CA2634328A1 - Method and system for trend detection and analysis - Google Patents

Method and system for trend detection and analysis Download PDF

Info

Publication number
CA2634328A1
CA2634328A1 CA002634328A CA2634328A CA2634328A1 CA 2634328 A1 CA2634328 A1 CA 2634328A1 CA 002634328 A CA002634328 A CA 002634328A CA 2634328 A CA2634328 A CA 2634328A CA 2634328 A1 CA2634328 A1 CA 2634328A1
Authority
CA
Canada
Prior art keywords
data
data set
baseline
points
calculating
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CA002634328A
Other languages
French (fr)
Other versions
CA2634328C (en
Inventor
Phuc Luong Nguyen
Denis Collin
Peter Matschek
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Pratt and Whitney Canada Corp
Original Assignee
Individual
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Individual filed Critical Individual
Priority to CA2634328A priority Critical patent/CA2634328C/en
Publication of CA2634328A1 publication Critical patent/CA2634328A1/en
Application granted granted Critical
Publication of CA2634328C publication Critical patent/CA2634328C/en
Anticipated expiration legal-status Critical
Expired - Lifetime legal-status Critical Current

Links

Classifications

    • 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
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0221—Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/02—Preprocessing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/12—Classification; Matching

Abstract

A method for comparing a data set to a baseline value comprising: providing the data set to be analyzed; locating potentially bad data points in at least a portion of the data set using an odd-man out recursive technique; preparing a baseline set by discarding the potentially bad data points from the at least a portion of the data set; and calculating a baseline value from the baseline set. A method for smoothing a data set comprising: providing the data set to be analyzed;
determining a baseline value from at least a portion of the data set;
smoothing the data set to diminish an effect of extraneous data points and obtain smoothed data, wherein the smoothing includes using a low-point weighted average and regression technique. A method for removing very low frequency noise from a time-based data set comprising- providing the time-based data set to be analyzed; determining a baseline value from at least a portion of the data set;
smoothing the data set to diminish an effect of extraneous data points and obtain smoothed data, wherein the smoothing includes using a Fast Fourier Transform (FFT) algorithm to transfer the time-based data set into a frequency based data set; attenuating low frequencies of the frequency-based data set; and using an inverse Fourier Transform algorithm to transfer the attenuated frequency based data set into an attenuated time-based data set. Combinations of these methods are also disclosed Systems to carry out these methods are also disclosed.

Claims (18)

WHAT IS CLAIMED IS:
1. A method for comparing a data set to a baseline value for use in data analysis of said data set having a plurality of data points, comprising:

providing said data set to be analyzed;

locating potentially bad data points in at least a portion of said data set using an odd-man out recursive technique, preparing a baseline set by discarding said potentially bad data points from said at least a portion of said data set, and calculating a baseline value from said baseline set.
2. A method as claimed in claim 1, further comprising ordering said data points in chronological order prior to said locating
3. A method as claimed in claim 2, wherein said at least a portion has data points from an earliest time value
4 A method as claimed in claim 3, wherein said at least a portion has 15 data points
A method as claimed in any one of claims 1 to 4, wherein said locating comprises performing a linear regression on all points of at least a portion of said data set except a first point considered, calculating a standard deviation for all points of said at least a portion of said data set except said first point considered, repeating said steps of performing and calculating a standard deviation until said linear regression and said standard deviation are calculated for each point considered, wherein said discarding comprises discarding data points with a lowest standard deviation; and wherein said calculating a baseline value from said baseline set comprises calculating an intercept of said baseline set to determine said baseline value
6. A method as claimed in claim 5, further comprising performing a second linear regression on said baseline set prior to said calculating said intercept.
7. A method as claimed in any one of claims 1 to 6, further comprising, after said step of providing said data set :

providing predicted values corresponding to said data set, calculating a difference between each of said data points of said data set and said predicted values to obtain difference data, wherein said step of locating and preparing are carried out using said difference data
8. A method as claimed in claim 7, wherein said predicted values are obtained from a set of equations representing a model for said data set.
9. A method as claimed in any one of claims 1 to 8, further comprising determining a relative change in said baseline value based on said discarding and determining a target number of data points to discard based on a significance of said relative change.
A system for comparing a data set to a baseline value for use in data analysis of said data set having a plurality of data points, comprising:

a data provider for providing said data set to be analyzed, an odd-man out locator for locating potentially bad data points in at least a portion of said data-set using an odd-man out recursive technique, a data discarder for preparing a baseline set by discarding said potentially bad data points from said at least a portion of said data set; and a baseline calculator for calculating a baseline value from said baseline set.
11. A system as claimed in claim 10, further comprising a data sorter for ordering said data points in chronological order prior to said locating.
12 A system as claimed in claim 11, wherein said at least a portion has data points from an earliest time value
13. A system as claimed in claim 12, wherein said at least a portion has 15 data points.
14. A system as claimed in any one of claims 10 to 13, wherein said odd-man out locator comprises a linear regression calculator for performing a linear regression on all points of at least a portion of said data set except a first point considered, calculating a standard deviation for all points of said at least a portion of said data set except said first point considered, repeating said steps of performing and calculating a standard deviation until said linear regression and said standard deviation are calculated for each point considered;

wherein said data discarder discards data points with a lowest standard deviation; and wherein said baseline calculator calculates an intercept of said baseline set to determine said baseline value
15. A system as claimed in claim 14, further comprising a second linear regression calculator for performing a second linear regression on said baseline set prior to said calculating said intercept.
16. A system as claimed in any one of claims 10 to 15, further comprising, a comparison provider for providing predicted values corresponding to said data set, a difference calculator for calculating a difference between each of said data points of said data set and said predicted values to obtain difference data, wherein said difference data is provided to said odd-man out locator for said locating.
17. A system as claimed in claim 16, wherein said comparison provider obtains said predicted values from a set of equations representing a model for said data set
18. A system as claimed in any one of claims 10 to 17, further comprising a change determiner for determining a relative change in said baseline value based on said discarding and determining a target number of data points to discard based on a significance of said relative change.
CA2634328A 2003-01-24 2003-01-24 Method and system for trend detection and analysis Expired - Lifetime CA2634328C (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CA2634328A CA2634328C (en) 2003-01-24 2003-01-24 Method and system for trend detection and analysis

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CA2634328A CA2634328C (en) 2003-01-24 2003-01-24 Method and system for trend detection and analysis
CA002417074A CA2417074C (en) 2003-01-24 2003-01-24 Method and system for trend detection and analysis

Related Parent Applications (1)

Application Number Title Priority Date Filing Date
CA002417074A Division CA2417074C (en) 2003-01-24 2003-01-24 Method and system for trend detection and analysis

Publications (2)

Publication Number Publication Date
CA2634328A1 true CA2634328A1 (en) 2004-07-24
CA2634328C CA2634328C (en) 2011-08-16

Family

ID=32686737

Family Applications (3)

Application Number Title Priority Date Filing Date
CA2634470A Expired - Lifetime CA2634470C (en) 2003-01-24 2003-01-24 Method and system for trend detection and analysis
CA002417074A Expired - Lifetime CA2417074C (en) 2003-01-24 2003-01-24 Method and system for trend detection and analysis
CA2634328A Expired - Lifetime CA2634328C (en) 2003-01-24 2003-01-24 Method and system for trend detection and analysis

Family Applications Before (2)

Application Number Title Priority Date Filing Date
CA2634470A Expired - Lifetime CA2634470C (en) 2003-01-24 2003-01-24 Method and system for trend detection and analysis
CA002417074A Expired - Lifetime CA2417074C (en) 2003-01-24 2003-01-24 Method and system for trend detection and analysis

Country Status (2)

Country Link
US (3) US7039554B2 (en)
CA (3) CA2634470C (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10876391B2 (en) 2015-08-27 2020-12-29 Halliburton Energy Services, Inc. Tuning predictions of wellbore operation parameters
US11053792B2 (en) 2015-08-27 2021-07-06 Halliburton Energy Services, Inc. Predicting wellbore operation parameters
US11085273B2 (en) 2015-08-27 2021-08-10 Halliburton Energy Services, Inc. Determining sources of erroneous downhole predictions

Families Citing this family (57)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7428478B2 (en) * 2001-08-17 2008-09-23 General Electric Company System and method for improving accuracy of baseline models
US7403877B2 (en) * 2001-08-17 2008-07-22 General Electric Company System, method and computer product for baseline modeling a product or process
AU2003288885A1 (en) * 2003-12-31 2005-07-21 Systems On Silicon Manufacturing Co. Pte. Ltd. System and method for process degradation and problematic tool identification
US7139674B2 (en) * 2004-05-18 2006-11-21 The Boeing Company Systems and methods for filtering and smoothing data
US7415328B2 (en) * 2004-10-04 2008-08-19 United Technologies Corporation Hybrid model based fault detection and isolation system
GR1005241B (en) * 2005-05-18 2006-06-19 Kintec �������� ������� �������� �������� & ����������� ���������� ������ ����������� ��� �������� ���������� ������� �� System for information collection, breakdown detection, monitoring, transmission, and activation of cellular telephony telemetry signals
US7398171B2 (en) * 2005-06-30 2008-07-08 Applera Corporation Automated quality control method and system for genetic analysis
DE102005041556A1 (en) * 2005-09-01 2007-03-08 Continental Teves Ag & Co. Ohg Method for determining an admission pressure in a motor vehicle brake system
US8620623B2 (en) * 2005-11-14 2013-12-31 Globaltrak, Llc Hierarchical and distributed information processing architecture for a container security system
US20070150949A1 (en) * 2005-12-28 2007-06-28 At&T Corp. Anomaly detection methods for a computer network
US7808916B1 (en) * 2005-12-28 2010-10-05 At&T Intellectual Property Ii, L.P. Anomaly detection systems for a computer network
EP1818746A1 (en) * 2006-02-10 2007-08-15 ALSTOM Technology Ltd Method of condition monitoring
US7593833B2 (en) * 2006-03-03 2009-09-22 At&T Intellectual Property I, L.P. System and method for determining performance of network lines
US20080021661A1 (en) * 2006-07-24 2008-01-24 Andrew Thompson Field adjustment for engine monitoring system
US20080064938A1 (en) * 2006-09-08 2008-03-13 Semler John R Method of determining location of an ingested capsule
US20080126012A1 (en) * 2006-11-29 2008-05-29 United Technologies Corpoation State initialization for gas turbine engine performance diagnostics
US20080140345A1 (en) * 2006-12-07 2008-06-12 International Business Machines Corporation Statistical summarization of event data
US8682835B1 (en) 2006-12-15 2014-03-25 Intellectual Assets Llc Asset surveillance method and system comprising a dynamic model framework
US20080154473A1 (en) * 2006-12-22 2008-06-26 United Technologies Corporation Gas turbine engine performance data validation
DE102007032950A1 (en) * 2007-07-14 2009-01-15 Continental Teves Ag & Co. Ohg Method for measuring the admission pressure on an analogized, electromagnetically controlled hydraulic valve
US20090125554A1 (en) * 2007-11-14 2009-05-14 Orient George E Statistical process control resource tracker
US8700550B1 (en) 2007-11-30 2014-04-15 Intellectual Assets Llc Adaptive model training system and method
US8712929B1 (en) 2007-11-30 2014-04-29 Intellectual Assets Llc Dynamic data filtering system and method
US8145444B1 (en) 2007-11-30 2012-03-27 Intellectual Assets Llc Asset surveillance system and method comprising self-calibrating fault detection
US20090178278A1 (en) * 2008-01-16 2009-07-16 Quinn Daniel E Method of reverse engineering
US20090201287A1 (en) * 2008-02-07 2009-08-13 Honeywell International Inc. System and method of adaptively filtering parametric data for display
US9189102B2 (en) * 2008-05-16 2015-11-17 8631654 Canada Inc. Data filtering method and electronic device using the same
US8068997B2 (en) * 2009-02-06 2011-11-29 Honeywell International Inc. Continuous performance analysis system and method
EP2226766A3 (en) * 2009-03-02 2014-06-11 Sikorsky Aircraft Corporation Rotor system health monitoring using shaft load measurements and virtual monitoring of loads
US8712729B2 (en) * 2009-11-17 2014-04-29 United Technologies Corporation Anomalous data detection method
US8996334B2 (en) 2011-03-02 2015-03-31 General Electric Company Method and system for analysis of turbomachinery
CN102320382A (en) * 2011-07-07 2012-01-18 中国国际航空股份有限公司 Aircraft performance detection method
FR2979013B1 (en) * 2011-08-08 2015-07-03 Snecma AIRCRAFT ENGINE TEST BENCH MONITORING SYSTEM
US8832267B2 (en) * 2012-08-07 2014-09-09 Ca, Inc. System and method for adaptive baseline calculation
US10372779B2 (en) * 2012-09-13 2019-08-06 Lenovo Enterprise Solutions (Singapore) Pte. Ltd. Frequent content continuity visual assistance in content browsing
US10712174B2 (en) * 2012-10-10 2020-07-14 Honeywell International Inc. Filter activation and deactivation based on comparative rates
US9221535B2 (en) * 2013-09-18 2015-12-29 Honeywell International Inc. Adaptive remaining useful life balancing control system and method for multi-engine systems
US10114823B2 (en) * 2013-11-04 2018-10-30 Ayasdi, Inc. Systems and methods for metric data smoothing
EP2905666A1 (en) * 2014-02-07 2015-08-12 Siemens Aktiengesellschaft Estimation of health parameters in industrial gas turbines
US10443509B2 (en) * 2014-10-31 2019-10-15 General Electric Company System and method for turbomachinery vane prognostics and diagnostics
US10387412B1 (en) * 2015-02-12 2019-08-20 Cloud & Stream Gears Llc Incremental Z-score calculation for big data or streamed data using components
US11043987B2 (en) * 2016-03-30 2021-06-22 British Telecommunications Public Limited Company Estimating the electrical line length of a digital subscriber line
US11327797B2 (en) 2016-05-09 2022-05-10 Oracle International Corporation Memory usage determination techniques
US10307785B2 (en) 2016-05-24 2019-06-04 Rpm Wood Finishes Group, Inc. Heat pump dispenser
DE112016006822T5 (en) * 2016-06-30 2019-01-10 Intel Corporation SENSOR-BASED RECORDING AND DEVICE
TWI623905B (en) * 2016-11-23 2018-05-11 財團法人工業技術研究院 Method and system for detecting parameters effecting trend of continuous process
CN108090241B (en) 2016-11-23 2021-08-17 财团法人工业技术研究院 Trend variable identification method and system of continuous process
US10519964B2 (en) 2016-12-06 2019-12-31 General Electric Company System and method for turbomachinery rotor and blade prognostics and diagnostics
US10125779B2 (en) 2016-12-06 2018-11-13 General Electric Company System and method for turbomachinery vane diagnostics
DE102016125803A1 (en) * 2016-12-28 2018-06-28 Fritz Studer Ag Machine tool, in particular grinding machine, and method for determining an actual state of a machine tool
US10703503B2 (en) * 2017-05-03 2020-07-07 General Electric Company Method of displaying data in an aircraft
WO2019109338A1 (en) * 2017-12-08 2019-06-13 Nokia Shanghai Bell Co., Ltd Methods and systems for generation and adaptation of network baselines
US10822993B2 (en) * 2018-06-06 2020-11-03 General Electric Company Method for operating a turbo machine
US20200004619A1 (en) * 2018-06-28 2020-01-02 Honeywell International Inc. System and method for detecting a shift in real data trend using the configurable adaptive threshold
WO2020027861A1 (en) * 2018-08-02 2020-02-06 Landmark Graphics Corporation Distributed control system using asynchronous services in a wellbore
CN112800823B (en) * 2021-04-19 2024-04-02 上海普适导航科技股份有限公司 Water body rapid extraction method
CN113514148B (en) * 2021-07-09 2023-06-27 西安因联信息科技有限公司 Equipment vibration early warning method and system based on low-delay data smoothing

Family Cites Families (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4634110A (en) * 1983-07-28 1987-01-06 Harris Corporation Fault detection and redundancy management system
DE69118486T2 (en) * 1990-06-13 1996-12-19 Sabine Musical Mfg Co Inc METHOD AND DEVICE FOR ADAPTIVELY FILTERING AN AUDIORESONANCE FREQUENCY
US5268938A (en) * 1992-01-21 1993-12-07 International Business Machines Corporation Redundancy scheme for Fourier transform coding on peak limited channels
US5517426A (en) * 1992-10-29 1996-05-14 Underwood; Marcos A. Apparatus and method for adaptive closed loop control of shock testing system
EP0770967A3 (en) * 1995-10-26 1998-12-30 Koninklijke Philips Electronics N.V. Decision support system for the management of an agile supply chain
US6027941A (en) * 1996-05-15 2000-02-22 Curagen Corporation Method for distance measurements with solid-state NMR
US6513386B2 (en) * 1999-10-22 2003-02-04 Skf Condition Monitoring Digital vibration coupling stud
AU4733601A (en) * 2000-03-10 2001-09-24 Cyrano Sciences Inc Control for an industrial process using one or more multidimensional variables
US6754569B2 (en) * 2001-05-24 2004-06-22 Simmonds Precision Products, Inc. Method and apparatus for normalizing condition indicators
US6606693B1 (en) * 2001-09-25 2003-08-12 Alan Morris Method and system for long-term digital data storage
US6829538B2 (en) * 2002-04-23 2004-12-07 Westerngeco, L.L.C. Method for suppressing systematic amplitude variations
US7346013B2 (en) * 2002-07-18 2008-03-18 Coherent Logix, Incorporated Frequency domain equalization of communication signals
US7027953B2 (en) * 2002-12-30 2006-04-11 Rsl Electronics Ltd. Method and system for diagnostics and prognostics of a mechanical system

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10876391B2 (en) 2015-08-27 2020-12-29 Halliburton Energy Services, Inc. Tuning predictions of wellbore operation parameters
US11053792B2 (en) 2015-08-27 2021-07-06 Halliburton Energy Services, Inc. Predicting wellbore operation parameters
US11085273B2 (en) 2015-08-27 2021-08-10 Halliburton Energy Services, Inc. Determining sources of erroneous downhole predictions

Also Published As

Publication number Publication date
CA2634470A1 (en) 2004-07-24
US7321845B2 (en) 2008-01-22
CA2634470C (en) 2013-05-14
US20050209823A1 (en) 2005-09-22
US7216063B2 (en) 2007-05-08
CA2417074A1 (en) 2004-07-24
US20040148139A1 (en) 2004-07-29
CA2634328C (en) 2011-08-16
US20050209821A1 (en) 2005-09-22
US7039554B2 (en) 2006-05-02
CA2417074C (en) 2009-07-21

Similar Documents

Publication Publication Date Title
CA2634328A1 (en) Method and system for trend detection and analysis
CN108845250B (en) On-load tap-changer fault identification method based on vibration signal feature extraction
CN107948930B (en) Indoor positioning optimization method based on position fingerprint algorithm
KR101829560B1 (en) Method, system and computer device for predicting a capacity based on kalman filter
CN106850511B (en) Method and device for identifying access attack
CN113537102B (en) Feature extraction method of microseismic signals
CN107123432A (en) A kind of Self Matching Top N audio events recognize channel self-adapted method
CN112034434A (en) Radar radiation source identification method based on sparse time-frequency detection convolutional neural network
CN109214318B (en) Method for searching weak peak of unsteady time sequence
CN112180315A (en) Fault feature extraction method, device and system for optical fiber current transformer
CN108847828A (en) A kind of non-linear event triggering filtering method with stochastic modeling error
CN114154546A (en) Noise reduction method for steel production process data
CN114842867A (en) DFT-based audio sinusoidal signal frequency estimation method and system
CN110190917B (en) Frequency spectrum hole sensing method, device and equipment for LTE230MHz power wireless private network
CN111901268A (en) BPSK/QPSK signal modulation identification method based on frequency spectrum rearrangement and Gumbel distribution fitting test
CN109117698B (en) Noise background estimation method based on minimum mean square error criterion
CN116028790A (en) Quick identification and screening method for ship mechanical vibration characteristic line spectrum
CN106057211A (en) Signal matching method and device
CN113297195B (en) Time series abnormity detection method, device and equipment
CN115442188A (en) Channel estimation method, device, equipment and storage medium
CN110376436B (en) Multi-scale noise power spectral line spectrum detection method
CN110248325B (en) Bluetooth indoor positioning system based on signal multiple noise elimination
Li et al. Two-branch wavelet denoising for accurate spectrum sensing in cognitive radios
CN111858699A (en) Time series correlation detection method, equipment and storage medium
CN110110341B (en) Pulse detection method for automatic recommendation of decision threshold

Legal Events

Date Code Title Description
EEER Examination request
MKEX Expiry

Effective date: 20230124