WO2016204818A1 - Cross-domain time series data conversion apparatus, methods, and systems - Google Patents

Cross-domain time series data conversion apparatus, methods, and systems Download PDF

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Publication number
WO2016204818A1
WO2016204818A1 PCT/US2016/014441 US2016014441W WO2016204818A1 WO 2016204818 A1 WO2016204818 A1 WO 2016204818A1 US 2016014441 W US2016014441 W US 2016014441W WO 2016204818 A1 WO2016204818 A1 WO 2016204818A1
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Prior art keywords
data
type
time series
distributed representation
different
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Ceased
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English (en)
French (fr)
Inventor
Justin B. CLAYTON
Diasuke OKANOHARA
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Preferred Networks Inc
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Preferred Networks Inc
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Priority to JP2017565763A priority Critical patent/JP6676077B2/ja
Priority to EP16812063.2A priority patent/EP3311310A4/en
Publication of WO2016204818A1 publication Critical patent/WO2016204818A1/en
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G06N3/0455—Auto-encoder networks; Encoder-decoder networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/09—Supervised learning

Definitions

  • the data type C can be directly converted to or from any other data type in the data type conversion graph 650.
  • data type F 612 cannot be directly converted to data type D 608.
  • there are two sequences of two data type conversions e.g., F to C and C to D, or F to E and E to D.
  • Accelerometers that detect vibration in rotating components can provide early detection of potential problems with the equipment and enable the facility operator to take preventative action of repairing or shutting down the piece of equipment before failure occurs.
  • Vibration time series data is generally collected from accelerometers that are permanently mounted near rotating components.
  • a single rotating component may be monitored by multiple accelerometers.
  • a common technique involves monitoring a rotating component with three accelerometers, one each to measure vibration along the vertical (X), tangential (Y), and radial (Z) axes.
  • X vertical
  • Y tangential
  • Z radial
  • data type C 606, data type E 610, and data type F 612 may be fused to create a data type G.
  • an already fused data type e.g., data type XY 706
  • another non-fused data type e.g., data type U
  • another already fused data type e.g., data type UVW
  • Embodiment 7 The method of Embodiment 1, wherein the first distributed representation has a reduced dimensionality compared with the first time series of the first type of data.
  • Embodiment 14 The method of Embodiment 1, wherein the analysis model is arranged to determine that the steam trap is faulty using the second time series of the second type of data.
  • Embodiment 18 The method of Embodiment 17, further comprising transmitting at least one of the first distributed representation, the second distributed representation, the second time series, the third distributed representation, and the third time series from a first device to a second device.
  • An apparatus comprising: a data collection device configured to collect a first type of data over a period of time, the first type of data being indicative of a first parameter; a memory configured to store time series data collected by the data collection device; an encoder configured to convert the first time series of the first type of data into a first distributed representation for the first type of data; a data type converter configured to convert the first distributed representation into a second distributed representation for a second type of data, the second type of data being indicative of a second parameter which is different from the first parameter; an analyzer configured to receive an input that is based on the second distributed representation, and to run an analysis model on the input to detect or predict an event based on behavior of the second parameter; and an output module configured to transmit an output from the analysis model that corresponds to an event detected or predicted by the analysis model, wherein behavior of the first parameter and behavior of the second parameter provide information about a common phenomenon, and wherein behavior of the first parameter is incompatible with the analysis model.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Computing Systems (AREA)
  • Software Systems (AREA)
  • Artificial Intelligence (AREA)
  • Mathematical Physics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Computational Linguistics (AREA)
  • Molecular Biology (AREA)
  • General Health & Medical Sciences (AREA)
  • Biophysics (AREA)
  • Biomedical Technology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Computational Mathematics (AREA)
  • Algebra (AREA)
  • Probability & Statistics with Applications (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Image Analysis (AREA)
  • Radiation Pyrometers (AREA)
  • Measurement Of Velocity Or Position Using Acoustic Or Ultrasonic Waves (AREA)
  • Testing And Monitoring For Control Systems (AREA)
  • Investigating Or Analyzing Materials Using Thermal Means (AREA)
PCT/US2016/014441 2015-06-19 2016-01-22 Cross-domain time series data conversion apparatus, methods, and systems Ceased WO2016204818A1 (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
JP2017565763A JP6676077B2 (ja) 2015-06-19 2016-01-22 クロスドメイン時系列データ変換装置、方法、およびシステム
EP16812063.2A EP3311310A4 (en) 2015-06-19 2016-01-22 DEVICE, METHOD AND SYSTEMS FOR DOMAIN-RELATED TIME-DATA-DATA-CONVERSION

Applications Claiming Priority (2)

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US14/744,475 US10460251B2 (en) 2015-06-19 2015-06-19 Cross-domain time series data conversion apparatus, methods, and systems
US14/744,475 2015-06-19

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WO2016204818A1 true WO2016204818A1 (en) 2016-12-22

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EP (1) EP3311310A4 (enExample)
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WO (1) WO2016204818A1 (enExample)

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JP2018524711A (ja) 2018-08-30
US20200019877A1 (en) 2020-01-16
EP3311310A1 (en) 2018-04-25
JP6942833B2 (ja) 2021-09-29
JP2021192300A (ja) 2021-12-16
US20160371316A1 (en) 2016-12-22
EP3311310A4 (en) 2019-02-13
JP7253596B2 (ja) 2023-04-06
US10460251B2 (en) 2019-10-29
JP2020098646A (ja) 2020-06-25
US12106232B2 (en) 2024-10-01
JP6676077B2 (ja) 2020-04-08

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