FLAME INSTABILITY DETECTOR

Abstract

Systems and methods for detecting an instability associated with at least one burner are disclosed. A detector measures a signal associated with a characteristic of the at least one burner. The signal is converted to a time-varying signal spectrum using at least one processor. An instability is detected based at least in part on the time-varying signal spectrum. The instability can be detected based on an instability indicator calculated based at least in part on the time-varying signal spectrum. A threshold can be associated with the instability indicator such that an instability is detected when the instability indicator is greater than the threshold.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application Ser. No. 61/737,878 filed Dec. 17, 2012, which is herein incorporated by reference in its entirety.

FIELD

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.

BACKGROUND

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.

In addition, industrial furnaces can contribute substantially to total refinery NOx emissions. 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. However, 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.

Traditionally, flame monitoring in industrial furnaces has been accomplished through visual inspection, analyzer-based monitoring, and photodetector devices. Visual inspection can readily identify flame blowoff, but is generally inadequate for identifying instability prior to blowoff. Analyzer-based monitoring typically has long latency and lacks the dynamic coverage needed for reliable detection. Photodetector devices such as flame eye are mainly burner based and expensive for wide-deployment. Furthermore, the practical use of line-of-sight techniques, such as Tunable Diode Laser-based monitoring, can be restricted due to their design.

New flame monitoring strategies have been introduced, but are limited in various ways. For example, variance-based approaches have been proposed, but are limited due to their low output signal-to-noise ratio, which requires an operator to choose between early detection and a low false positive rate. In addition, draft pressure fluctuation approaches have been reported in the past, but these techniques have been limited to a specific frequency range.

SUMMARY

The purpose and advantages of the present application will be set forth in and apparent from the description that follows, as well as will be learned by practice of the disclosed subject matter. Additional advantages of the disclosed subject matter will be realized and attained by the method and apparatus particularly pointed out in the written description and claims hereof, as well as from the appended drawings.

To achieve these and other advantages and in accordance with the purpose of the application, as embodied and broadly described, 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.

For example, 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.

In accordance with one embodiment of the disclosed subject matter, 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.

In accordance with one representative embodiment of the disclosed subject matter, 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.

In accordance with another embodiment of the disclosed subject matter, an instability indicator can be used to detect an instability. The instability indicator can correspond to a probability of instability. Additionally or alternatively, 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. For example, the time-varying signal spectrum can be normalized to obtain a probability mass 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 mass function can then be used as an instability indicator.

As disclosed herein, the instability indicator can be compared to a threshold value. An instability is detected when the instability indicator exceeds a threshold value.

In accordance with another embodiment, an alarm is provided when an instability is detected. Corrective action can be taken when the instability is taken. For example, the corrective action can include adjusting an operating property of the at least one burner or disabling the at least one burner.

Also disclosed herein is a system to detect an instability associated with 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.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 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.

FIG. 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.

FIG. 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.

FIG. 4 is a series of video frames showing the flames of three burners over time. The video frame rate for the video frames in FIG. 4 is around 6.4 frames per second which, with the oscillation cycle having 11 frames, leads to an approximately 1.72 second oscillation cycle, or equivalently 0.58 Hz peak frequency.

FIG. 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.

FIG. 6 is a graph of a scalar time series calculated based on a series of video frames in accordance with the disclosed subject matter.

FIG. 7A 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.

FIG. 7B is a simplified version of FIG. 7A and is presented for purposes of explanation only.

FIG. 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.

FIG. 9 is a flow chart describing a representative embodiment of a method for determining an instability indicator in accordance with the disclosed subject matter.

FIG. 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.

FIG. 11 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

DETAILED DESCRIPTIONS OF THE PREFERRED EMBODIMENTS

Overview

Generally, 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. Additionally, 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.

Reference will now be made in detail to representative embodiments of the disclosed subject matter, examples of which are illustrated in the accompanying drawings. The methods and systems disclosed herein will be described in conjunction with each other for clarity.

With reference to FIG. 1, 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. (See 102) Stable combustion generates more or less random broadband pressure fluctuations. In contrast, flame instability, as represented by the oscillating phase, is typically coherent, as manifested by harmonic pressure oscillations. (See 104) The final phase is blowoff. (See 106)

Accordingly, 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. Although 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. Moreover, while 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. For example, 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.

DEFINITIONS

In the discussion herein, 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.

In the discussion herein, the term “time-varying signal spectrum” refers to the characteristics of the frequency over time. For example, in one embodiment that 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.

In the discussion herein, the term “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 that provides updated pressure measurements based on the sampling rate of the sensor).

In the discussion herein, the term “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. For example, the spectral entropy can be the Shannon entropy of the time-varying signal spectrum.

As used herein, 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.

Detectors

With reference to FIG. 2, an exemplary method of detecting an instability is shown. First, a signal is obtained from a detector that measures a characteristic associated with the burner. (See 202). The detector can be disposed in any manner that allows it to measure the characteristic of interest. For example, 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.

Many characteristics of the burner can be measured by the detector without departing from the scope of the disclosed subject matter. For example, 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. FIG. 3 illustrates an exemplary draft pressure measurement as a function of time as the flame approaches blowoff.

In another embodiment, 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. For example, with reference to FIG. 4, the detector captures video frames 402-424. Each of the video frames in FIG. 4 shows the flames associated with three burners at various times.

To convert the series of video frames into a time-varying signal spectrum, the series of video frames are converted into a scalar time series signal, e.g., each video frame is converted into a single value that can be plotted against time. In one embodiment, a video frame can be converted into a single value based on the intensity associated with each pixel. For example, and with reference to FIG. 5, the intensity for each pixel in a first video frame (t=0) can be measured (See 502). The intensity for each pixel in a second video frame (t=1) can also be measured (See 504). The first video frame immediately precedes the second video frame (i.e., the first and second video frames are consecutive samples). For each pixel, 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). The magnitude of the fluctuation index can then be plotted at time t=1 as part of the scalar time series signal. This process can be repeated for each subsequent frame, or selected subsequent frames as desired. With reference to FIG. 6, a scalar time series signal obtained from a series of video frames is shown.

Additionally or alternatively, the characteristic can be a vibration metric, such as the oscillation of the furnace piping. The associated detector can be an accelerometer, with measurements processed accordingly.

Other characteristics and detectors can also be used. For example, 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.

Time-Varying Signal Spectrum

The signal obtained from the detector describes the measured characteristic as a function of time. With further reference to FIG. 2, 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.

Reference will now be made to a representative method of converting the signal into the time-varying signal spectrum. Given a measured signal x(t), its time-frequency distribution y(t,f) can be computed using the following general form:

y(t,f)=F_τ→f[G(t,τ)*_τK_x(t,τ)]  (1)

where F_τ→f denotes the Fourier transform from delay to frequency, K_x(t, τ)=x(t+0.5τ)x(t−0.5τ), and G(t, τ) is a kernel function. For example, in the case of a short-time Fourier transform, G(t, τ)=h(t+0.5τ)h(t−0.5τ) for some window function. Typically the window function h(τ) 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.

More generally, the signal can be 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. For example, a spectrogram obtained in accordance with the disclosed subject matter is shown in FIG. 7A. A rough approximation of the spectrogram is shown in FIG. 7B highlighting the time trajectory of the peak spectral component. The representation in FIG. 7B is shown for purposes of explanation only. As illustrated in FIG. 7B, 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. As used herein, the term “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. For example, with further reference to FIG. 3, it can be seen that the amplitude of the draft pressure signal increases as blowoff approaches. Finally, the spectrogram shows that the signal does not have a frequency after blowoff at 706. Although the disclosed subject matter is not limited to any particular theory of operation, 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.

In another embodiment of the disclosed subject matter, the time-varying signal spectrum can be represented as the root mean square vibration measurement on furnace piping measured by an accelerometer. In one embodiment, the signal measured by the accelerometer can be filtered (e.g., using a low pass filter) before the root mean square representation is calculated. FIG. 8 illustrates an exemplary root mean square (RMS) plot of the vibration measurement on furnace piping as the flame approaches blowoff.

Instability Detector

With reference now to FIG. 2, the disclosed subject matter further includes detecting, based at least in part on the time-varying signal spectrum, an instability associated with 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.

With reference to FIG. 9, an exemplary method of detecting an instability is described. The time-varying signal spectrum is normalized by the sum of the magnitude of all the spectral components (See 902):

$\begin{array}{cc}\hat{y}\left(t,f_k\right)=\frac{y\left(t,f_k\right)}{\sum _{k=0}^{K-1}y\left(t,f_k\right)}& \left(2\right)\end{array}$

where y(t,fk) is the estimated signal spectrum at frequency fk and is non-negative. Variations of this normalization in terms of spectral sub-bands instead of the whole frequency band can be used as known in the art.

With further reference to FIG. 9, a probability mass function in frequency can be derived based on the normalized time-varying signal spectrum (See 904):

P_k(t)=ŷ(t,f_k)  (3)

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.

With further reference to FIG. 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:

$\begin{array}{cc}s\left(t\right)=-\sum _{k=0}^{K-1}P_k\left(t\right)\ln P_k\left(t\right)& \left(4\right)\end{array}$

As discussed above, stable combustion generally generates random noises that are relatively flat over a broad range of time, which results in a higher entropy value. In contrast, unstable combustion leads to oscillations with clear spectral peaks, resulting in lower spectral entropy. Therefore, the inverse of the spectral entropy can be used as an instability indicator. (See 908) An instability indicator in accordance with the disclosed subject matter is shown in FIG. 10 (See 1002).

The use of an instability detector in accordance with the disclosed subject matter can provide an improved signal-to-noise ratio and sensitivity of the instability detector. For example, FIG. 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 FIG. 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 positive 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 (SNR) 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.

As previously noted and with further reference to FIG. 7B, 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. For example, an operating property of the burner can be adjusted. For example, the amount of steam injected into the furnace can be decreased until the instability is resolved. Similarly, the burner can be disabled, which can prevent an explosion and allow repairs and/or maintenance to be provided to the furnace. In the case of a severe instability with an unknown root cause, the at least one burner can be shut down.

Instability Detection System

For purpose of explanation and illustration, and not limitation, an exemplary embodiment of the system for detecting an instability associated with a burner in accordance with the application is shown in FIG. 11. The instability detection system 1100 can include a detector 1102, a converter 1104, and an instability detector 1106.

The detector 1102 is disposed within or near a furnace 1108 having at least one burner 1110. The detector 1102 can be disposed in any manner that allows it to measure the characteristic of interest. For example, the detector may be disposed within the furnace 1108 (e.g., in the case of a pressure sensor) or outside of the furnace 1108 (e.g., in the case of a video camera for recording the flame).

The converter 1104 is coupled to the detector 1102 and is configured to receive the signal from the detector 1102 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 1104 and detect an instability based on the time-varying signal spectrum. The instability detector 1108 can include an instability indicator generator 1112 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 1104, the instability detector 1106, the instability indicator generator 1112, and other functional units of the instability detection system 1100 can be implemented in a variety of ways as known in the art. For example, each of the functional units can be implemented using an integrated single processor. Alternatively, the each functional unit can be implemented on a separate processor. Therefore, the instability detection system 1100 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. Alternatively, the processor can be designed to carry 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 that, 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 1110. The at least one burner 1110 can be a part of a furnace 1108. 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 1108 can be located at a refinery or similar location. The at least one burner 1110 or another functional element of the furnace 1108 (e.g., a steam injector) can be coupled to the instability detector 1106 and a corrective action processor in order to automatically institute a corrective action when an instability is detected. The corrective action processor can include one or more processors comprising one or more circuits as discussed above.

The instability detection system 1100 can further include additional components in accordance with the disclosed subject matter. For example, 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.

ADDITIONAL EMBODIMENTS

Additionally or alternately, 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 11

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 detector 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 17 or 18, wherein 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, 18, 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, wherein 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 part 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).

While the present application is described herein in terms of certain preferred embodiments, those skilled in the art will recognize that various modifications and improvements may be made to the application without departing from the scope thereof. Thus, it is intended that the present application include modifications and variations that are within the scope of the appended claims and their equivalents. Moreover, although individual features of one embodiment of the application may be discussed herein or shown in the drawings of one embodiment and not in other embodiments, it should be apparent that individual features of one embodiment may be combined with one or more features of another embodiment or features from a plurality of embodiments.

In addition to the specific embodiments claimed below, the application is also directed to other embodiments having any other possible combination of the dependent features claims below and those disclosed above. As such, the particular features presented in the dependent claims and disclosed above can be combined with each other in other manners within the scope of the application such that the application should be recognized as also specifically directed to other embodiments having any other possible combinations. Thus, the foregoing description of specific embodiments of the application has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the application to those embodiments disclosed.

Claims

1. A method for detecting an instability associated with a at least one burner, comprising:

obtaining a signal from a detector, the detector measuring at least one characteristic associated with a 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.

2. The method of claim 1, wherein the instability is detected based on a single channel of data.

3. The method of claim 1, wherein the characteristic comprises a pressure metric.

4. The method of claim 3, wherein the detector comprises a dynamic pressure sensor.

5. The method of claim 1, wherein the characteristic comprises a fluctuation metric.

6. The method of claim 5, wherein the detector comprises a device to capture video frames.

7. The method of claim 6, further comprising converting each video frame into a single scalar to produce a scalar time series signal.

8. The method of claim 1, wherein the characteristic comprises a vibration metric.

9. The method of claim 8, wherein the detector comprises an accelerometer.

10. The method of claim 1, wherein the time-varying signal spectrum comprises a spectrogram.

11. The method of claim 1, wherein the converting the signal comprises using a time-frequency analysis.

12. The method of claim 11, wherein the time-frequency analysis comprises a short-time Fourier transform.

13. The method of claim 1, wherein detecting an instability associated with the at least one burner comprises computing a spectral entropy based on the time-varying signal spectrum.

14. The method of claim 1, wherein detecting an instability associated with the at least one burner comprises determining an instability indicator.

15. The method of claim 14, wherein the instability indicator corresponds to a probability of instability.

16. The method of claim 14, wherein determining the instability indicator comprises converting the time-varying signal into the instability indicator.

17. The method of claim 16, wherein converting the time-varying signal spectrum comprises normalizing the time-varying signal spectrum.

18. The method of claim 17, wherein converting the time-varying signal spectrum further comprises calculating a probability mass function from the normalized time-varying signal spectra.

19. The method of claim 18, wherein converting the time-varying signal spectrum further comprises computing a Shannon entropy of the probability mass function as a function of time.

20. The method of claim 19, wherein the instability indicator comprises an inverse of the spectral entropy.

21. The method of claim 14, detecting an instability further comprises comparing the instability indicator to a threshold value.

22. The method of claim 21, wherein the instability is detected when the instability indicator exceeds the threshold value.

23. The method of claim 1, further comprising providing an alarm when the instability is detected.

24. The method of claim 1, further comprising taking corrective action when the instability is detected.

25. The method of claim 24, wherein the corrective action comprises adjusting an operating property of the at least one burner.

26. The method of claim 25, wherein the corrective action comprises disabling the at least one burner.

27. The method of claim 1, wherein the at least one burner comprises a plurality of burners.

28. The method of claim 27, wherein the detector measures the at least one characteristic of the plurality of burners as a whole.

29. The method of claim 28, wherein detecting the instability comprises detecting an instability associated with the plurality of burners as a whole.

30. A system for detecting an instability associated with a at least one burner, comprising:

a detector configured to obtain a signal measuring at least one characteristic associated with a 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 part on the time-varying signal spectrum.

31. The system of claim 30, configured for use in accordance with any of the methods described in claims 1 through 29.