EP0834845A1 - Procédé d'analyse en fréquence d'un signal - Google Patents

Procédé d'analyse en fréquence d'un signal Download PDF

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Publication number
EP0834845A1
EP0834845A1 EP96115952A EP96115952A EP0834845A1 EP 0834845 A1 EP0834845 A1 EP 0834845A1 EP 96115952 A EP96115952 A EP 96115952A EP 96115952 A EP96115952 A EP 96115952A EP 0834845 A1 EP0834845 A1 EP 0834845A1
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EP
European Patent Office
Prior art keywords
wavelet
signal
fuzzy logic
pass filter
frequency
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.)
Withdrawn
Application number
EP96115952A
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German (de)
English (en)
Inventor
Marc Pierre Dr. Thuillard
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.)
Cerberus AG
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Cerberus AG
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 Cerberus AG filed Critical Cerberus AG
Priority to EP96115952A priority Critical patent/EP0834845A1/fr
Priority to AT97939930T priority patent/ATE214504T1/de
Priority to DE59706608T priority patent/DE59706608D1/de
Priority to PCT/CH1997/000354 priority patent/WO1998015931A1/fr
Priority to EP97939930A priority patent/EP0865646B1/fr
Priority to CN97191373A priority patent/CN1129879C/zh
Priority to KR1019980704157A priority patent/KR19990071873A/ko
Priority to PL97327070A priority patent/PL327070A1/xx
Priority to US09/077,106 priority patent/US6011464A/en
Priority to JP10517041A priority patent/JP2000503438A/ja
Publication of EP0834845A1 publication Critical patent/EP0834845A1/fr
Withdrawn legal-status Critical Current

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    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B29/00—Checking or monitoring of signalling or alarm systems; Prevention or correction of operating errors, e.g. preventing unauthorised operation
    • G08B29/18—Prevention or correction of operating errors
    • G08B29/185—Signal analysis techniques for reducing or preventing false alarms or for enhancing the reliability of the system
    • G08B29/186—Fuzzy logic; neural networks
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B17/00—Fire alarms; Alarms responsive to explosion
    • G08B17/02—Mechanical actuation of the alarm, e.g. by the breaking of a wire

Definitions

  • the invention relates to a method for frequency analysis of a signal using wavelets and Fuzzy logic, in particular an output signal from a safety detector such as one Flame detectors, noise detectors, fire detectors, passive infrared detectors or the like to avoid false alarms.
  • a safety detector such as one Flame detectors, noise detectors, fire detectors, passive infrared detectors or the like to avoid false alarms.
  • the output signals from safety detectors are often typical for them Frequency spectra marked. By analyzing these frequency spectra, the origin can be determined of the signals can be determined, and especially real alarm signals from interference signals differentiate and avoid false alarms. Especially with flame detectors the typical low frequency flickering of a flame is analyzed to determine the radiation from real flames from a source of interference such as reflected sunlight or to distinguish flickering light source.
  • the wavelet transform is as described, for example, in "The Fast Wavelet Transform” (Mac A. Cody, Dr. Dobb's Journal, April 1992), a transformation or illustration of a signal from the time domain to the frequency domain and is therefore basically the Fourier transform and Fast Fourier transform similar. It differs from these but through the basic function of the transformation, after which the signal is developed.
  • a Fourier transform uses a sine and cosine function, which in Frequency range are localized sharply and are undetermined in the time range.
  • a wavelet transformation a so-called wavelet or wave packet is used.
  • Gaussian, Spline or Haar wavelet each through two parameters arbitrarily shifted in the time domain and stretched in the frequency domain or can be compressed.
  • a wavelet transformation can therefore be used in both Signals localized in time as well as in the frequency domain are transformed.
  • a fast Wavelet transformation is carried out by the pyramid algorithm according to Mallat, which is based on repeated application of a low-pass and high-pass filter, by which the low-frequency from the high-frequency signal components are separated. Doing so in each case the output signal of the low-pass filter in turn a pair of low / high-pass filters fed.
  • a series of approximations of the original signal results, of which each has a coarser resolution than the previous one.
  • the number of operations for the Transformation is required is proportional to the length of the original signal, while in the Fourier transform this number is disproportionate to the signal length.
  • the fast wavelet transformation can also be performed inversely by using the original signal from the approximated values and coefficients for the reconstruction is restored.
  • the algorithm for the decomposition and reconstruction of the signal as well as a table of the coefficients of the decomposition and reconstruction are based on the example for a spline wavelet in "An Introduction to Wavelets” by Charles K. Chui (Academic Press, San Diego, 1992).
  • Fuzzy logic is well known.
  • Signal values known as fuzzy sets, or unsharp amounts according to a Membership function, where the value of the membership function, or the degree of belonging to a fuzzy set is between zero and one. It is important that the membership function can be normalized, i.e. the sum of all Values of the membership function equal to one, where by the fuzzy logic evaluation one clear interpretation of the signal allowed.
  • Known applied analyzes for the output signals of security detectors are for For example the Fourier analysis, the Fast Fourier analysis, the zero crossing method or Turning point method. The latter is used in GB 2 277 989 for flame detectors described. Here the time spans between radiation maxima (turning points) measured and checked for their regularities and irregularities. In doing so Irregular radiation maxima as a flame and regular as a disturbance interpreted.
  • the object of the invention is to provide a method for frequency analysis of a signal create that is combined with a fuzzy logic evaluation and compared to State of the art analysis method with a smaller number of calculation steps is carried out so that in a shorter time a result of the same or higher accuracy is achieved. Furthermore, the method is intended to be a simpler processor and thereby be more cost-effective.
  • the object is achieved according to the invention by a method for frequency analysis of a Signal solved that a fast wavelet transformation of the signal with a fuzzy logic evaluation united, with the original signal in the wavelet transform a multi-stage filter cascade of high / low pass filter pairs is carried out and by each Level of the filter cascade from the initial values of the high pass filter one Membership function is generated directly in this form for further analysis of the Frequency signal is used according to fuzzy logic rules.
  • a Safety detectors allow the results of the fuzzy evaluation to decide whether an alarm is issued or an interference signal is present.
  • the number of required Computational steps for the wavelet analysis is significant in comparison to Fourier analyzes reduced. This is the necessary computing time to identify the signal, and it this reduces the cost of the processor.
  • the original digitized signal is first replaced by a fast one Wavelet transformation analyzed.
  • the signal is processed according to the Mallat algorithm through several stages of a cascade of high and low pass filter pairs. From the Results of the high-pass filter then become a membership function at each filter level ⁇ generated, which contains the sum of the calculated values from the high pass filter and by the sum of the squares of the original signal values is divided. The sum of the Membership functions ⁇ that are generated here for each filter level are the same or almost equal to one.
  • Frequency analysis of this type gives the following advantages.
  • the high-pass filters of the wavelet transform first provide information about the high-frequency signals. This is Particularly advantageous in the flame report, since it contains information about the higher ones Frequencies speeds up the identification of the type of signal and increases its accuracy can be. For example, if a high-frequency signal of over 15 Hz is detected, this interpreted as an interference signal. The subsequent message, fault signal or alarm signal, takes place earlier and is more certain to be correct.
  • Wavelets are often very in their form simple, such as a hair wavelet, and allow analysis with few Arithmetic steps, which additionally shortens the computing time and decision time. Are less Lines of code required, an inexpensive processor can also be used. The Shortening the decision time is not, however, a loss of accuracy Signal identification connected.
  • a is used for the wavelet transformation orthonormal or semi-orthonormal wavelet or a wavelet packet basis used.
  • the membership functions are derived from the results of the high pass filter and the wavelet coefficient for the reconstruction of the original signal. More specifically, the membership function contains a weighted by the wavelet coefficients Sum of the squared values of the high pass filter and in the denominator the sum of the squared Value of the original signal. The sum of these membership functions is approximate here equal to one, especially if the original signal contains enough values.
  • the membership functions are then used for a fuzzy logic evaluation of the Frequency information used.
  • the wavelet transformation is carried out using an orthonormal or semi-orthonormal wavelet or a wavelet packet basis, where at a membership function is created for each filter level, which is the sum of the squared Output values of the high pass filter and in the denominator the sum of the squared values of the original signal contains.
  • membership functions are in turn normalized and are used in this form directly for a fuzzy logic evaluation of the frequency information used.
  • FIGS. 1 and 2 show a block diagram the method with the fast wavelet analysis through several filter stages and Further analysis using fuzzy logic.
  • Figure 2 shows membership functions using the example of a Frequency analysis using a fast hair wavelet transformation.
  • a fast wavelet transformation is first carried out using any wavelet as is known in the prior art.
  • An orthonormal or semi-orthonormal wavelet or a wavelet packet base is preferably used.
  • the signal values are denoted by x i, k and y i, k , where x means the original signal values and the values from the low-pass filters (LP) and y the values from the high-pass filters (HP).
  • the index i denotes the level of the filter cascade in increasing numbers, the original signal being at level zero.
  • the index k denotes an individual value of a signal.
  • An original signal x 0, k at zero level is assumed, which is transformed by several filterings.
  • the output signal of the first high-pass filter gives the values y 1, k and the output signal of the low-pass filter gives the values x 1, k , which also forms the input signal for the second filter stage.
  • the output signal of the second high-pass filter gives the values y 2, y , that of the low-pass filter x 2, k is in turn fed to a third pair of filters , etc. It should be noted here that the number of values resulting from the filter stages is different for each stage . More precisely, at each level the number of values decreases by a factor of two.
  • the coefficients p and q for the wavelet reconstruction can again be found in the above-mentioned book.
  • the membership functions ⁇ i are now generated from the output values of the high-pass filter of the respective filter stage and the associated coefficients q for the wavelet reconstruction.
  • the digitized raw values x 0 , k are subjected to a quick hair analysis.
  • membership functions ⁇ are shown as a function of the frequency ⁇ , which have been generated from the results of a fast Haar wavelet transformation.
  • ⁇ N + 1 illustrate the degree of affiliation of very low frequencies
  • ⁇ N that of low frequencies
  • ⁇ 1 and ⁇ 2 the degree of affiliation of high and medium frequencies ⁇ . It is clearly evident here that the sum of the curve values is one for each selected frequency ⁇ .
  • This method is suitable for differentiation when used on flame detectors between interference signals, such as periodic signals above 15 Hz, and real ones Flame signals, such as narrow-band signals of low frequency or broadband signals in the low frequency range.
  • interference signals such as periodic signals above 15 Hz
  • Flame signals such as narrow-band signals of low frequency or broadband signals in the low frequency range.
  • the procedure for evaluating signals is also passive for noise detectors Infrared detector, for the spectral analysis of the signals of individual pixels in image processing as well as for various sensors such as gas and vibration sensors.

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  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Fuzzy Systems (AREA)
  • Software Systems (AREA)
  • Artificial Intelligence (AREA)
  • Automation & Control Theory (AREA)
  • Evolutionary Computation (AREA)
  • Computer Security & Cryptography (AREA)
  • Mathematical Physics (AREA)
  • Emergency Management (AREA)
  • Business, Economics & Management (AREA)
  • Alarm Systems (AREA)
  • Radar Systems Or Details Thereof (AREA)
  • Geophysics And Detection Of Objects (AREA)
  • Fire-Detection Mechanisms (AREA)
  • Lighting Device Outwards From Vehicle And Optical Signal (AREA)
  • Feedback Control In General (AREA)
  • Photometry And Measurement Of Optical Pulse Characteristics (AREA)
EP96115952A 1996-10-04 1996-10-04 Procédé d'analyse en fréquence d'un signal Withdrawn EP0834845A1 (fr)

Priority Applications (10)

Application Number Priority Date Filing Date Title
EP96115952A EP0834845A1 (fr) 1996-10-04 1996-10-04 Procédé d'analyse en fréquence d'un signal
AT97939930T ATE214504T1 (de) 1996-10-04 1997-09-19 Verfahren zur analyse des signals eines gefahrenmelders und gefahrenmelder zur durchführung des verfahrens
DE59706608T DE59706608D1 (de) 1996-10-04 1997-09-19 Verfahren zur analyse des signals eines gefahrenmelders und gefahrenmelder zur durchführung des verfahrens
PCT/CH1997/000354 WO1998015931A1 (fr) 1996-10-04 1997-09-19 Procede d'analyse du signal d'un avertisseur de danger, et avertisseur de danger pour la mise en oeuvre de ce procede
EP97939930A EP0865646B1 (fr) 1996-10-04 1997-09-19 Procede d'analyse du signal d'un avertisseur de danger, et avertisseur de danger pour la mise en oeuvre de ce procede
CN97191373A CN1129879C (zh) 1996-10-04 1997-09-19 用于分析危险报警器信号的方法及实施该方法的危险报警器
KR1019980704157A KR19990071873A (ko) 1996-10-04 1997-09-19 위험탐지신호를해석하는방법및상기방법을수행하기위한위험탐지기
PL97327070A PL327070A1 (en) 1996-10-04 1997-09-19 Method of analysing signals from a hazard signalling device and hazard signalling device as such
US09/077,106 US6011464A (en) 1996-10-04 1997-09-19 Method for analyzing the signals of a danger alarm system and danger alarm system for implementing said method
JP10517041A JP2000503438A (ja) 1996-10-04 1997-09-19 危険検出器の信号を分析する方法とその方法を実施するための危険検出器

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
EP96115952A EP0834845A1 (fr) 1996-10-04 1996-10-04 Procédé d'analyse en fréquence d'un signal

Publications (1)

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EP0834845A1 true EP0834845A1 (fr) 1998-04-08

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EP96115952A Withdrawn EP0834845A1 (fr) 1996-10-04 1996-10-04 Procédé d'analyse en fréquence d'un signal
EP97939930A Expired - Lifetime EP0865646B1 (fr) 1996-10-04 1997-09-19 Procede d'analyse du signal d'un avertisseur de danger, et avertisseur de danger pour la mise en oeuvre de ce procede

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Country Status (9)

Country Link
US (1) US6011464A (fr)
EP (2) EP0834845A1 (fr)
JP (1) JP2000503438A (fr)
KR (1) KR19990071873A (fr)
CN (1) CN1129879C (fr)
AT (1) ATE214504T1 (fr)
DE (1) DE59706608D1 (fr)
PL (1) PL327070A1 (fr)
WO (1) WO1998015931A1 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
AU776482B2 (en) * 2000-03-15 2004-09-09 Siemens Building Technologies Ag Method for the processing of a signal from an alarm and alarms with means for carrying out said method
WO2007012331A2 (fr) * 2005-07-29 2007-02-01 V & M Deutschland Gmbh Procede de verification non destructive de tuyaux, pour detecter d'eventuels defauts superficiels

Families Citing this family (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6742183B1 (en) 1998-05-15 2004-05-25 United Video Properties, Inc. Systems and methods for advertising television networks, channels, and programs
US6219373B1 (en) * 1998-06-15 2001-04-17 The United States Of America As Represented By The Secretary Of The Navy Wavelet-based interference filtering for spread-spectrum signal
US6184792B1 (en) * 2000-04-19 2001-02-06 George Privalov Early fire detection method and apparatus
RU2003133287A (ru) * 2001-05-11 2005-05-27 Детектор Электроникс Корпорэйшн (Us) Способ и устройство обнаружения пламени путем формирования изображения пламени
FR2841424A1 (fr) * 2002-06-25 2003-12-26 Koninkl Philips Electronics Nv Procede de detection d'artefacts de bloc
US7202794B2 (en) * 2004-07-20 2007-04-10 General Monitors, Inc. Flame detection system
CN101711393A (zh) * 2007-01-16 2010-05-19 Utc消防及保安公司 基于视频的火灾检测的系统和方法
US8094015B2 (en) * 2009-01-22 2012-01-10 International Business Machines Corporation Wavelet based hard disk analysis
US8941734B2 (en) * 2009-07-23 2015-01-27 International Electronic Machines Corp. Area monitoring for detection of leaks and/or flames
US8359616B2 (en) 2009-09-30 2013-01-22 United Video Properties, Inc. Systems and methods for automatically generating advertisements using a media guidance application
US8949901B2 (en) 2011-06-29 2015-02-03 Rovi Guides, Inc. Methods and systems for customizing viewing environment preferences in a viewing environment control application
CN103501205B (zh) * 2013-10-11 2016-05-11 北京理工大学 基于模糊综合评判的目标跳频信号识别方法

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US4866420A (en) * 1988-04-26 1989-09-12 Systron Donner Corp. Method of detecting a fire of open uncontrolled flames
EP0706142A2 (fr) * 1994-09-30 1996-04-10 Sensormatic Electronics Corporation Méthode et dispositif de détection d'une étiquette de marquage pour le surveillance électronique d'articles utilisant un traitement de signaux de transformation en vaguelettes
EP0718814A1 (fr) * 1994-12-19 1996-06-26 Cerberus Ag Procédé et dispositif de détection de flamme

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US5453733A (en) * 1992-07-20 1995-09-26 Digital Security Controls Ltd. Intrusion alarm with independent trouble evaluation
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Patent Citations (3)

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Publication number Priority date Publication date Assignee Title
US4866420A (en) * 1988-04-26 1989-09-12 Systron Donner Corp. Method of detecting a fire of open uncontrolled flames
EP0706142A2 (fr) * 1994-09-30 1996-04-10 Sensormatic Electronics Corporation Méthode et dispositif de détection d'une étiquette de marquage pour le surveillance électronique d'articles utilisant un traitement de signaux de transformation en vaguelettes
EP0718814A1 (fr) * 1994-12-19 1996-06-26 Cerberus Ag Procédé et dispositif de détection de flamme

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Title
AKAY Y M ET AL: "NONINVASIVE DETECTION OF CORONARY ARTERY DISEASE", IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE, vol. 13, no. 5, November 1994 (1994-11-01), pages 761 - 764, XP000598968 *
CHAKRABARTI C ET AL: "EFFICIENT REALIZATIONS OF THE DISCRETE AND CONTINUOUS WAVELET TRANSFORMS: FROM SINGLE CHIP IMPLEMENTATIONS TO MAPPINGS ON SIMD ARRAY COMPUTERS", IEEE TRANSACTIONS ON SIGNAL PROCESSING, vol. 43, no. 3, 1 March 1995 (1995-03-01), pages 759 - 771, XP000507252 *
MUFTI M ET AL: "AUTOMATED FAULT DETECTION AND IDENTIFICATION USING A FUZZY-WAVELET ANALYSIS TECHNIQUE", CONFERENCE RECORD AUTOTESTCON '95, ATLANTA, AUG. 8 - 10, 1995, vol. 31, 8 August 1995 (1995-08-08), INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS, pages 169 - 175, XP000555102 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
AU776482B2 (en) * 2000-03-15 2004-09-09 Siemens Building Technologies Ag Method for the processing of a signal from an alarm and alarms with means for carrying out said method
WO2007012331A2 (fr) * 2005-07-29 2007-02-01 V & M Deutschland Gmbh Procede de verification non destructive de tuyaux, pour detecter d'eventuels defauts superficiels
WO2007012331A3 (fr) * 2005-07-29 2007-04-19 V&M Deutschland Gmbh Procede de verification non destructive de tuyaux, pour detecter d'eventuels defauts superficiels
US7783432B2 (en) 2005-07-29 2010-08-24 V & M Deutschland Gmbh Method for nondestructive testing of pipes for surface flaws

Also Published As

Publication number Publication date
KR19990071873A (ko) 1999-09-27
DE59706608D1 (de) 2002-04-18
PL327070A1 (en) 1998-11-23
JP2000503438A (ja) 2000-03-21
EP0865646A1 (fr) 1998-09-23
ATE214504T1 (de) 2002-03-15
EP0865646B1 (fr) 2002-03-13
CN1129879C (zh) 2003-12-03
WO1998015931A1 (fr) 1998-04-16
US6011464A (en) 2000-01-04
CN1205094A (zh) 1999-01-13

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