Method and system for monitoring state of boiler burner

Abstract

The invention relates to the technical field of boiler combustors, and particularly discloses a state monitoring method and system for a boiler combustor, and the method comprises the steps: obtaining a boiler combustor fault event when a historical boiler combustor fault occurs, and building a combustor fault feature set according to the boiler combustor fault event; performing association rule mining on the combustor fault feature set based on an association rule algorithm to obtain a strong association rule of the combustor fault feature set; and determining the combustion state coefficient of the current combustor according to the strong association rule of the combustor fault feature set, and performing combustor state early warning according to the combustion state coefficient of the current combustor. According to the method, association rule mining is carried out on the multi-source data of the boiler burner, the fault state of the burner can be monitored in real time, the fault type can be analyzed, and the fault state monitoring level of the burner is effectively improved.

Description

Technical Field

[0001] This application relates to the technical field of boiler burners, and more specifically, to a method and system for monitoring the state of a boiler burner. Background Art

[0002] The pulverized coal burner is a main component of the combustion equipment of a coal-fired boiler. Its functions are: (1) conveying fuel and air into the furnace. (2) organizing the timely and sufficient mixing of fuel and air. (3) ensuring that the fuel ignites as soon as possible and stably after entering the furnace, and burns out quickly and completely. When pulverized coal burns, in order to reduce the heat required for ignition, the pulverized coal is quickly heated to make the pulverized coal reach the ignition temperature as soon as possible to achieve ignition as soon as possible. Therefore, the air volume required for pulverized coal combustion is divided into primary air and secondary air. The function of primary air is to send pulverized coal into the furnace and supply the oxygen required for the combustion of volatile matter in the initial ignition stage of pulverized coal. The secondary air is mixed in after the pulverized coal air flow ignites, and supplies the oxygen required for the combustion of coke and residual volatile matter in the coal to ensure complete combustion of the pulverized coal. Since the combustion process of pulverized coal in the furnace is complex and there are many influencing factors, the comprehensive action of multiple variables brings great difficulties to the fault diagnosis of boiler combustion; in the process of combustion diagnosis of the boiler, too much reliance is placed on the on-site experience of professional personnel and limited testing means, lacking reliable detection and diagnosis devices or methods to help technicians conduct quantitative analysis, and thus it is impossible to monitor the state of the burner in a timely manner, bringing potential safety hazards and economic losses to the operation of the boiler. Summary of the Invention

[0003] The present invention provides a method and system for monitoring the state of a boiler burner to solve the problem of low level of fault state monitoring of boiler burners in the prior art, including: Obtaining the boiler burner fault events when historical boiler burner faults occur, and establishing a burner fault feature set according to the boiler burner fault events; Performing association rule mining on the burner fault feature set based on the association rule algorithm to obtain strong association rules of the burner fault feature set; Determining the combustion state coefficient of the current burner according to the strong association rules of the burner fault feature set, and performing burner state early warning according to the combustion state coefficient of the current burner.

[0004] Further, the establishment of the burner fault feature set according to the boiler burner fault events includes: Obtaining the burner multi-source index data of the boiler burner fault event, where the burner multi-source index data includes operation sound data, combustion image data and operation temperature data; Obtaining the preset standard index data corresponding to each multi-source index data, and calculating the distance value between the multi-source index data and the corresponding preset standard index data; Based on the variation of the distance values between multi-source index data and corresponding preset standard index data, establish time-series data of distance value variation. Based on the time-series data of distance value variation corresponding to each multi-source index data, establish a burner fault feature set.

[0005] Further, calculating the distance value between the multi-source index data and the corresponding preset standard index data includes: Calculate the difference between the multi-source index data and the corresponding preset standard index data, and determine the initial distance value according to the difference between the multi-source index data and the corresponding preset standard index data; Obtain the variation of the multi-source index data, and draw a curve of the variation of the multi-source index data according to the variation of the multi-source index data; Calculate the absolute value of the slope between the current multi-source index data and the multi-source index data in the previous preset time period according to the curve of the variation of the multi-source index data, and perform normalization processing on the absolute value of the slope between the current multi-source index data and the multi-source index data in the previous preset time period to obtain a distance correction coefficient; Multiply the distance correction coefficient by the initial distance value to obtain the distance value between the current multi-source index data and the corresponding preset standard index data.

[0006] Further, mining the strong association rules of the burner fault feature set based on the association rule algorithm includes: Perform standardization processing on the time-series data of distance value variation and the corresponding fault types in the burner fault feature set to obtain a standardized burner fault feature set; Obtain the preset minimum support threshold and the preset minimum confidence threshold, and mine the burner fault feature set based on the association rule algorithm according to the preset minimum support threshold and the preset minimum confidence threshold to obtain multiple strong association rules.

[0007] Further, mining the burner fault feature set based on the association rule algorithm according to the preset minimum support threshold and the preset minimum confidence threshold to obtain multiple strong association rules includes: Obtain the feature item set of each fault type in the burner fault feature set, and divide the feature item set into a first leading item set and a first succeeding item set; Calculate the target support of the first leading item set and the first succeeding item set, and compare the target support with the preset minimum support threshold; When the target support is greater than or equal to the preset minimum support threshold, take the first leading item set and the first succeeding item set corresponding to the target support as frequent item sets; Determine the association rule set according to the frequent item sets, and determine the strong association rules of the burner fault feature set according to the association rule set.

[0008] Further, the strong association rules for determining the burner fault feature set according to the association rule set include: Determine the association rule set according to the frequent item set, and divide the association rule set into a second leading item set and a second subsequent item set; Calculate the target confidence of the second leading item set and the second subsequent item set, and the target lift of the second leading item set and the second subsequent item set; Compare the target confidence with the preset minimum confidence threshold, and at the same time, compare the target lift with 1; When the target confidence is greater than or equal to the minimum confidence threshold and the target lift is greater than 1, use the corresponding association rule as the strong association rule corresponding to the burner fault feature set.

[0009] Further, the determination of the combustion state coefficient of the current burner according to the strong association rules of the burner fault feature set includes: Obtain the time series data of the distance value change corresponding to the multi-source index data of the current burner, establish an association rule set according to the strong association rules, and calculate the correlation coefficient between the time series data of the distance value change corresponding to the current multi-source index data and the time series data of the distance value change in the association rule set; Determine the state weight and the corresponding fault probability according to the correlation coefficient between the time series data of the distance value change corresponding to the current multi-source index data and the time series data of the distance value change with strong association rules, and determine the burner state coefficient according to the state weight and the corresponding fault probability.

[0010] Further, the determination of the burner state coefficient according to the state weight and the corresponding fault probability includes: Determine the burner state coefficient according to the state weight and the corresponding fault probability based on the state coefficient calculation formula. The state coefficient calculation formula is specifically

[0011] where is the burner state coefficient, is the state weight corresponding to the time series data of the distance value change of the i-th strong association rule, is the number of time series data of the distance value change with strong association rules, is the fault probability, is the preset allowable fault probability, is the preset range coefficient, is the natural exponential function.

[0012] Further, the burner state warning according to the combustion state coefficient of the current burner includes: Obtain the preset standard burner state parameters, and calculate the difference between the current burner state parameters and the preset standard burner state parameters; Determine whether the difference between the current burner status parameter and the preset standard burner status parameter is greater than the first preset threshold. If the difference between the current burner status parameter and the preset standard burner status parameter is greater than the first preset threshold, set the first level as the warning level of the burner; If the difference between the current burner status parameter and the preset standard burner status parameter is less than or equal to the first preset threshold, determine whether the difference between the current burner status parameter and the preset standard burner status parameter is greater than the second preset threshold; If the difference between the current burner status parameter and the preset standard burner status parameter is greater than the second preset threshold, set the second level as the warning level of the burner; If the difference between the current burner status parameter and the preset standard burner status parameter is less than or equal to the second preset threshold, set the third level as the warning level of the burner.

[0013] To achieve the above object, the present invention also provides a status monitoring system for a boiler burner, including: A building module, configured to obtain boiler burner fault events when historical boiler burner faults occur, and establish a burner fault feature set according to the boiler burner fault events; An association module, configured to perform association rule mining on the burner fault feature set based on the association rule algorithm to obtain strong association rules of the burner fault feature set; A monitoring module, configured to determine the combustion status coefficient of the current burner according to the strong association rules of the burner fault feature set, and perform burner status warning according to the combustion status coefficient of the current burner.

[0014] The beneficial effects of the present invention are as follows: By applying the above technical solutions, the present invention performs association rule mining on multi-source data of the boiler burner, can monitor in time through the changes of multi-source data before a fault occurs, monitor the fault status of the burner in real time and analyze the fault type, and give a timely warning for the fault status level, effectively improving the fault status monitoring level of the burner. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present application. For those skilled in the art, without creative efforts, other drawings can also be obtained according to these drawings.

[0016] Figure 1 Shows the overall flowchart of a status monitoring method for a boiler burner proposed in an embodiment of the present invention; Figure 2 The structural schematic diagram of a state monitoring system for a boiler burner proposed by an embodiment of the present invention is shown. Specific embodiments

[0017] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0018] The embodiments of the present application provide a method for monitoring the state of a boiler burner, as Figure 1 shown, including: S101, obtaining the boiler burner fault events when historical boiler burner faults occur, and establishing a burner fault feature set according to the boiler burner fault events; In some embodiments of the present application, the establishing a burner fault feature set according to the boiler burner fault events includes: obtaining the burner multi-source index data of the boiler burner fault events, where the burner multi-source index data includes operation sound data, combustion image data, and operation temperature data; obtaining the preset standard index data corresponding to each multi-source index data, and calculating the distance value between the multi-source index data and the corresponding preset standard index data; establishing distance value change time series data according to the change situation of the distance value between the multi-source index data and the corresponding preset standard index data, and establishing a burner fault feature set according to the distance value change time series data corresponding to each multi-source index data.

[0019] In this embodiment, by collecting the burner multi-source index data during the fault events when historical boiler burner faults occur, obtaining the operation sound data, combustion image data, and operation temperature data through preprocessing the multi-source index data, calculating the distance value between each multi-source index data and the standard data by presetting the standard index data of each multi-source index data, and thus establishing a burner fault feature set according to the distance value change time series data, and establishing a fault feature set by combining the multi-source index data of historical burner faults, the real-time operation state of the burner can be analyzed more accurately.

[0020] In some embodiments of the present application, calculating the distance value between the multi-source metric data and the corresponding preset standard metric data includes: calculating the difference between the multi-source metric data and the corresponding preset standard metric data, and determining an initial distance value according to the difference between the multi-source metric data and the corresponding preset standard metric data; obtaining the change situation of the multi-source metric data, and drawing a change curve of the multi-source metric data according to the change situation of the multi-source metric data; calculating the absolute value of the slope between the current multi-source metric data and the multi-source metric data in the previous preset time period according to the change curve of the multi-source metric data, and performing normalization processing on the absolute value of the slope between the current multi-source metric data and the multi-source metric data in the previous preset time period to obtain a distance correction coefficient; multiplying the distance correction coefficient by the initial distance value to obtain the distance value between the current multi-source metric data and the corresponding preset standard metric data.

[0021] In this embodiment, the absolute value of the slope between the current multi-source metric data and the multi-source metric data in the previous preset time period is calculated through the change curve of the multi-source metric data, and the distance correction coefficient is obtained by normalizing the absolute value of the slope, so as to prevent misjudgment of faults caused by data fluctuations.

[0022] S102, performing association rule mining on the burner fault feature set based on the association rule algorithm to obtain strong association rules of the burner fault feature set; In some embodiments of the present application, performing association rule mining on the burner fault feature set based on the association rule algorithm to obtain strong association rules of the burner fault feature set includes: performing standardization processing on the time series data of the distance value change and the corresponding fault types in the burner fault feature set to obtain a standardized burner fault feature set; obtaining a preset minimum support threshold and a preset minimum confidence threshold, and performing mining on the burner fault feature set based on the association rule algorithm according to the preset minimum support threshold and the preset minimum confidence threshold to obtain multiple strong association rules.

[0023] In some embodiments of the present application, performing mining on the burner fault feature set based on the association rule algorithm according to the preset minimum support threshold and the preset minimum confidence threshold to obtain multiple strong association rules includes: obtaining the feature item set of each fault type in the burner fault feature set, and dividing the feature item set into a first leading item set and a first succeeding item set; calculating the target support degree of the first leading item set and the first succeeding item set, and comparing the target support degree with the preset minimum support threshold; when the target support degree is greater than or equal to the preset minimum support threshold, taking the first leading item set and the first succeeding item set corresponding to the target support degree as frequent item sets; determining an association rule set according to the frequent item sets, and determining strong association rules of the burner fault feature set according to the association rule set.

[0024] In some embodiments of the present application, the strong association rules for determining the burner fault feature set according to the association rule set include: determining the association rule set according to the frequent item set, and dividing the association rule set into a second leading item set and a second subsequent item set; calculating the target confidence of the second leading item set and the second subsequent item set and the target lift of the second leading item set and the second subsequent item set; comparing the target confidence with the preset minimum confidence threshold, and at the same time, comparing the target lift with 1; when the target confidence is greater than or equal to the minimum confidence threshold and the target lift is greater than 1, taking the corresponding association rule as the strong association rule corresponding to the burner fault feature set.

[0025] In this embodiment, based on the apriori association rule algorithm, strong association rules of the distance value change time series data and the corresponding fault types in the burner fault feature set are mined. By mining a large amount of data in the burner fault feature set, strong association rules are extracted to improve the fault monitoring efficiency of the burner.

[0026] S103, determining the combustion state coefficient of the current burner according to the strong association rules of the burner fault feature set, and performing burner state warning according to the combustion state coefficient of the current burner.

[0027] In some embodiments of the present application, the determining the combustion state coefficient of the current burner according to the strong association rules of the burner fault feature set includes: obtaining the distance value change time series data corresponding to the multi-source index data of the current burner, establishing an association rule set according to the strong association rules, and calculating the correlation coefficient between the distance value change time series data corresponding to the current multi-source index data and the distance value change time series data in the association rule set; determining the state weight and the corresponding fault probability according to the correlation coefficient between the distance value change time series data corresponding to the current multi-source index data and the distance value change time series data with strong association rules, and determining the burner state coefficient according to the state weight and the corresponding fault probability.

[0028] In some embodiments of the present application, the determining the burner state coefficient according to the state weight and the corresponding fault probability includes: determining the burner state coefficient according to the state weight and the corresponding fault probability based on the state coefficient calculation formula, and the state coefficient calculation formula is specifically

[0029] where, is the burner state coefficient, is the state weight corresponding to the i-th distance value change time series data with strong association rules, is the number of distance value change time series data with strong association rules, is the fault probability, is the preset allowable fault probability, is a preset range coefficient, is the natural exponential function.

[0030] In this embodiment, the corresponding failure probability is obtained through the confidence level of the strong association rule, and the state weight is obtained through the correlation coefficient between the time series data of the distance value corresponding to the current multi-source index data and the time series data of the distance value with strong association rules. Furthermore, the state coefficient of the burner is determined based on the state weight and the failure probability, realizing the precise monitoring of the failure state of the burner.

[0031] In some embodiments of the present application, the burner state warning based on the combustion state coefficient of the current burner includes: obtaining the preset standard burner state parameters, and calculating the difference between the current burner state parameters and the preset standard burner state parameters; determining whether the difference between the current burner state parameters and the preset standard burner state parameters is greater than a first preset threshold. If the difference between the current burner state parameters and the preset standard burner state parameters is greater than the first preset threshold, the first level is set as the warning level of the burner; if the difference between the current burner state parameters and the preset standard burner state parameters is less than or equal to the first preset threshold, it is determined whether the difference between the current burner state parameters and the preset standard burner state parameters is greater than a second preset threshold; if the difference between the current burner state parameters and the preset standard burner state parameters is greater than the second preset threshold, the second level is set as the warning level of the burner; if the difference between the current burner state parameters and the preset standard burner state parameters is less than or equal to the second preset threshold, the third level is set as the warning level of the burner.

[0032] Based on the same technical concept, as Figure 2 shown, the present invention also provides a state monitoring system for a boiler burner, including: A building module, configured to obtain the boiler burner failure events when the historical boiler burner fails, and establish a burner failure feature set according to the boiler burner failure events; an association module, configured to perform association rule mining on the burner failure feature set based on the association rule algorithm to obtain the strong association rules of the burner failure feature set; a monitoring module, configured to determine the combustion state coefficient of the current burner according to the strong association rules of the burner failure feature set, and perform burner state warning according to the combustion state coefficient of the current burner.

[0033] By applying the above technical solutions, the present invention obtains the boiler burner fault events when historical boiler burner faults occur, establishes a burner fault feature set according to the boiler burner fault events; performs association rule mining on the burner fault feature set based on the association rule algorithm to obtain the strong association rules of the burner fault feature set; determines the combustion state coefficient of the current burner according to the strong association rules of the burner fault feature set, and performs burner state early warning according to the combustion state coefficient of the current burner. The present invention performs association rule mining on the multi-source data of the boiler burner, can monitor the fault state of the burner in real time and analyze the fault type, and effectively improves the fault state monitoring level of the burner.

[0034] Through the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be implemented by hardware or by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various implementation scenarios of the present invention.

[0035] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A method for monitoring the state of a boiler burner, characterized in that: include: Obtain boiler burner failure events when historical boiler burner failures occur, and establish a burner failure feature set based on the boiler burner failure events; Based on the association rule algorithm, the association rule mining of the burner fault feature set is carried out to obtain the strong association rule of the burner fault feature set; The combustion state coefficient of the current burner is determined according to the strong association rule of the burner fault feature set, and the burner state warning is performed according to the combustion state coefficient of the current burner.

2. The method for monitoring the state of a boiler burner according to claim 1, characterized in that: The method of establishing a burner fault feature set according to a boiler burner fault event includes: Acquire burner multi-source indicator data of a boiler burner fault event, wherein the burner multi-source indicator data includes operation sound data, combustion image data and operation temperature data; Obtaining preset standard indicator data corresponding to each multi-source indicator data, and calculating the distance value between the multi-source indicator data and the corresponding preset standard indicator data; Distance value change time series data is established according to the distance value change between the multi-source indicator data and the corresponding preset standard indicator data, and a burner fault feature set is established according to the distance value change time series data corresponding to each multi-source indicator data.

3. The method for monitoring the state of a boiler burner according to claim 2, characterized in that: The calculating of the distance value between the multi-source indicator data and the corresponding preset standard indicator data includes: Calculate the difference between the multi-source indicator data and the corresponding preset standard indicator data, and determine the initial distance value according to the difference between the multi-source indicator data and the corresponding preset standard indicator data; Obtain the change of multi-source indicator data, and draw a multi-source indicator data change curve according to the change of the multi-source indicator data; Calculate the absolute value of the slope between the current multi-source indicator data and the multi-source indicator data in the previous preset period according to the multi-source indicator data change curve, and normalize the absolute value of the slope between the current multi-source indicator data and the multi-source indicator data in the previous preset period to obtain a distance correction coefficient; The distance correction coefficient is multiplied by the initial distance value to obtain the distance value between the current multi-source indicator data and the corresponding preset standard indicator data.

4. The method for monitoring the state of a boiler burner according to claim 1, characterized in that: The association rule mining of the burner fault feature set based on the association rule algorithm is performed to obtain the strong association rules of the burner fault feature set, including: Standardizing the distance value change time series data and the corresponding fault types in the burner fault feature set to obtain a standardized burner fault feature set; A preset minimum support threshold and a preset minimum confidence threshold are obtained, and the burner fault feature set is mined based on an association rule algorithm according to the preset minimum support threshold and the preset minimum confidence threshold to obtain multiple strong association rules.

5. The method for monitoring the state of a boiler burner according to claim 4, characterized in that: The burner fault feature set is mined based on the association rule algorithm according to the preset minimum support threshold and the preset minimum confidence threshold to obtain multiple strong association rules, including: Obtain a feature item set for each fault type in the burner fault feature set, and divide the feature item set into a first leading item set and a first successor item set; Calculate the target support of the first leading item set and the first successor item set, and compare the target support with the preset minimum support threshold; When the target support is greater than or equal to the preset minimum support threshold, the first leading item set and the first successor item set corresponding to the target support are taken as frequent item sets; The association rule set is determined according to the frequent item set, and the strong association rule of the burner fault feature set is determined according to the association rule set.

6. The method for monitoring the state of a boiler burner according to claim 5, characterized in that: The method of determining the strong association rule of the burner fault feature set according to the association rule set includes: Determine the association rule set according to the frequent item set, and divide the association rule set into the second leading item set and the second successor item set; Calculate the target confidence of the second leading item set and the second successor item set and the target lift of the second leading item set and the second successor item set; Compare the target confidence with the preset minimum confidence threshold, and at the same time, compare the target lift with 1; When the target confidence is greater than or equal to the minimum confidence threshold and the target lift is greater than 1, the corresponding association rule is used as the strong association rule corresponding to the burner fault feature set.

7. The method for monitoring the state of a boiler burner according to claim 1, characterized in that: The method of determining the combustion state coefficient of the current burner according to the strong association rule of the burner fault feature set includes: Obtain the distance value change time series data corresponding to the current burner multi-source indicator data, establish an association rule set according to the strong association rule, and calculate the correlation coefficient between the distance value change time series data corresponding to the current multi-source indicator data and the distance value change time series data in the association rule set; The state weight and the corresponding fault probability are determined according to the correlation coefficient between the distance value change time series data corresponding to the current multi-source indicator data and the distance value change time series data with strong association rules, and the burner state coefficient is determined according to the state weight and the corresponding fault probability.

8. The method for monitoring the state of a boiler burner according to claim 7, characterized in that: Determining the burner state coefficient according to the state weight and the corresponding failure probability includes: The burner state coefficient is determined based on the state coefficient calculation formula according to the state weight and the corresponding failure probability. The state coefficient calculation formula is specifically: in, is the burner state coefficient, is the state weight corresponding to the time series data of distance value change with strong association rules of the ith one, is the number of time series data with distance value changes that have strong association rules, is the failure probability, To preset the allowable failure probability, is the preset range coefficient, is a natural exponential function.

9. The method for monitoring the state of a boiler burner according to claim 8, characterized in that: The burner state warning is performed according to the current combustion state coefficient of the burner, including: Obtaining preset standard burner state parameters, and calculating the difference between the current burner state parameters and the preset standard burner state parameters; Determine whether the difference between the current burner state parameter and the preset standard burner state parameter is greater than a first preset threshold value, and if the difference between the current burner state parameter and the preset standard burner state parameter is greater than the first preset threshold value, set the first level as the warning level of the burner; If the difference between the current burner state parameter and the preset standard burner state parameter is less than or equal to the first preset threshold, then determine whether the difference between the current burner state parameter and the preset standard burner state parameter is greater than the second preset threshold; If the difference between the current burner state parameter and the preset standard burner state parameter is greater than a second preset threshold, the second level is set as the burner warning level; If the difference between the current burner state parameter and the preset standard burner state parameter is less than or equal to the second preset threshold, the third level is set as the early warning level of the burner.

10. A boiler burner condition monitoring system, characterized in that: include: Establishing a module for acquiring boiler burner failure events when historical boiler burner failures occur, and establishing a burner failure feature set according to the boiler burner failure events; An association module is used to mine association rules for the burner fault feature set based on an association rule algorithm to obtain strong association rules for the burner fault feature set; The monitoring module is used to determine the combustion state coefficient of the current burner according to the strong association rule of the burner fault feature set, and to issue a burner state warning according to the combustion state coefficient of the current burner.

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