WO2018083147A1 - Verfahren zur diagnose eines technischen systems - Google Patents
Verfahren zur diagnose eines technischen systems Download PDFInfo
- Publication number
- WO2018083147A1 WO2018083147A1 PCT/EP2017/078007 EP2017078007W WO2018083147A1 WO 2018083147 A1 WO2018083147 A1 WO 2018083147A1 EP 2017078007 W EP2017078007 W EP 2017078007W WO 2018083147 A1 WO2018083147 A1 WO 2018083147A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- kernel
- volterra
- order
- technical system
- state
- Prior art date
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Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
-
- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M8/00—Fuel cells; Manufacture thereof
- H01M8/04—Auxiliary arrangements, e.g. for control of pressure or for circulation of fluids
-
- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M8/00—Fuel cells; Manufacture thereof
- H01M8/04—Auxiliary arrangements, e.g. for control of pressure or for circulation of fluids
- H01M8/04298—Processes for controlling fuel cells or fuel cell systems
- H01M8/04313—Processes for controlling fuel cells or fuel cell systems characterised by the detection or assessment of variables; characterised by the detection or assessment of failure or abnormal function
- H01M8/04664—Failure or abnormal function
- H01M8/04671—Failure or abnormal function of the individual fuel cell
-
- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M8/00—Fuel cells; Manufacture thereof
- H01M8/04—Auxiliary arrangements, e.g. for control of pressure or for circulation of fluids
- H01M8/04298—Processes for controlling fuel cells or fuel cell systems
- H01M8/04992—Processes for controlling fuel cells or fuel cell systems characterised by the implementation of mathematical or computational algorithms, e.g. feedback control loops, fuzzy logic, neural networks or artificial intelligence
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/30—Hydrogen technology
- Y02E60/50—Fuel cells
Definitions
- the subject invention relates to a method for the diagnosis of a technical system, which images an input signal to an output signal, wherein the input signal is superimposed during operation of the technical system, a start signal with at least one exciting frequency and for diagnosis, the input signal and / or the output signal is analyzed to detect a fault condition of the technical system.
- electrochemical impedance spectroscopy allows to detect the dynamic behavior of the galvanic element.
- a small alternating current or a small alternating voltage of known amplitude and exciting frequency (also several exciting frequencies) is impressed on the galvanic element and the response (in amplitude and phase) is measured and evaluated as a function of the exciting frequency.
- the evaluation is carried out by analyzing the fundamental wave or the fundamental waves at several excitation frequencies.
- Electrochemical impedance spectroscopy can also diagnose various influences on the current operating state and the method can also be used in real operation of the galvanic element.
- THDA Total Harmony Distortion Analysis
- electrochemical impedance spectroscopy as well as THDA are based on the fact that the excitation frequency is known in advance, with which a certain state of the galvanic element can be excited in the best possible way in order to enable a meaningful diagnosis.
- the "healthy", ie error - free, galvanic element provides a different response to a start signal than a galvanic element with a faulty state
- the excitation signal in particular the excitation frequency, should excite this fault state particularly well in order to be able to diagnose the faulty state
- these optimum exciter frequencies are not known in advance for a specific galvanic element, but rather have to be laboriously determined empirically.
- a galvanic element is for example a battery, an accumulator or a fuel cell.
- a fuel cell may include, for example, an alkaline fuel cell (AFC), a polymer electrolyte fuel cell (PEMFC), a direct methanol fuel cell (DMFC), a phosphoric acid fuel cell (PAFC), a molten carbonate fuel cell (MCFC) or a solid oxide fuel cell (SOFC ) be.
- An electrolyzer can be, for example, a polymer electrolyte electrolyzer, a solid oxide electrolyzer or an alkaline electrolyzer. It is therefore an object of the subject invention to determine an excitation frequency for a diagnostic method of a technical system based on the excitation of the technical system with a start signal with the exciting frequency in a simpler manner.
- the technical system is modeled as Volterra series with Volterra kernel, in the error-free state of the technical system a Volterra kernel of the nth order for the error-free state is determined for a defined error state of the technical system, an nth-order Volterra kernel for the error-prone state is determined from the nth-order Volterra kernel for the error-free state and the nth-order Volterra kernel for the error-prone state, a difference kernel nth Order is formed and the nth-order difference kernel is evaluated to determine a frequency range in which the gain of the difference kernel exceeds a predetermined limit and the excitation frequency for the start signal is selected from this frequency range.
- This approach allows a systematic, simple determination of the optimum for a certain error state excitation frequencies of the technical system.
- the exciting frequency for the exciting signal is selected at which the gain of the nth-order differential kernel becomes a maximum value.
- a maximum value search can also be easily automated in order to determine the pickup frequencies completely automatically.
- a parametric model such as a non-linear, polynomial NARMAX or NARX model, from which the Volterra kernels can be derived analytically.
- the parametric model is estimated in the time domain from known data. From the parametric model, the Volterra kernels are preferably derived analytically using Harmony Probing Algorithm.
- FIG. 4 shows a fuel cell as an example of a technical system to be diagnosed
- 5 shows an exemplary APRBS input signal
- the invention is based on the modeling of the non-linear transmission behavior of a technical system, for example a galvanic element or an electrolyzer.
- the transmission behavior is known to be the response (output signal y (t)) of the technical system to a specific excitation (input signal u (t)).
- the input signal u (t) is, for example, an electric current I
- the output signal y (t) is a self-adjusting electrical voltage U or vice versa.
- a technical system often has a (highly) non-linear input / output behavior.
- the advantage of using a Volterra series is that the Volterra series can be easily transformed into the frequency domain using the multidimensional Fourier transformation.
- the transmission behavior in the frequency domain is given by the transfer functions H n (joo) and results from the multidimensional Fourier transformation
- a parametric model is a model that determines the current system output from the parameter-weighted past inputs and outputs.
- a data point is the output quantity y (N) for a specific input quantity u (N), and, if appropriate, disturbance e (N).
- a data point can be measured on the technical system, or can be known. This representation is linear in the parameters ⁇ , whereby the polynomial
- a better model quality can be achieved by decomposing the regression matrix P into an orthogonal matrix W and a triangular matrix A using a known orthogonalization method, for example the Gram-Schmidt orthogonalization method.
- the matrices W and A result from the respective Orthogonalmaschinesvon.
- Such a transformation into orthogonal space is advantageous because a parametric model such as the polynomial NARMAX or NARX model may contain a very large number of potential parameters,, many of which describe the input / output behavior of the technical system at all are not relevant. A solution of this overdetermined system of equations is often numerically difficult or even impossible, since the regression matrix P is very poorly conditioned.
- the transformation also allows an evaluation of which parameters ⁇ are important and which of the parameters ⁇ are not required (a so-called structure selection).
- the regression matrix P forms an orthogonal basis and the parameters g i, can be independently calculated by the above equation.
- the individual regressors can be assessed according to importance, for example, in that only regressors are used which have a predetermined error component ERR.
- the other regressors are set to zero.
- the Volterra kernels can be derived directly analytically.
- An example of this is the so-called Recursive Probing Algorithm (often referred to as Harmony Probing Algorithm) described in Chapter 6 of Billings. It is exploited that, given the Volterra series, see input (approach functions) knows the principal answer of the technical system (harmonics, intermodulation, etc.).
- the parametric model past outputs and inputs
- these known approach functions are now used.
- the only unknown in the system of equations are the Volterra kernels, which can be resolved, whereby the nth-order Volterra kernel depends on the Volterra kernel (n-1) -th order, etc.
- the Volterra Kernels are recursively determined.
- H n, dif f , co 2 , ..., coj f (H n fault ( ⁇ ,, ⁇ 2 , ..., ⁇ ⁇ ), ⁇ ⁇ ⁇ ( ⁇ ,, ⁇ 2 , ..., ⁇ ⁇ )), determined and evaluated. It may, for example, a review kernel n H, n-th order diff as the quotient of the fault-free kernel H n nom n-th order and the faulty kernel H n, f aU
- H 2 diff ( ⁇ 15 ⁇ 2 )
- t / H n, nom describes the relative change in case of an error and the difference H n , f aU i r H n, nom the absolute magnitude of the change.
- the two sizes can be evaluated individually as well as in combination.
- the evaluation kernel H n , diff can now be evaluated in such a way that the frequency ranges are searched in which a gain of the evaluation kernel H n , diff exists which exceeds a defined limit gain.
- the frequency is sought where the maximum gain results.
- the difference kernel of the 2nd order is particularly advantageous in this case since it can still be represented graphically simply as a 3D plot or as a contour plot of the gain, which simplifies the evaluation. This will be explained with reference to FIGS. 1 to 3 using the example of a second-order Volterra kernel and the quotient as the evaluation kernel H 2 , diff.
- 1 shows the gain of the Volterra kernel 2nd order H 2 , n om a technical system, such as a galvanic element, in error-free state.
- 2 shows the gain of the Volterra kernel 2nd order H 2 , fauit the same technical system in the faulty state, ie in a specific, clear error case. Shown is in each case the Volterra kernel 2nd order H 2 , n om as a two-dimensional contour plot of the gain as a function of the frequencies ⁇ - ⁇ , ⁇ 2 , which in the figures as in this context often applied as normalized with half the sampling frequency frequency is.
- the gain (as the amount of the complex function, ie the Volterra kernel) and the phase, as a function of the frequencies ( ⁇ - ⁇ , ⁇ 2 , ... , ⁇ ⁇ ), calculated and represented.
- the starting signal a (t) is not the normal operation of the technical system compared to the amplitudes of the input signal of low amplitude (eg A, Ai, A 2 less than 10%, preferably less than 5%, the expected maximum amplitude of the input signal) disturb.
- the ongoing diagnosis of the operation of the technical system can then be performed, for example, in a known manner continuously with the electrochemical impedance spectroscopy or the Total Harmonie Distortion Analysis.
- excitation frequencies oo mp , p> 1, in the excitation signal a (t) are included, then it is important that the excitation frequencies oo mp do not affect each other in the frequency spectrum, which can be easily ensured by appropriate selection. It is therefore important to ensure a clean separation of the resulting frequency spectra in order to be able to keep the different error states clearly separate.
- a non-linear, accurate time model (polynomial NARMAX or NARX model) is also obtained as a "waste product" of the method according to the invention the collective term Model-based Fault Diagnosis and isolation (FDI) are known.
- the identified time model can be continuously included in the online operation of the technical system and updated.
- the parametric model of the technical system e.g. A NARMAX or NARX model may be present, or may be identified in advance, as described below by way of an electrochemical element as a technical system 1.
- the exemplary technical system in the form of the galvanic element is shown in FIG.
- the galvanic element in the form of a fuel cell are electrical loads 2a, 2b, e.g. a hybrid powertrain or a vehicle battery connected. Between the fuel cell and the loads 2a, 2b may be arranged in a known manner, a power part 3, to regulate the flow of energy and the voltage and current levels.
- the galvanic element can also, in particular in the case of a fuel cell, connected to an unspecified, well-known, gas conditioning 4, which serves to condition the reaction gases for the fuel cell as needed, in particular concerning pressure, humidity, temperature, mass flow.
- gas conditioning e.g. for hydrogen, be provided.
- the gas conditioning is shown schematically and will not be described in detail.
- Faults in the gas conditioning can lead to faulty operating states in the galvanic element.
- a reaction gas may be too moist or too dry, the pressure of a reaction gas may be too high, the mass flow of a reaction gas may be too low, etc.
- damage in the galvanic element e.g. a damaged ion exchange membrane of a cell, or changes occur that can be recognized as fault conditions.
- Such fault conditions lead to suboptimal operation of the galvanic element s and can even lead to damage or destruction of the galvanic element. It is therefore important to continuously monitor the operating state of the galvanic element in order to be able to take appropriate countermeasures quickly in the event of a fault.
- the monitoring should be based on the current and voltage curve of the galvanic element, ie the input and output variables of the technical system 1, take place.
- the galvanic element is now led to a fault-free operating state, ie, for example, the gas conditioning works without errors and all the reaction gases are sufficiently and properly conditioned and that no other fault condition exists.
- the galvanic element an input signal u (t), here for example in the form of a temporal current l (t), impressed, which should excite the galvanic element as well as possible to capture the static and dynamic behavior of the galvanic element as well as possible can.
- a suitable input signal u (t) is, for example, a current profile l (t) in the form of an amplitude-modulated pseudo random binary sequence (APRBS) signal, as shown in FIG. 5, or a random Gaussian sequence signal.
- a PRBS amplitude-modulated pseudo random binary sequence
- the resulting output signal y (t), here for example in the form of the temporal voltage curve U (t), is shown in FIG displayed for a short period of time.
- the input signal u (t) and output signal y (t) are sampled at a predetermined sample rate, eg 100 Hz, from which the data points result and from which, as outlined above, a NARMAX or NARX model is identified.
- the identified NARX model results, for example, in the form
- y (k) -0.4453u (k - 2) + 0.0059y (k - 4) + 1.2348y (k - 1) + 0.5177u (k - 3) +
- This parametric model can then be analytically transformed into the frequency domain as outlined above, resulting in the Volterra kernels in the frequency domain. This is repeated, the galvanic element now being operated in a defined error state, for example with too low a relative humidity of a process gas.
- the at least one exciter frequency oo m is then identified from the evaluation kernel, with which the technical system 1 must be excited in order to optimally excite this fault state during operation of the technical system 1 and thus make it diagnosable.
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- Electrochemistry (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Chemical & Material Sciences (AREA)
- Sustainable Energy (AREA)
- Sustainable Development (AREA)
- Manufacturing & Machinery (AREA)
- Fuzzy Systems (AREA)
- Automation & Control Theory (AREA)
- Software Systems (AREA)
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- Physics & Mathematics (AREA)
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- Fuel Cell (AREA)
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Abstract
Description
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Priority Applications (5)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP2019522773A JP2019537004A (ja) | 2016-11-04 | 2017-11-02 | 技術システムを診断するための方法 |
DE112017005566.0T DE112017005566A5 (de) | 2016-11-04 | 2017-11-02 | Verfahren zur Diagnose eines technischen Sytems |
US16/347,140 US20200064406A1 (en) | 2016-11-04 | 2017-11-02 | Method for diagnosing a technical system |
KR1020197013032A KR20190077384A (ko) | 2016-11-04 | 2017-11-02 | 기술 시스템을 진단하기 위한 방법 |
CN201780071786.8A CN109983353A (zh) | 2016-11-04 | 2017-11-02 | 用于诊断技术系统的方法 |
Applications Claiming Priority (2)
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ATA51008/2016A AT519335B1 (de) | 2016-11-04 | 2016-11-04 | Verfahren zur Diagnose eines technischen Systems |
ATA51008/2016 | 2016-11-04 |
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WO2018083147A1 true WO2018083147A1 (de) | 2018-05-11 |
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PCT/EP2017/078007 WO2018083147A1 (de) | 2016-11-04 | 2017-11-02 | Verfahren zur diagnose eines technischen systems |
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US (1) | US20200064406A1 (de) |
JP (1) | JP2019537004A (de) |
KR (1) | KR20190077384A (de) |
CN (1) | CN109983353A (de) |
AT (1) | AT519335B1 (de) |
DE (1) | DE112017005566A5 (de) |
WO (1) | WO2018083147A1 (de) |
Families Citing this family (2)
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US10985951B2 (en) | 2019-03-15 | 2021-04-20 | The Research Foundation for the State University | Integrating Volterra series model and deep neural networks to equalize nonlinear power amplifiers |
CN115219902A (zh) * | 2022-07-06 | 2022-10-21 | 山东大学 | 一种动力电池寿命快速测试方法及系统 |
Citations (1)
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DE102005020318A1 (de) * | 2005-05-02 | 2006-11-16 | Infineon Technologies Ag | Verfahren zum Ermitteln eines Modells für ein elektrisches Netzwerk und Verwendung des Verfahrens |
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US6714481B1 (en) * | 2002-09-30 | 2004-03-30 | The United States Of America As Represented By The Secretary Of The Navy | System and method for active sonar signal detection and classification |
US20070136018A1 (en) * | 2005-12-09 | 2007-06-14 | Fernandez Andrew D | Nonlinear model calibration using attenuated stimuli |
JP2010216995A (ja) * | 2009-03-17 | 2010-09-30 | Toyota Motor Corp | 電池特性判断方法及び電池特性判断装置 |
MX346388B (es) * | 2010-10-22 | 2017-03-03 | Nucleus Scient Inc | Aparato y metodo para cargar baterías rápidamente. |
KR101416399B1 (ko) * | 2012-12-11 | 2014-07-09 | 현대자동차 주식회사 | 연료 전지 스택의 고장 진단 장치 |
KR101511866B1 (ko) * | 2013-11-08 | 2015-04-14 | 현대오트론 주식회사 | 연료전지 스택의 고장 진단 방법 및 이를 실행하는 장치 |
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2016
- 2016-11-04 AT ATA51008/2016A patent/AT519335B1/de not_active IP Right Cessation
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2017
- 2017-11-02 KR KR1020197013032A patent/KR20190077384A/ko not_active Application Discontinuation
- 2017-11-02 JP JP2019522773A patent/JP2019537004A/ja active Pending
- 2017-11-02 US US16/347,140 patent/US20200064406A1/en not_active Abandoned
- 2017-11-02 DE DE112017005566.0T patent/DE112017005566A5/de not_active Withdrawn
- 2017-11-02 WO PCT/EP2017/078007 patent/WO2018083147A1/de active Application Filing
- 2017-11-02 CN CN201780071786.8A patent/CN109983353A/zh active Pending
Patent Citations (1)
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DE102005020318A1 (de) * | 2005-05-02 | 2006-11-16 | Infineon Technologies Ag | Verfahren zum Ermitteln eines Modells für ein elektrisches Netzwerk und Verwendung des Verfahrens |
Non-Patent Citations (3)
Title |
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BORIS BENSMANN ET AL: "Nonlinear Frequency Response of Electrochemical Methanol Oxidation Kinetics: A Theoretical Analysis", JOURNAL OF THE ELECTROCHEMICAL SOCIETY, vol. 157, no. 9, 1 January 2010 (2010-01-01), US, pages B1279, XP055452827, ISSN: 0013-4651, DOI: 10.1149/1.3446836 * |
KADYK T ET AL: "Nonlinear frequency response analysis of PEM fuel cells for diagnosis of dehydration, flooding and CO-poisoning", JOURNAL OF ELECTROANALYTICAL CHEMISTRY AND INTERFACIAL ELECTROCHEMISTRY, ELSEVIER, AMSTERDAM, NL, vol. 630, no. 1-2, 15 May 2009 (2009-05-15), pages 19 - 27, XP026044120, ISSN: 0022-0728, [retrieved on 20090210], DOI: 10.1016/J.JELECHEM.2009.02.001 * |
PENG Z K ET AL: "Feasibility study of structural damage detection using NARMAX modelling and Nonlinear Output Frequency Response Function based analysis", MECHANICAL SYSTEMS AND SIGNAL PROCESSING, ELSEVIER, AMSTERDAM, NL, vol. 25, no. 3, 14 September 2010 (2010-09-14), pages 1045 - 1061, XP028363605, ISSN: 0888-3270, [retrieved on 20101021], DOI: 10.1016/J.YMSSP.2010.09.014 * |
Also Published As
Publication number | Publication date |
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JP2019537004A (ja) | 2019-12-19 |
DE112017005566A5 (de) | 2019-08-29 |
AT519335B1 (de) | 2022-08-15 |
CN109983353A (zh) | 2019-07-05 |
KR20190077384A (ko) | 2019-07-03 |
AT519335A1 (de) | 2018-05-15 |
US20200064406A1 (en) | 2020-02-27 |
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