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EP3916691B1 - Procédé de détection de contamination d'un détecteur d'incendie, détecteur d'incendie, programme informatique et support d'enregistrement lisible par machine - Google Patents

Procédé de détection de contamination d'un détecteur d'incendie, détecteur d'incendie, programme informatique et support d'enregistrement lisible par machine Download PDF

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Publication number
EP3916691B1
EP3916691B1 EP21167826.3A EP21167826A EP3916691B1 EP 3916691 B1 EP3916691 B1 EP 3916691B1 EP 21167826 A EP21167826 A EP 21167826A EP 3916691 B1 EP3916691 B1 EP 3916691B1
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Prior art keywords
time series
measurement signal
fire detector
analysis
series analysis
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German (de)
English (en)
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EP3916691A1 (fr
Inventor
Thomas Hanses
Christopher Haug
Markus Ulrich
Robert Hartl
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Robert Bosch GmbH
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Robert Bosch GmbH
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B17/00—Fire alarms; Alarms responsive to explosion
    • G08B17/10—Actuation by presence of smoke or gases, e.g. automatic alarm devices for analysing flowing fluid materials by the use of optical means
    • G08B17/103—Actuation by presence of smoke or gases, e.g. automatic alarm devices for analysing flowing fluid materials by the use of optical means using a light emitting and receiving device
    • G08B17/107—Actuation by presence of smoke or gases, e.g. automatic alarm devices for analysing flowing fluid materials by the use of optical means using a light emitting and receiving device for detecting light-scattering due to smoke

Definitions

  • the present invention relates to a method for detecting contamination of a fire detector, as well as a fire detector configured to carry out the method, a computer program and a machine-readable storage medium.
  • Fire detectors are often designed as scattered light fire detectors, which have a scattered light path, a light source (e.g. LED) and a light sensor (e.g. photodiode).
  • the light source emits light in the IR or VIS range.
  • the light sensor is arranged at a defined angle to the direction of radiation of the light source, so that light normally does not hit the light sensor or only hits it to a very small extent.
  • Light is only detected by the light sensor when particles (e.g. smoke or dust) enter the optical path between the light source and the light sensor and scatter the light from the light source onto the light sensor.
  • particles e.g. smoke or dust
  • a fire is detected, for example, and issued as an alarm.
  • other physical variables e.g. temperature and/or CO content, can be used as criteria for fire detection.
  • the publication EN 10 2010 041 693 A1 describes a method for testing the functionality of a photoelectric smoke detector with a transmitter element and a sensor element.
  • the transmitter element is triggered to emit a test beam and the sensor element records this as a measurement signal.
  • the recorded measurement signal is compared with a Reference signal is compared and based on this the functionality of the smoke detector, in particular of the transmitting element and sensor element, is determined.
  • the publication EP 2 189 956 A1 relates to a fire detector and a method for detecting dirt or covers on the housing openings and/or an insect screen, which interfere with the penetration of fire measurements into the detection range of the fire detector.
  • the measurement signal or operating data of the detector is subjected to double exponential smoothing and weighting, so that a forecast of the remaining operating time of the detector is possible from the smoothed basic signal.
  • a method for improved functional testing of a fire detector is therefore desirable, in particular in order to be able to provide and/or operate a fire detector with improved safety and greater reliability.
  • a method for detecting contamination of a fire detector is provided with the features of claim 1. Furthermore, a fire detector, a computer program and a machine-readable storage medium are proposed. Preferred and/or advantageous embodiments emerge from the subclaims and the description.
  • contamination detection constitutes a functional test of the fire detector.
  • Contamination detection is understood, for example, to mean detection of the presence and/or degree of deposits of dirt, dust and/or moisture, aging and/or wear of electronics and/or dust particles in the air.
  • the fire detector is designed to detect a fire, in particular smoke, flames, embers and/or a smoldering fire.
  • the fire detector is designed in particular as an optical fire detector, in particular as a scattered light detector or scattered light smoke detector.
  • the fire detector is designed to detect the fire based on a thermal variable, for example the temperature, an attenuation, for example of ionizing radiation, and/or conductivity.
  • the fire detector can comprise a camera for image-based fire detection.
  • the fire detector has at least one sensor device for detecting a measured variable.
  • the sensor devices preferably comprise a light source and a light sensor.
  • the light source and light sensor are arranged in such a way that the light from the light source is not detected by the light sensor without scattering on particles and/or smoke, and is only detected by the light sensor when the light emitted by the light source is scattered on particles, dirt, moisture and/or smoke.
  • the light sensor is designed in particular to measure a quantity of light and to detect it as a measured variable.
  • the measured variable detected is output, for example, as a voltage and/or current.
  • the measured variable output forms in particular the measurement signal.
  • the light sensor is preferably designed as a photodiode.
  • the measurement signal preferably forms the detected quantity of light in millivolts.
  • the measured variable is detected by the sensor devices in particular continuously and/or cyclically, for example every second or faster.
  • the measuring signal includes in particular the measured quantity and forms, for example, a temporal progression of
  • the measurement signal includes noise and/or scatter.
  • the measurement of the measured variable is an error-prone measurement, so that, for example, the measurement of a constant variable also leads to deviations in the recorded measured variable, whereby these deviations have, for example, a spread, a standard deviation and/or a variance.
  • the deviations are understood in particular as noise and/or scatter, in particular around the actual measured value and/or an average value.
  • the noise and/or scatter is in particular based on electronic noise, measurement accuracy and/or sensitivity of the sensor devices, fluctuations in environmental parameters and/or other sources of error.
  • the noise and/or scatter is in particular a deviation on small time scales, in particular less than 1 second.
  • the measured variable is recorded with a sampling rate of less than 1 second, in particular less than or in the range of statistical noise and/or scatter.
  • the measurement value is recorded in particular with a resolution of less than 1 V, especially less than 1 mV, preferably with a resolution smaller than the statistical and/or electronic noise and/or scatter.
  • the measurement signal can be a plurality and/or a superposition of different noises and/or scatters, for example the measurement signal is a measured average value and/or real value plus a first noise, for example electronic noise, plus a second noise, for example a change in the ambient parameters.
  • the measurement signal is preferably designed as an analog signal, in particular a current or voltage signal.
  • the method comprises the detection of the measurement signal of the sensor device and/or the detection of several measurement signals of several sensor devices for and/or over at least one evaluation time interval.
  • the evaluation time interval is in particular designed to comprise at least 1000, preferably at least 10000 and in particular at least 100000 measurement points, wherein a measurement point describes a measured variable detected at a time.
  • the measurement signal is detected for at least 5 minutes, in particular at least 1 hour and in particular at least one day, wherein the temporal resolution of the measured value acquisition, for example, less than or equal to 1
  • the light sensor and its photodiode are designed to output a voltage signal, wherein the resolution and/or scale division of the measurement signal is less than or equal to 1 mV.
  • the detection of the measurement signal includes storing the measurement signal, in particular as a measurement signal curve.
  • the method provides a time series analysis for the recorded measurement signal and/or for the recorded measurement signals.
  • the time series analysis is carried out for the evaluation time interval.
  • the evaluation time interval is divided into sub-intervals, wherein the sub-intervals are in particular of the same size.
  • the time interval is divided in different ways for the time series analysis, for example for different sub-interval lengths.
  • the time series analysis includes a plurality of sub-evaluations for different sub-intervals.
  • the time series analysis is based on known statistical, mathematical and/or stochastic methods and/or models.
  • the time series analysis is preferably carried out using a computer and/or software implementation.
  • analysis parameters are determined, calculated and/or estimated by the time series analysis.
  • the analysis parameters obtained are, for example, analysis results of the time series analysis.
  • the level of contamination, functionality and/or operational readiness of the fire detector is determined.
  • the level of contamination of a smoke chamber and/or contamination of a smoke inlet opening into a labyrinth of an optical scattered light smoke detector is determined based on the time series analysis.
  • the level of contamination, functionality and/or operational readiness is preferably determined based on the analysis parameters obtained. For example, a deviation of the analysis parameter(s) from a target value or target range is determined, whereby, for example, if the deviation is too large upwards and/or downwards, contamination is deemed to be detected and/or present.
  • the invention is based on the idea that by evaluating a measurement signal in a longer evaluation time interval, conclusions can be drawn about contamination and/or operability of the fire detector. While for the evaluation and/or determination of a fire, essentially only the current magnitude of the measurement signal or the measured value is of interest, the temporal course of the measurement signal can provide additional information through a time series analysis, by means of which contamination or operability can be assessed and/or determined.
  • the time series analysis is preferably carried out for the noise and/or the scatter.
  • the method provides, for example, a time series analysis of the course of the scatter and/or the noise over time. This is based on the idea that the mean value or main part of the measurement signal used to determine fire changes only slowly and slightly due to small contaminations. By evaluating and time series analyzing these small fluctuations in the form of noise and scatter, even slow processes such as contamination, electrical noise or dust can be detected. For example, it is detected and/or analyzed how the noise and/or the scatter changes over time in the evaluation time interval. For example, scatter widths and/or widths of the noise can be determined, analyzed and/or used for this purpose.
  • the measurement signal is made up of a dominant mean value component, a slowly changing trend component and/or a quasi-periodic trend component.
  • the mean value component corresponds, for example, to a certain particle concentration, which reacts with a strong increase in the event of a fire, for example.
  • the slow trend component is based, for example, on contamination, in particular in the form of deposits of dust, moisture or aging of the light source and/or the light sensor.
  • a quasi-periodic trend component is understood to be, for example, a swirling formation of dust, for example through ventilation, blowing and/or thermals. According to the invention, the method provides that the measurement signal is trend-corrected before the time series analysis.
  • the trend-correcting of the measurement signal for the recorded measurement signal can take place in the evaluation time interval after the measurement signal has been recorded, but before the time series analysis.
  • the trend can, for example, be a linear trend, a quadratic or any other trend. polynomial trend.
  • the time series analysis is carried out for the trend-adjusted measurement signal.
  • the trend adjustment can also be carried out during the actual time series analysis, whereby the measurement signal of the evaluation time interval is divided into sub-intervals and the trend adjustment is carried out for the respective sub-intervals, whereby the further actual time series analysis, in particular of the noise and/or the scatter, is carried out for and/or in the trend-adjusted sub-intervals.
  • This design is based on the consideration that some time series analyses, in particular statistical, stochastic and/or mathematical methods, are not possible for trend-affected quantities, signals and/or courses.
  • Time series analysis is a fluctuation analysis.
  • the fluctuation analysis is designed to mathematically analyze a time series and/or measurement series, here the measurement signal, and in particular to determine and/or quantify a correlation, in particular long-term correlation.
  • the time series analysis in particular the fluctuation analysis, can be implemented analytically or numerically.
  • the fluctuation analysis can be determined for autocorrelations and/or cross-correlations. This unclaimed
  • the design is based on the consideration that changes in the state of the fire detector, for example contamination, do not necessarily have to be accompanied by a change in the mean value, but can also cause a slow change that remains unnoticed for a long time. For this reason, for example, a correlation of the measurement signal, in particular the noise and/or the scatter, can be evaluated.
  • the smallest changes in the state of the fire detector, which do not yet have an effect on the mean value, can manifest themselves in the noise, whereby in addition to the noise width, the correlation in particular is a sensitive measure of the changes and can therefore detect changes on the smallest scale at an early stage.
  • the time series analysis can include a correlation analysis and/or an autocorrelation analysis.
  • determining correlations in the measurement signal physically relevant information about the fire detector system can be determined and deposits, contamination, dust or electronic aging can be detected early without having to wait for the slow trend of the actual contamination and/or aging.
  • a method is thus provided that can detect and determine contamination and/or functional impairments of a fire detector early on. A long-term stable and error-resistant fire detector can therefore be operated.
  • the time series analysis can comprise a Hurst analysis.
  • the Hurst analysis is used to determine an analysis parameter, for example an exponent H, the so-called Hurst exponent.
  • a Hurst R/S analysis is carried out as a time series analysis.
  • the range (R) of the cumulative and possibly mean-adjusted time series, here the measurement signal is determined for the evaluation time interval divided into subintervals with a subinterval length (s) and is related to the standard deviation (S) of the non-cumulative time series, so that R/S is determined.
  • a functional relationship of R/S can be determined depending on the observation length.
  • the functional relationship is evaluated in particular as a power law R/S - s H and H is determined as the Hurst exponent.
  • a deviation of the Hurst exponent from an ideal value is used to determine contamination and/or operational readiness, the ideal value being, for example, 0.5.
  • the time series analysis comprises a plurality of individual time series analyses.
  • the individual time series analyses are in particular time series analyses based on different statistical moments.
  • individual Hurst analyses are carried out for different statistical moments q.
  • integer values between -10 and 10 are used as statistical moments.
  • the Hurst exponents H are determined as H(q) for the different statistical moments.
  • Such an analysis is referred to in particular as a multifractal spectrum.
  • the determination of contamination and/or operational readiness is based on an evaluation of the functional relationship of H(q).
  • the time series analysis comprises and/or is based on a detrended fluctuation analysis (DFA) or a multifractal detrended fluctuation analysis (MF-DFA).
  • DFA detrended fluctuation analysis
  • MF-DFA multifractal detrended fluctuation analysis
  • the measurement signal is recorded for a plurality of evaluation time intervals, for example at least twice, preferably at least ten times and in particular at least 100 times.
  • the evaluation time intervals are preferably connected flush to one another, for example so that the next evaluation time interval follows directly after the end of the first evaluation time interval; alternatively, the evaluation time intervals can have overlaps or be designed without overlaps, so that, for example, there is a break in recording between two evaluation time intervals.
  • the individual evaluation time intervals are evaluated using time series analysis.
  • the time series analysis in particular the determination of the Hurst exponent and/or the analysis parameters, is carried out for the recorded measurement signals of the plurality of evaluation time intervals. For example, the analysis parameters and/or Hurst parameters are compared.
  • a temporal progression, a change, a correlation or a functional relationship is determined for the Hurst exponents and/or analysis parameters, wherein the determination of the contamination is preferably based on the comparison, the determination and/or the evaluation.
  • the evaluation time intervals are preferably based on a rolling window or sliding window analysis.
  • the rolling window has, for example, a fixed interval length, in particular the evaluation time interval length, wherein the rolling window is shifted to determine the majority of evaluation time intervals, wherein the shifting represents a temporal shift of the detection point.
  • the rolling window can be shifted continuously or discretely, for example with a time offset equal to the evaluation time interval length.
  • the method provides that based on the time series analysis, in particular the analysis parameter and/or the Hurst exponent, the type of contamination is determined and/or a distinction is made between the types of contamination. For example, based on the time series analysis, it is determined whether it is a deposit, in particular a deposit in the area of the sensor devices, or a deposit at an inlet, for example the flow inlet or the opening of the smoke chamber. Furthermore, a distinction can be made as to whether it is a deposit or dust, for example whether it is stationary contamination or dynamic contamination. In particular, the type of contamination that can be distinguished or determined is whether an inlet, for example the opening of the smoke chamber, is tapered, narrowed and/or blocked, which would result in the smoke to be detected entering more poorly.
  • the smoke chamber comprises in particular the measuring channel.
  • a superdiffusion is and/or can be determined based on the time series analysis.
  • Superdiffusion is understood to mean a movement in which the movement of a Brownian particle (i.e., for example, a diffusing particle) is superimposed with a preferred direction.
  • a superposition occurs, for example, when a dust particle is superimposed with an air flow in the area of the sensor devices. Due to a deposit in a flow inlet of the fire detector, for example the opening of the smoke chamber, the air flow in the area of the measuring channel is reduced, so that dust particles are less superimposed with a preferred direction, and the characteristic of the superdiffusion decreases with increasing reduction of the air flow, blockage and deposits of the flow inlet.
  • the method can thus a contamination determination, in particular clogging and/or deposition determination, based on a time series analysis and the determination of superdiffusion based thereon.
  • a further subject of the invention is a fire detector for detecting a fire and/or smoke.
  • the fire detector is designed and/or configured to carry out the method as described above. Based on the measurement variable recorded by the sensor device and/or the measurement signal formed therefrom, the fire detector detects the fire and/or smoke.
  • the fire detector has an evaluation unit, which can be designed using software or hardware.
  • the evaluation unit is in particular designed and/or configured to carry out and/or carry out the method described above.
  • the evaluation unit is designed to record the measurement signal for the evaluation time interval, in a special embodiment for a plurality of evaluation time intervals.
  • the evaluation unit is designed to carry out the time series analysis, the measurement signal having a dominant mean value component, a slow trend component and/or a quasi-periodic trend component, the measurement signal being trend-corrected before or during the time series analysis, the time series analysis being carried out for the trend-corrected measurement signal.
  • the evaluation unit can determine contamination and/or operational readiness of the fire detector.
  • a further subject of the invention is a computer program for execution on a computer and/or the fire detector as described above.
  • the computer program comprises and/or is based on a program code with program code means.
  • the computer program is designed to carry out the steps of the method as described above when executed on the computer and/or the fire detector.
  • the computer program is implemented in the fire detector, specifically in the evaluation unit, so that the evaluation unit determines the contamination and/or the operational readiness of the fire detector by executing the computer program and thus the method.
  • a further subject matter of the invention is a machine-readable storage medium, for example a DVD, CD, diskette or other residual storage medium.
  • the computer program in particular the program code and/or the program code means, is stored on the storage medium.
  • FIGS 1a-d show an embodiment of a photoelectric fire detector 1.
  • the fire detector 1 is designed for mounting on a ceiling 2.
  • the fire detector 1 comprises a sensor device, wherein the sensor device comprises a light source 3 and a light detector 4.
  • the fire detector 1 has a measuring channel 5, wherein the measuring channel 5 is also referred to as a scattered light path.
  • the light detector 4 is designed to detect incident light as a measured variable and to output a voltage signal as a measuring signal.
  • the fire detector 1 in Figure 1a shows the fire detector 1 in a normal state, also called the idle state.
  • the normal state is understood to be the state of the fire detector in a fully functional state without smoke and/or fire, dirt, dust and/or moisture.
  • the light source 3 and light detector 4 are arranged in a common plane, for example aligned with the ceiling 2.
  • the light emitted by the light source 3 can be described in the direction of the light detector 4 by a beam path 6. In the present state, the light detector 3 does not detect any light emitted by the light source 3, since the normal beam path 6 does not extend to the detector.
  • the measurement signal is a voltage around a central zero value, eg 0 mV or 0 mV plus an offset, whereby the measurement signal has a scatter and/or noise around the central zero value.
  • FIG 1b shows fire detector 1 from Figure 1a in the presence of a fire.
  • Smoke 7 has now penetrated the measuring channel 5.
  • the smoke 7 comprises in particular particles and has reflective properties.
  • the light emitted by the light source 3 is scattered by the smoke 7 in the measuring channel 5, with part of the scattered light following an extended beam path 8 to the light detector 4.
  • the scattered light is detected by the light detector 4 and output as a measured variable or measuring signal, with the measuring signal deviating significantly from the mean zero value.
  • the fire is finally detected based on the measuring signal.
  • Figure 1c shows the fire alarm Figure 1a, b in a state without fire and smoke, but with contamination in the form of a dust deposit 9 in the measuring channel 5.
  • the dust deposit 9, also called a dust layer, comprises dust particles and has reflective properties.
  • the extended beam path 8 is present, which is caused by scattering at the dust deposit.
  • more or less strong light scattering is detected by the light detector 4, so that a measuring signal deviating from the mean zero value is output.
  • the measuring signal will be in the normal state in the range of scattering and/or noise.
  • Figure 1d shows fire detector 1 in a state with dust and/or dirt in the area of a smoke inlet opening 10, also called the sensor area entrance.
  • the smoke inlet opening 10 forms the entrance for smoke, air, dust and/or currents into the measuring channel 5. Dust adhering to the area of the smoke inlet opening 10 is carried along by air and/or currents, thus entering the measuring channel 5 and opening up the extended beam path 8 through light scattering, so that a measurement signal deviating from the mean zero value is detected here too.
  • the dust in the area of the smoke inlet opening means that no more smoke can enter and thus no fire is detected.
  • the method described here is intended to detect that the entrance is "blocked". This means that less fluctuation should be measured in the chamber when it is closed than when it is open, in which the air flow is greater.
  • FIG. 2a shows the schematic development of light scattering in a fire detector as shown in the Figures 1a-d with time t.
  • a layer of dust 9 is deposited on a floor 11 of the measuring channel 5.
  • Three snapshots are shown at times t1, t2 and t3.
  • the light from the light source 3 and the beam paths 6 as well as extended beam paths 8 are also shown.
  • a flow 12 which can vary in intensity, leads to movements and a change in the layer of dust 9, so that these fluctuations are reflected in the measuring signal by the light detector 4.
  • a similar measurement signal is obtained for dust 13, which is carried along by the flow 12 through the measuring channel 5 as a dust cloud ( Figure 3a ).
  • dust 13 which is carried along by the flow 12 through the measuring channel 5 as a dust cloud ( Figure 3a ).
  • These entrained dust particles also scatter light from the light source 3 to the light detector 4, so that here too the measurement signal s ( Figure 3b ) there is a fluctuation and/or noise around the mean zero value.
  • Figure 4a shows the path 13 of a dust particle in the measuring channel 5, shown in an xy plane.
  • the dust particle follows a Brownian molecular motion, resulting in a zigzag pattern as path 13.
  • Figure 4b is the temporal progression of the distance r(t) to the initial position for the particle from Figure 4a
  • a mean square displacement ⁇ r 2 (t)> is proportional to the time t.
  • the signal curve of the measurement signal S is shown.
  • the measurement signal S exhibits fluctuations, particularly due to the zigzag path of the dust particle, whereby the fluctuations are uncorrelated.
  • Figure 5a shows how Figure 4a the path 13 of a dust particle in the measuring channel 5, shown in an xy plane.
  • the dust particle here has a preferred direction 14, for example resulting from a superposition of an air flow in the measuring channel 5, which carries the dust particle in the x direction.
  • the path 13 here is not purely given by an uncorrelated Brownian motion, but is extended in the x direction.
  • Figure 5b shows the corresponding distance r(t), which deviates and varies more than the distance r(t) of the free particle ( Figure 4 ).
  • the mean square displacement ⁇ r 2 (t)> also scales more strongly with time. Such a relationship is also noticeable in the measurement signal S.
  • the Figures 6a - d show the principle of a trend-adjusted fluctuation analysis of a measurement signal S.
  • Figure 6a also a cumulative series 15 is shown. The cumulative series 15 results in the trend-adjusted fluctuation analysis as well as in a classic Hurst analysis by successively summing the time series, whereby the time series forms the measurement signal S.
  • the Figures 6b and 6c also show the measurement signal S and the cumulative series 15.
  • the analysis time interval is divided into equal-sized segments (subintervals) of length s.
  • the analysis time interval is divided in different ways, whereby the types differ in the selected length s.
  • Figure 6b shows a subdivision into smaller subintervals, i.e. smaller length s, than the Figure 6c shown subdivision.
  • a polynomial of the nth degree is now fitted to the time series and subtracted from the cumulative series.
  • the variance in each segment is then determined from the resulting residue and averaged over the number of segments.
  • the root of this averaged variance is then taken (standard deviation formation). The result is referred to, for example, as the fluctuation parameter F(s).
  • Figure 7a shows an example of a time series analysis of a measurement signal S issued by a fire detector 1.
  • the cumulative series 15 associated with the measurement signal S is also shown.
  • a fluctuation analysis in particular as for Figure 6 describe, provides the Figure 7b shown functional relationship of the fluctuation function F(s), where F(s) - s ⁇ applies.
  • Figures 8a, b, c show schematically a time series analysis of the measurement signal S in a rolling window analysis.
  • Figure 6a shows the state Z of fire detector 1 as a time course.
  • Figure 6b shows the measurement signal S of the fire detector 1 in the same period of time. While the state Z of the fire detector 1 changes over time, e.g. clogging of housing openings or deposits of dirt, the mean value of the measurement signal S does not change or changes very little during this time. If, however, the fluctuations in given time windows A1, A2, A3, etc. are analyzed, the long-term correlation can change over time depending on the state. This can, for example, result in a significant change in the analysis parameters P determined from the time series analysis ( Figure 8c ). For example, the scale parameter ⁇ is used as the analysis parameter P.

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  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Business, Economics & Management (AREA)
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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Investigating Or Analysing Materials By Optical Means (AREA)
  • Fire-Detection Mechanisms (AREA)

Claims (10)

  1. Procédé de reconnaissance de l'encrassement d'un avertisseur d'incendie (1), l'avertisseur d'incendie (1) comprenant un dispositif capteur destiné à acquérir une grandeur de mesure et à délivrer un signal de mesure (S), le signal de mesure (S) présentant un bruit et/ou une dispersion,
    le procédé comprenant les étapes suivantes :
    - acquisition du signal de mesure (S) du dispositif capteur pour un intervalle de temps d'évaluation,
    - réalisation d'une analyse de séries temporelles pour le signal de mesure (S) dans l'intervalle de temps d'évaluation,
    - détermination d'un encrassement et/ou d'un état prêt à fonctionner de l'avertisseur d'incendie (1) sur la base de l'analyse de séries temporelles
    le signal de mesure (S) présentant une part de valeur moyenne dominante, une part de tendance lente et/ou une part de tendance quasi-périodique, le signal de mesure (S) étant corrigé en tendance avant ou pendant l'analyse de séries temporelles, l'analyse de séries temporelles étant effectuée pour le signal de mesure corrigé en tendance,
    l'analyse de séries temporelles comprenant et/ou formant une analyse de Hurst ou l'analyse de séries temporelles étant basée sur une analyse des fluctuations redressées ou une analyse des fluctuations redressées multifractales.
  2. Procédé selon la revendication 1, caractérisé en ce que l'analyse de séries temporelles est effectuée pour le bruit et/ou la dispersion.
  3. Procédé selon l'une des revendications précédentes, caractérisé en ce que l'analyse de séries temporelles comprend une pluralité d'analyses de séries temporelles individuelles, les analyses de séries temporelles individuelles étant basées sur des moments statistiques (q) différents et/ou sur des degrés (n) différents d'un facteur polynomial.
  4. Procédé selon l'une des revendications précédentes, caractérisé en ce que des signaux de mesure (S) du dispositif capteur sont acquis pour une pluralité d'intervalles de temps d'évaluation (A1, A2, A3), l'analyse de séries temporelles étant effectuée respectivement pour les signaux de mesure (S) des intervalles de temps d'évaluation (A1, A2, A3), l'encrassement et/ou l'état prêt à fonctionner de l'avertisseur d'incendie (1) étant déterminé sur la base d'une comparaison de la pluralité d'analyses de séries temporelles.
  5. Procédé selon la revendication 1, caractérisé en ce que la pluralité d'intervalles de temps d'évaluation (A1, A2, A3) sont basés sur une fenêtre glissante.
  6. Procédé selon l'une des revendications précédentes, caractérisé en ce qu'une distinction du type d'encrassement, notamment le dépôt dans la zone du dispositif capteur, le dépôt au niveau d'une entrée de zone de capteur (10), la poussière, la réduction et/ou l'obstruction de l'entrée de zone de capteur (10), est effectuée sur la base de l'analyse de séries temporelles.
  7. Procédé selon l'une des revendications précédentes, caractérisé en ce qu'une détermination d'une superdiffusion sous la forme d'un mouvement, lors de laquelle le mouvement d'une particule brownienne est superposé à une direction préférentielle, est effectuée sur la base de l'analyse de séries temporelles, l'encrassement étant déterminé sur la base de la détermination de la superdiffusion.
  8. Avertisseur d'incendie (1) destiné à la détection d'un incendie et/ou de fumée sur la base d'une grandeur de mesure et/ou d'un signal de mesure (S), l'avertisseur d'incendie (1) comprenant un dispositif capteur destiné à l'acquisition de la grandeur de mesure et à la délivrance du signal de mesure (S), le signal de mesure (S) présentant un bruit et/ou une dispersion, comprenant une unité d'évaluation, l'unité d'évaluation étant configurée pour mettre en œuvre le procédé selon l'une des revendications précédentes.
  9. Programme informatique destiné à être exécuté sur un ordinateur et/ou l'avertisseur d'incendie (1) selon la revendication 8, caractérisé en ce que le programme informatique est configuré pour exécuter les étapes du procédé selon l'une des revendications 1 à 7 lors de son exécution sur l'ordinateur et/ou l'avertisseur d'incendie (1).
  10. Support de stockage lisible par machine, notamment support de stockage lisible par machine non volatil, sur lequel est stocké le programme informatique selon la revendication 9.
EP21167826.3A 2020-05-25 2021-04-12 Procédé de détection de contamination d'un détecteur d'incendie, détecteur d'incendie, programme informatique et support d'enregistrement lisible par machine Active EP3916691B1 (fr)

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EP4246483A1 (fr) * 2022-03-18 2023-09-20 Siemens Schweiz AG Détecteurs d'incendie à thermistances non chauffées, en particulier ntcs, destinés à la détection des fluctuations thermiques dans la zone des ouvertures d'entrée, ainsi que procédé correspondant
US12243411B2 (en) 2023-06-27 2025-03-04 Honeywell International Inc. Monitoring a self-testing fire sensing device
CN116778661B (zh) * 2023-07-05 2024-06-07 深圳市华翌科技有限公司 一种烟感智能预警方法

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DE102008036437A1 (de) * 2008-08-05 2010-02-18 Hekatron Vertriebs Gmbh Verfahren zum Bestimmen der Betriebsdauer eines Gefahrenmelders und Gefahrenmelder

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DE3831654A1 (de) * 1988-09-17 1990-03-22 Hartwig Beyersdorf Optischer rauchmelder
US7224284B2 (en) * 2004-07-09 2007-05-29 Tyco Safety Products Canada Ltd. Smoke detector calibration
DE102007039401B4 (de) * 2007-08-21 2012-07-12 Hekatron Vertriebs Gmbh Rauchmelder mit Verschmutzungsüberwachung
EP2189956B1 (fr) * 2008-11-21 2013-05-08 Hekatron Vertriebs GmbH Détecteur d'incendie et procédé destiné à la reconnaissance d'impuretés
DE102010041693B4 (de) 2010-09-30 2021-08-19 Robert Bosch Gmbh Verfahren zum Prüfen der Funktionsfähigkeit eines photoelektrischen Rauchmelders sowie Rauchmelder zur Durchführung des Verfahrens
DE102011088850B3 (de) * 2011-12-16 2013-04-04 Robert Bosch Gmbh Vorrichtung zur Detektion von Rauch und Verfahren zum Prüfen der Funktionsfähigkeit einer Vorrichtung zur Detektion von Rauch

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