016-06-28 Machine learning of physical conditions based on abstract relations and sparse labels
PCT/US2016/042465 WO2017011734A1 (en) 2015-07-16 2016-07-15 Machine learning of physical conditions based on abstract relations and sparse labels

Publications (2)

Publication Number Publication Date
CA2992297A1 CA2992297A1 (en) 2017-01-19
CA2992297C true CA2992297C (en) 2021-06-29

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CA2992297A Active CA2992297C (en) 2015-07-16 2016-07-15 Machine learning of physical conditions based on abstract relations and sparse labels

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US (1) US10552762B2 (https=)
EP (1) EP3323052A4 (https=)
JP (1) JP6732005B2 (https=)
CA (1) CA2992297C (https=)
WO (1) WO2017011734A1 (https=)

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JP6648078B2 (ja) * 2017-08-03 2020-02-14 株式会社ｔｏｏｒ 状態判定装置、状態判定方法及び状態判定プログラム
JP6826021B2 (ja) * 2017-11-20 2021-02-03 株式会社日立製作所 ストレージシステム
US11074272B1 (en) 2017-12-21 2021-07-27 Seeq Corporation System and method for managing streaming calculations
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US11972178B2 (en) * 2018-02-27 2024-04-30 Falkonry Inc. System and method for explanation of condition predictions in complex systems
JP7081953B2 (ja) * 2018-03-28 2022-06-07 株式会社日立システムズ アラート通知装置およびアラート通知方法
WO2020011648A1 (en) * 2018-07-13 2020-01-16 Asml Netherlands B.V. Pattern grouping method based on machine learning
US10635984B2 (en) * 2018-07-23 2020-04-28 Falkonry Inc. System and method for the assessment of condition in complex operational systems based on multi-level pattern recognition
CN112513892B (zh) * 2018-07-31 2024-06-25 三菱电机株式会社 信息处理装置、计算机可读的记录介质及信息处理方法
AU2019375200B2 (en) 2018-11-09 2024-10-03 Augury Systems Ltd. Automated analysis of non-stationary machine performance
KR102920247B1 (ko) * 2018-12-12 2026-01-30 제네럴 일렉트릭 컴퍼니 하이브리드 발전소
CN109974835B (zh) * 2018-12-29 2021-06-04 无锡联河光子技术有限公司 一种基于光纤信号特征的振动检测识别和时空定位方法和系统
US11821973B2 (en) * 2019-05-22 2023-11-21 Raytheon Company Towed array superposition tracker
JP6641056B1 (ja) * 2019-07-16 2020-02-05 株式会社東芝 機器の異常診断方法および機器の異常診断システム
CN110795703B (zh) * 2019-09-20 2024-04-16 华为技术有限公司 数据防窃取方法和相关产品
WO2021064781A1 (ja) * 2019-09-30 2021-04-08 三菱電機株式会社 情報処理装置、プログラム及び情報処理方法
WO2021079472A1 (ja) * 2019-10-24 2021-04-29 富士通株式会社 異常検出方法、異常検出プログラム及び異常検出装置
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Also Published As

Publication number Publication date
US20170017901A1 (en) 2017-01-19
JP6732005B2 (ja) 2020-07-29
US10552762B2 (en) 2020-02-04
JP2018533096A (ja) 2018-11-08
EP3323052A4 (en) 2018-10-10
CA2992297A1 (en) 2017-01-19
EP3323052A1 (en) 2018-05-23
WO2017011734A1 (en) 2017-01-19

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