WO2014099477A1 - Détecteur d'instabilité de flamme - Google Patents

Détecteur d'instabilité de flamme Download PDF

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Publication number
WO2014099477A1
WO2014099477A1 PCT/US2013/074057 US2013074057W WO2014099477A1 WO 2014099477 A1 WO2014099477 A1 WO 2014099477A1 US 2013074057 W US2013074057 W US 2013074057W WO 2014099477 A1 WO2014099477 A1 WO 2014099477A1
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WO
WIPO (PCT)
Prior art keywords
instability
time
burner
detector
foregoing
Prior art date
Application number
PCT/US2013/074057
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English (en)
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WO2014099477A4 (fr
Inventor
Weichang Li
Gary T. Dobbs
Jeffrey M. Grenda
Amy B. Herhold
San Chhotray
Duane R. Mcgregor
Limin Song
Original Assignee
Exxonmobil Research And Engineering Company
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Application filed by Exxonmobil Research And Engineering Company filed Critical Exxonmobil Research And Engineering Company
Publication of WO2014099477A1 publication Critical patent/WO2014099477A1/fr
Publication of WO2014099477A4 publication Critical patent/WO2014099477A4/fr

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Classifications

    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F23—COMBUSTION APPARATUS; COMBUSTION PROCESSES
    • F23N—REGULATING OR CONTROLLING COMBUSTION
    • F23N5/00—Systems for controlling combustion
    • F23N5/24—Preventing development of abnormal or undesired conditions, i.e. safety arrangements
    • F23N5/242—Preventing development of abnormal or undesired conditions, i.e. safety arrangements using electronic means
    • 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
    • G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
    • G05B23/024—Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks

Definitions

  • the invention is generally related to flame instability detectors. Particularly, the present application relates to monitoring a flame state and identifying an instability using a single channel detector.
  • Furnace monitoring is becoming an increasingly important problem in refinery operations.
  • Industrial furnaces, fired heaters, and boilers are used extensively across multiple refinery processes such as process heating and steam production, and are generally responsible for the largest proportion of the total refinery fuel consumption.
  • the proper operation of these furnaces is particularly relevant for safety, environmental, and energy efficiency concerns.
  • NOx emissions can be reduced through lowering the adiabatic flame temperature while maintaining safe operation, which can be achieved by, e.g., enhancing fuel gas recirculation, steam injection, or use of technologies such as premixed flames and ultra-low NOx s.
  • these technologies are often more prone to flame instability than tradition processes. It therefore is necessary to monitor the burner stability and provide feedback signals to control fuel and'or diluent flow when instabilities occur,
  • the disclosed subject matter includes a method for detecting an instability associated with at least one burner.
  • the method can include the steps of obtaining a signal from a detector, the detector measuring at least one characteristic associated with at least one burner; converting, using at least one processor, the signal into a time-varying signal spectrum; and detecting, based at least in part on the time- varying signal spectrum, an instability associated with the at least one burner.
  • the detector can be, for example, a single- channel detector.
  • the characteristic can be a pressure metric, a fluctuation metric, or a vibration metric.
  • the detector can be a dynamic pressure sensor, a device that captures video frames, or an accelerometer.
  • converting the signal into a time-varying signal spectrum comprises using a time- frequency analysis, such as a short-time Fourier transform.
  • the time -varying signal can be represented as a spectrogram.
  • detecting an instability associated with the at least one burner can comprise computing a spectral entropy based at least in part on the time-varying signal spectrum.
  • an instability indicator can be used to detect an instability.
  • the instability indicator can correspond to a probability of instability.
  • the instability indicator can correspond to the temporal-spectral structure of the signal obtained from the detector.
  • the time-varying signal spectrum can be converted into the instability indicator.
  • the time-varying signal spectrum can be normalized to obtain a probability ma ss function.
  • the Shannon entropy of the probability mass function as a function of time can be computed.
  • the inverse of the Shannon entropy of the probability m ss function can then be used as an instability indicator.
  • the instability indicator can be compared to a threshold value. An instability is detected when the instability indicator exceeds a threshold value.
  • an alarm is provided when an instability is detected.
  • Corrective action can be taken when the instability is taken.
  • the correcti ve action can include adjusting an operating property of the at least one burner or disabling the at least one burner.
  • the system can include a detector for obtaining a signal, the detector measuring a characteristic associated with at least one burner; a converter comprising at least one converting processor, the converter configured to convert the signal into a time- varying signal spectrum; and an instability detector comprising at least one instability detector processor, the instability detector configured to detect, based at least in part on the time-varying signal spectrum, an instability associated with the at least one burner. Additional aspects and features of the system are described in conjunction with the method.
  • FIGURE 1 is a graph showing the draft pressure measured by two pressure detectors over time as a flame is driven from stable to unstable combustion and approaches blowoff.
  • FIGURE 2 is a flow chart describing a representative embodiment of a method for detecting an instability associated with at least one burner in accordance with the disclosed subject matter.
  • FIGURE 3 is a graph showing the draft pressure measured by a pressure detector over time as a flame is driven from stable to unstable combustion and approaches blowoff.
  • FIGURE 4 is a series of video frames showing the flames of three burners over time.
  • the video frame rate for the video frames in Figure 4 is around 6.4 frames per second which, with the oscillation cycle having 1 1 frames, leads to an approximately 1.72 second oscillation cycle, or equivalent! ⁇ / 0.58 Hz peak frequency.
  • FIGURE 5 is a flow chart describing a representative embodiment of a method for converting a series of video frames into a scalar time series signal in accordance with the disclosed subject matter.
  • FIGURE 6 is a graph of a scalar time series calculated based on a series of video frames in accordance with the disclosed subject matter.
  • FIGURE 7 A is a spectrogram showing the frequency of a draft pressure signal over time as a flame is driven from stable to unstable combustion and approaches blowoff.
  • FIGURE 7B is a simplified version of Figure 7A and is presented for purposes of explanation only.
  • FIGURE 8 is a graph showing a room mean square plot of a vibration measurement on furnace piping over time as a flame is driven from stable to unstable combustion and approaches blowoff.
  • FIGURE 9 is a flow chart describing a representative embodiment of a method for determining an instability indicator in accordance with the disclosed subject matter.
  • FIGURE 10 is a graph illustrating a comparison between an instability indicator determined in accordance with the disclosed subject matter and an instability indicator determined based on a variance-based approach.
  • FIGURE 1 1 is an illustration of a representative embodiment of a system for detecting an instability associated with at least one burner in accordance with the disclosed subject matter
  • the disclosed subject matter is directed to a method of detecting an instability associated with at least one burner, the method comprising obtaining a signal from a detector, the detector measuring at least one characteristic associated with at least one burner, converting, using at least one processor, the signal into a time- varying signal spectrum, and detecting, based at least in part on the time-varying signal spectrum, an instability associated with the at least one burner.
  • a system is provided herein.
  • the system generally includes a detector configured to obtain a signal measuring at least one characteristic associated with at least one burner, a converter, coupled to the detector, comprising at least one processor and configured to receive the signal from the detector and convert the signal into a time-varying signal spectrum, and an instability detector, coupled to the converter, comprising at least one processor and configured to detect an instability associated with the at least one burner based at least in part on the time -varying signal spectrum.
  • a time series of draft pressure measurements from two detectors is shown as a flame is driven from a stable condition or phase to an unstable condition or phase and approaches blowoff.
  • the time series typically has three phases.
  • the first phase is the stable phase.
  • Stable combustion generates more or less random broadband pressure fluctuations.
  • flame instability as represented by the oscillating phase, is typically coherent, as manifested by harmonic pressure oscillations.
  • the final phase is blowoff. (See 106)
  • the disclosed subject matter generally relates to the detection of an instability associated with at least one burner based on the spectral structure in the measured signal .
  • the disclosed systems and methods are generally discussed as utilizing pressure measurements, it will be understood by those having ordinary skill in the art that other measurements can also be used, as explained in greater detail herein.
  • the disclosed systems and methods are generally discussed as detecting an instability associated with a single burner, those having ordinary skill in the art will understand that the disclosed subject matter can be used to detect an instability associated with more than one burner.
  • a single detector can measure a draft pressure associated with a two burner system and detect an instability associated with the two burner system as a whole.
  • the phrase "detecting an instability” refers to identifying an anomaly from stable combustion. However, the phrase does not include determining whether a flame is present.
  • time-varying signal spectrum refers to the characteristics of the frequency over time.
  • ihai time-varying signal spectrum refers to the signal spectral density estimated continuously over time that characterizes both the spectral structure (e.g. flat over broadband or spiky with peak frequencies) and the time evolution of these spectral structures, including the time trajectory of the peak frequency positions and the magnitude of the associated spectral components.
  • the time-varying signal spectrum can be represented, for example, as a spectrogram.
  • single channel instability detector refers to an apparatus that detects an instability using only a single stream of data (e.g., an apparatus including a single pressure sensor ihai provides updated pressure measurements based on the sampling rate of the sensor).
  • spectral entropy refers to entropy calculated based on the time-varying signal spectrum.
  • a wide variety of methods for calculating entropy can be used as known in the art.
  • the spectral entropy can be the Shannon entropy of the time-varying signal spectrum.
  • the term “coupled” means operatively in communication with each other, either directly or indirectly, using any suitable techniques, including through hard wire, connectors, and remote communicators.
  • a signal is obtained from a detector that measures a characteristic associated with the burner.
  • the detector can be disposed in any manner that allows it to measure the characteristic of interest.
  • the detector may be disposed within the furnace (e.g., in the case of a pressure sensor) or outside of the furnace (e.g., in the case of a video camera for recording the flame).
  • the instability detector can be a single -channel instability detector such that only one detector (e.g., a pressure sensor) is needed. . Two or more detectors can be used in case one of the detectors malfunctions, but the identification of an instability will be based on input from only a single channel of data. In another embodiment, multiple detectors can be used and the measured signals can be combined. While this may improve, for example, the signal-to-noise ratio, the use of a single detector can improve deployment feasibility as compared with the multi-channel format.
  • the characteristic can be a pressure metric such as the draft pressure.
  • the associated detector can be a dynamic pressure sensor, such as a pressure probe, that can capture a high frequency signal.
  • Figure 3 illustrates an exemplary draft, pressure measurement as a function of time as the flame approaches blowoff.
  • the characteristic can be a fluctuation metric.
  • the associated detector can be a device, such as a video camera, that captures video frames.
  • the detector captures a series of video frames.
  • the detector captures video frames 402.-424.
  • Each of the video frames in Figure 4 shows the flames associated with three burners at various times.
  • each video frame is converted into a single value that can be plotted against time.
  • a change index between the intensity in the first video frame and the intensity at the second video frame is calculated (See 508).
  • the change index for each pixel is then aggregated to calculate a fluctuation index for the second video frame (See 510).
  • a scalar time series signal obtained from a series of video frames is shown.
  • the characteristic can be a vibration metric, such as the oscillation of the furnace piping.
  • the associated detector can be an acceierometer, with measurements processed accordingly.
  • optical sensors can be used to measure flicker.
  • Analytic measurements such as measurements of carbon dioxide and sulfur dioxide levels in a furnace, can also be used.
  • the signal obtained from the detector describes the measured characteristic as a function of time.
  • the signal is converted into a time-varying signal spectrum using at least one processor (See 204).
  • the signal can be converted to a time-varying signal spectrum using a time-frequency analysis.
  • the time- frequency analysis can be, for example, a short-time Fourier transform,
  • G(t, ⁇ ) is a kernel function.
  • Git, ⁇ ) h(t+Q.5x)h(t-0.5x) for some window function.
  • the window function h(i) is a locally supported function with finite squared integral, such as a raised cosine window or Hamming window, so that it effectively computes the spectral density of the signal inside a shaped sliding window ,
  • the signal can he converted into a time-varying signal spectrum by any method that determines the frequency spectrum of the signal over time.
  • the time -varying signal spectrum can be represented as a spectrogram.
  • a spectrogram obtained in accordance with the disclosed subject matter is shown in Figure 7 A.
  • a rough approximation of the spectrogram is shown in Figure 7B highlighting the time trajectory of the peak spectral component.
  • the representation in Figure 7B is shown for purposes of explanation only.
  • the spectrogram shows that the signal does not have a steady frequency until the start of a harmonic oscillation at 702.
  • the spectrogram further shows that the signal maintains a fairly steady frequency until a deterioration point 704.
  • deterioration point refers to an area where the signal rapidly decreases from a prolonged steady frequency
  • a lower frequency generally corresponds to a higher amplitude of the signal.
  • the amplitude of the draft pressure signal increases as blowoff approaches.
  • the spectrogram shows that the signal does not have a frequency after blowoff at 706,
  • the time variations of the instability signal spectrum including decreasing peak frequency and higher spectral magnitude, can be explained by an increasing flame lift-off distance which causes a longer re-attach time, and as a result, more intense pressure fluctuations.
  • the time-varying signal spectrum can be represented as the root mean square vibration measurement on furnace piping measured by an accelerometer.
  • the signal measured by the accelerometer can be filtered (e.g., using a low pass filter) before the root mean square representation is calculated.
  • Figure 8 illustrates an exemplary root mean square (RMS) plot of the vibration measurement on furnace piping as the flame approaches blowoff.
  • RMS root mean square
  • the disclosed subject matter further includes detecting, based at least in part on the time-varying signal spectrum, an instability associated w th the burner (See 206).
  • An instability indicator can be used to detect the instability.
  • the instability indicator can correspond to a probability that the flame is unstable based at least in part on the time- varying signal spectrum.
  • a probability mass function in frequency can be derived based on the normalized time -varying signal spectrum (See 904):
  • entropy By converting the time-varying signal spectrum at each time into a probability mass function, entropy can be utilized in capturing the information associated with a probability mass function (PMF). Namely, larger entropy is associated with uncertain distribution and therefore a flatter PMF, because in the absence of any information the distribution will be presumed random. Similarly, smaller entropy is associated with peaky PMF. For a signal associated with stable combustion, its spectrum is typically broadband and relatively flat, and the corresponding PMF is close to a uniform distribution which leads to large entropy. In the presence of instability, steady peak frequencies emerge and signal energy starts to concentrate around those peak frequency points, leading to a "peaky" PMF, or more certain distribution and therefore a smaller value of entropy. [0051] With further reference to Figure 9, the spectral entropy of the signal can be computed (See 906). The spectral entropy can be calculated as the Shannon entropy of the probability mass function as a function of time:
  • Figure 10 is a comparison between an instability indicator in accordance with the disclosed subject matter (utilizing the spectral entropy approach) and an instability indicator obtained using a variance-based approach. The two
  • instability indicators have been normalized such that they are at the same level in the absence of an instability.
  • the instability indicator of invention (labeled “spectral entropy” in Figure 10) shows a significantly higher level of sensitivity.
  • the instability detector can use a threshold to identify an instability.
  • the threshold can be mathematically derived or based on experimental observations. The identification of the threshold can vary based on several variables, including the detector utilized to obtain the signal, the desired target detection probability, the false positive rate, and the detection delay. For example, if it is desired to detect any instability as soon as possible, the threshold value will be lower and the false positi ve rate will increase. However, if it is desired to minimize the false positive rate (e.g., because incorrect detection of an instability is economically disadvantageous), the threshold can be raised and the detection delay will increase. An improved output signal vs.
  • noise ratio (SN ) obtained by a spectral entropy-based indicator can significantly improve detection performance in the sense that given a fixed false positive rate, it can achieve higher detection probability or a shorter detection delay than a detector with lower SNR such as the approach based on signal variance only.
  • the furnace may operate in a mild unstable state for an extended period of time at a steady frequency (as between 702 and 704). Due to the high costs (both economic and environmental) associated with stopping and restarting the furnace, it may be advantageous to keep the burner running during this time period. Therefore, in one embodiment, the threshold can be set so as to detect an instability at the deterioration point 704. Such threshold can be set, for example, based on experimental observation.
  • An alarm can be provided when an instability is detected.
  • the alarm can be, for example, an audio alarm such as a siren.
  • the alarm can also be, for example, a visual alarm such as a flashing light or an indication on the monitor of a computer screen. More generally, any method of informing an operator that an instability has been detected can be used as known in the art for its intended purpose.
  • Corrective action can be taken when an instability is detected.
  • an operating property of the burner can be adjusted.
  • the amount of steam injected into the furnace can be decreased until the instability is resolved.
  • the burner can be disabled, which can prevent an explosion and allow repairs and/or maintenance to be provided to the furnace.
  • the at least one burner can be shut down.
  • the instability detection system 1 100 can include a detector 1 102, a converter 1 104, and an instability detector 1 106.
  • the detector 1 102 is disposed within or near a furnace i 108 having at least one burner 1 1 10,
  • the detector 1 102 can be disposed in any manner that allows it to measure the characteristic of interest.
  • the detector may be disposed within the furnace 1 108 (e.g., in the case of a pressure sensor) or outside of the furnace 1 108 (e.g., in the case of a video camera for recording the flame).
  • the converter 1104 is coupled to the detector 1 102 and is configured to receive the signal from the detector 1 102 and convert the signal into a time-varying signal spectrum. As discussed above, the converter can implement this functionality in a number of ways including, for example, by using a short- time Fourier transform.
  • the instability detector 1106 is coupled to the converter and is configured to receive the time-varying signal spectrum from the converter 1 104 and detect an instability based on the time-varying signal spectrum.
  • the instability detector 1 108 can include an instability indicator generator 1 1 12 that is configured to determine an instability indicator in accordance with the disclosed subject matter. Additional functional units can be used to perform other functions of the method as disclosed herein.
  • the converter 1 104, the instability detector 1 106, the instability indicator generator 1 1 12, and other functional units of the instability detection system 1 100 can be implemented in a variety of ways as known in the art.
  • each of the functional units can be implemented using an integrated single processor.
  • the each functional unit can be implemented on a separate processor. Therefore, the instability detection system 1 100 can be implemented using at least one processor and/or one or more processors.
  • the at least one processor comprises one or more circuits.
  • the one or more circuits can be designed so as to implement the disclosed subject matter using hardware only.
  • the processor can be designed to can out the instructions specified by computer code stored in a hard drive, a removable storage medium, or any other storage media.
  • Such non-transitory computer readable media can store instructions th t, upon execution, cause the at least one processor to perform the methods in accordance with the disclosed subject matter.
  • the system can further include at least one burner 1 1 10.
  • the at least one burner 1 1 10 can be a part of a furnace 1 108.
  • the term "furnace,” as used herein, refers to a wide variety of equipment that includes at least one burner, including, for example, industrial furnaces, fired heaters, and boilers.
  • the furnace 1 108 can be located at a refinery or similar location.
  • the at least one burner 1 1 10 or another functional element of the furnace 1 108 e.g., a steam injector
  • the corrective action processor can include one or more processors comprising one or more circuits as discussed above.
  • the instability detection system 1 100 can further include additional components in accordance with the disclosed subject matter.
  • the system can include an alarm coupled to the instability detector that is activated when an instability is detected.
  • the alarm can be, for example, a siren, a flashing light, an alarm on a computer console (preferably a manned distributed control console), or any other alarm.
  • the invention can include one or more of the following embodiments
  • Embodiment 1 A method for detecting an instability associated with at least one burner based on the spectral entropy of a signal measuring a characteristic of the at least one burner.
  • Embodiment 2 A method for detecting an instability associated with at least one burner, comprising: obtaining a signal from a detector, the detector measuring at least one characteristic associated with the at least one burner; converting, using at least one processor, the signal into a time-varying signal spectrum; and detecting, based at least in part on the time-varying signal spectrum, an instability associated with the at least one burner.
  • Embodiment 3 The method of Embodiment 1 or Embodiment 2, wherein the instability is detected based on a single channel of data.
  • Embodiment 4 The method of Embodiment 1, 2, or 3, wherein the characteristic comprises a pressure metric.
  • Embodiment 5 The method of any of the foregoing embodiments wherein the detector comprises a dynamic pressure sensor.
  • Embodiment 6 The method of any of the foregoing embodiments, wherein the characteristic comprises a fluctuation metric.
  • Embodiment 7 The method of any of the foregoing embodiments, wherein the detector comprises a device that captures video frames.
  • Embodiment 8 The method of any of the foregoing embodiments, further comprising converting a video frame into a single scalar to produce a scalar times series signal
  • Embodiment 9 The method of any of the foregoing embodiments, wherein the characteristic comprises a vibration metric.
  • Embodiment 10 The method of any of the foregoing embodiments, wherein the detector comprises an accelerometer.
  • Embodiment 1 1 The method of any of the foregoing Embodiments, wherein the time- varying signal spectrum comprises a spectrogram.
  • Embodiment 12 The method of any of the foregoing Embodiments, wherein converting the signal comprises using time-frequency analysis.
  • Embodiment 13 The method of Embodiment 12, wherein the time- frequency analysis comprises a short-time Fourier transform.
  • Embodiment 14 The method of any of the foregoing Embodiments, wherein detecting an instability associated with the at least one burner comprises computing a spectral entropy based on the time -varying signal spectrum.
  • Embodiment 15 The method of any of the foregoing Embodiments, wherein detecting an instability associated with the at least one burner comprises determining an instability indicator.
  • Embodiment 16 The method of Embodiment 15, wherein the instability def ector corresponds to a probability of instability.
  • Embodiment 17 The method of Embodiments 15 or 16, wherein determining an instability indicator comprises converting the time-varying signal spectrum into the instability indicator.
  • Embodiment 18 The method of Embodiment 17, wherein converting the time-varying signal spectrum comprises normalizing the time-varying signal spectrum.
  • Embodiment 19 The method of Embodiments 7 or 18, wherem converting the time-varying signal spectrum comprises calculating a probability mass function based on the time- varying signal spectrum.
  • Embodiment 20 The method of Embodiments 17, 1 8, or 19, wherein converting the time-varying signal spectrum comprises computing a Shannon entropy of a probability mass function as a function of time.
  • Embodiment 21 The method of Embodiments 15, 16, 17, 18, 19, or 20, wherein the instability indicator comprises an inverse of a spectral entropy.
  • Embodiment 22 The method of any of the foregoing embodiments, wherein detecting an instability comprises comparing the instability indicator to a threshold value.
  • Embodiment 23 The method of Embodiment 22 , wherein the instability is detected when the instability indicator exceeds a threshold value.
  • Embodiment 24 The method of any of the foregoing Embodiments, further comprising providing an alarm when the instability is detected.
  • Embodiment 25 The method of any of the foregoing Embodiments, further comprising taking corrective action when the instability is detected.
  • Embodiment 26 The method of Embodiment 25, wherem the corrective action comprises adjusting an operating property of the at least one burner.
  • Embodiment 27 The method of Embodiment 25 or 26, wherein the corrective action comprises disabling the at least one burner.
  • Embodiment 28 The method of any of the foregoing Embodiments, wherein the at least one burner comprises a plurality of burners.
  • Embodiment 29 The method of any of the foregoing Embodiments, wherein the detector measures at least one characteristic of the plurality of burners as a whole.
  • Embodiment 30 The method of any of the foregoing Embodiments, wherein detecting an instability comprises detecting an instability of the plurality of burners as a whole.
  • Embodiment 31 A system for detecting an instability associated with at least one burner, comprising a detector configured to obtain a signal measuring at least one characteristic associated with at least one burner, a converter, coupled to the detector, comprising at least one processor and configured to receive the signal from the detector and convert the signal into a time-varying signal spectrum, and an instability detector, coupled to the converter, comprising the at least one processor and configured to detect an instability associated with the at least one burner based at least in pari on the time-varying signal spectrum.
  • Embodiment 32 The system of Embodiment 31, configured for use in accordance with any of the methods above (i.e., the methods of Embodiments 1 through 27).

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Abstract

L'invention porte sur des systèmes et sur des procédés pour détecter une instabilité associée à au moins un brûleur. Un détecteur mesure un signal associé à une caractéristique du ou des brûleurs. Le signal est converti en un spectre de signal variant dans le temps à l'aide d'au moins un processeur. Une instabilité est détectée sur la base, au moins en partie, du spectre de signal variant dans le temps. L'instabilité peut être détectée sur la base d'un indicateur d'instabilité calculé sur la base, au moins en partie, du spectre de signal variant dans le temps. Un seuil peut être associé à l'indicateur d'instabilité, de telle sorte qu'une instabilité est détectée quand l'indicateur d'instabilité est supérieur au seuil.
PCT/US2013/074057 2012-12-17 2013-12-10 Détecteur d'instabilité de flamme WO2014099477A1 (fr)

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