Fault monitoring method for steam soot blowing system

Abstract

The invention discloses a fault monitoring method for a steam soot blowing system, and the method comprises the steps: collecting first historical operation data and second historical operation data, screening system state data and boiler state data, and generating an application evaluation value of the first historical operation data; determining standard operation data of the steam soot blowing system according to the application evaluation value, and obtaining a standard operation data set of each monitoring index according to correlation degrees of the standard operation data, the monitoring indexes and preset monitoring nodes; acquiring real-time operation data, comparing the real-time operation data with the standard operation data set of the corresponding monitoring index, and calculating a fault evaluation coefficient of each monitoring index according to a comparison result; whether the steam soot blowing system breaks down or not is judged according to the fault evaluation coefficient, if the steam soot blowing system breaks down, the fault reason is determined, an alarm signal is sent, the data processing amount is reduced, and the monitoring efficiency of the running state and the running efficiency of the steam soot blowing system and the fault monitoring accuracy are improved.

Description

Technical Field

[0001] The present application relates to the technical field of fault monitoring, and in particular to a fault monitoring method for a steam sootblowing system. Background Art

[0002] During the operation of the boiler, dust accumulation on the heating surface is a common problem. The dust accumulation will affect the heat transfer effect, causing the exhaust gas temperature to increase, thereby reducing the boiler efficiency. In severe cases, dust accumulation may also cause a superheater tube burst accident, posing a threat to the safe operation of the boiler. The steam soot blowing system uses the blowing effect of high-pressure steam to quickly and effectively remove the dust and slag on the heating surface.

[0003] In the prior art, the fault monitoring method of the steam sootblowing system basically adopts the setting of a pressure detection device for real-time monitoring and regular inspection of the system status. However, the operating efficiency of the steam sootblowing system and the boiler cannot be determined, and the faults come from multiple aspects. Relying solely on the pressure detection device will lead to fault monitoring loopholes. There are many system equipment and inspection is difficult. The amount of data collected in the later stage is large, which reduces the efficiency and accuracy of fault analysis. Summary of the invention

[0004] To solve the above technical problems, the present application provides a fault monitoring method for a steam sootblowing system, which determines the system status data and the boiler status data, calculates the application evaluation value of the corresponding historical operation data, obtains each monitoring indicator and the standard operation data set of the corresponding preset monitoring node according to the application evaluation value, generates a fault assessment coefficient of the corresponding monitoring indicator according to the comparison result of the real-time operation data and the standard operation data set, judges whether the steam sootblowing system is faulty according to the fault assessment coefficient, and if a fault occurs, determines the corresponding fault type according to the monitoring indicator and sends an alarm signal, thereby reducing the data processing volume, improving the monitoring efficiency of the operating status and operating efficiency of the steam sootblowing system, and the accuracy of fault monitoring.

[0005] In some embodiments of the present application, a fault monitoring method for a steam sootblowing system is provided, comprising:

[0006] Preset multiple monitoring indicators of the steam soot blowing system and preset monitoring nodes corresponding to the monitoring indicators;

[0007] Collecting the first historical operation data and the second historical operation data, filtering out the system status data in the first historical operation data and the boiler status data in the second historical operation data, and generating an application evaluation value corresponding to the first historical operation data according to the system status data and the boiler status data;

[0008] Determine the standard operation data of the steam soot blowing system according to the application evaluation value, and obtain the standard operation data set of each monitoring indicator according to the correlation between the standard operation data and the monitoring indicator and the preset monitoring node;

[0009] Obtain the real-time operation data of each monitoring indicator at the preset monitoring node, compare the real-time operation data with the standard operation data set of the corresponding monitoring indicator, and calculate the fault assessment coefficient of each monitoring indicator according to the comparison result;

[0010] The fault assessment coefficients of multiple monitoring indicators are used to determine whether the steam soot blowing system has a fault. If a fault occurs, the cause of the fault is determined and an alarm signal is sent.

[0011] In some embodiments of the present application, multiple monitoring indicators of the steam sootblowing system and preset monitoring nodes corresponding to the monitoring indicators are pre-set, including:

[0012] Obtain the historical fault maintenance log of the steam soot blowing system, and obtain the fault type, fault cause and corresponding fault characteristics of the steam soot blowing system according to the historical fault maintenance log;

[0013] Set multiple monitoring indicators according to different fault causes of the same fault type;

[0014] The corresponding historical characteristic fault data is determined according to the fault characteristics corresponding to each fault cause, and a preset monitoring node is set according to the source and collection method of the historical characteristic fault data. The preset monitoring node is provided with a corresponding monitoring device.

[0015] In some embodiments of the present application, filtering out the system status data in the first historical operation data and the boiler status data in the second historical operation data includes:

[0016] Acquire a historical operation log, wherein the historical operation log includes first historical operation data of the steam sootblowing system, first historical characteristic data of the steam sootblowing system, second historical operation data of the boiler, and second historical characteristic data of the boiler for multiple historical operation cycles;

[0017] Establish a time reference line according to the historical operation time of the same historical operation cycle, and set the collection time node on the time reference line based on the preset time interval;

[0018] Corresponding to each collection time node of the time reference line respectively, the first historical operation data and the second historical operation data of the same historical operation cycle are collected, and the first historical state coefficient of the corresponding collection time node is calculated according to the multiple first historical feature data of the corresponding historical operation cycle, and the second historical state coefficient of the corresponding collection time node is calculated according to the multiple second historical feature data of the corresponding historical operation cycle;

[0019] Perform a first change degree analysis on the first historical operation data and the first historical state coefficient mapped on the same time reference line, mark the acquisition time node whose first change degree of the first historical state coefficient is greater than the first preset change degree threshold, subtract the marked acquisition time node from the first historical operation data of the previous adjacent acquisition time node to obtain the first data change value of the corresponding first historical operation data, and set the first historical operation data whose first data change value is greater than the first preset data change threshold as the system state data;

[0020] A second change degree analysis is performed on the second historical operating data and the second historical state coefficient mapped on the same time reference line, and the acquisition time node whose second change degree of the second historical state coefficient is greater than the second preset change degree threshold is marked, and the marked acquisition time node is subtracted from the second historical operating data of the previous adjacent acquisition time node to obtain the second data change value of the corresponding second historical operating data, and the second historical operating data whose second data change value is greater than the second preset data change threshold is set as the boiler state data.

[0021] In some embodiments of the present application, the credibility of the corresponding system status data is generated according to the frequency of occurrence of each system status data in multiple time reference lines and the total number of time reference lines, and the system status data with a credibility less than a preset credibility threshold is eliminated;

[0022] Generate the credibility of the corresponding boiler status data according to the frequency of occurrence of each boiler status data in multiple time reference lines and the total number of time reference lines, and eliminate the boiler status data with a credibility less than a preset credibility threshold;

[0023] An application evaluation value of the first historical operation data corresponding to the historical operation period is generated for the remaining system state data and the boiler state data.

[0024] In some embodiments of the present application, generating an application evaluation value corresponding to the first historical operation data according to the system state data and the boiler state data includes:

[0025] Comparing the system status data with the corresponding preset standard system status data interval to obtain the number of system status data not in the preset standard system status data interval and the difference amount of the system status data not in the preset standard system status data interval;

[0026] Comparing the boiler status data with the corresponding preset standard boiler status data interval, obtaining the number of boiler status data not in the preset standard boiler status data interval and the difference amount of the boiler status data not in the preset standard boiler status data interval;

[0027] Generate an application evaluation value of first historical operation data of the same historical operation period according to the difference amount of system state data and the difference amount of boiler state data;

[0028] The calculation formula of the application evaluation value is:

[0029]

[0030] Wherein, Y is the application evaluation value, g is the application evaluation conversion coefficient, n is the number of system state data that are not in the preset standard system state data interval, |△ki| is the difference amount of the i-th system state data, ei is the weight coefficient of the i-th system state data difference amount, a1 is the first weight coefficient, m is the number of boiler state data that are not in the preset standard boiler state data interval, |△ps| is the difference amount of the s-th boiler state data, and a2 is the second weight coefficient;

[0031] The application evaluation values ​​of the first historical operation data of the plurality of historical operation cycles are sorted according to size, and the first historical operation data corresponding to the first ranked application evaluation value is set as the standard operation data.

[0032] In some embodiments of the present application, according to the correlation between the standard operation data and the monitoring indicator and the preset monitoring node, a standard operation data set for each monitoring indicator is obtained, including:

[0033] Generate a correlation coefficient based on the dependency and association between the standard operation data and the historical characteristic fault data corresponding to the fault characteristics of each monitoring indicator;

[0034] Determine the location information of the monitoring device corresponding to the standard operation data according to the acquisition source of the standard operation data;

[0035] A compensation coefficient is set according to the relative distance between the location information and a plurality of preset monitoring nodes in each monitoring indicator, and a correlation degree is generated according to the compensation coefficient and the correlation coefficient;

[0036] Pre-set relevance threshold;

[0037] If the correlation between the standard operating data and the monitoring indicator and the preset monitoring node is greater than the correlation threshold, the standard operating data is set as the standard operating data of the corresponding monitoring indicator, and the standard operating parameter set of the corresponding monitoring indicator is constructed based on the standard operating data of the same monitoring indicator.

[0038] In some embodiments of the present application, the compensation coefficient is set according to the relative distance between the location information and a plurality of preset monitoring nodes in each monitoring indicator, including:

[0039] Presetting a first preset relative distance interval, a second preset relative distance interval, a third preset relative distance interval and a fourth preset relative distance interval;

[0040] When the relative distance is within the first preset relative distance interval, setting the compensation coefficient to a fourth preset compensation coefficient;

[0041] When the relative distance is within the second preset relative distance interval, setting the compensation coefficient to a third preset compensation coefficient;

[0042] When the relative distance is within a third preset relative distance interval, setting the compensation coefficient to the second preset compensation coefficient;

[0043] When the relative distance is in a fourth preset relative distance interval, the compensation coefficient is set to the first preset compensation coefficient.

[0044] In some embodiments of the present application, the fault assessment coefficient of each monitoring indicator is calculated according to the comparison result, including:

[0045] Acquire real-time operation data of each monitoring indicator at a preset monitoring node, compare the real-time operation data with a standard operation data set of the corresponding monitoring indicator, and obtain a comparison result, wherein the comparison result includes an operation data difference between the real-time operation data and the corresponding standard operation data in the standard operation data set;

[0046] A fault assessment coefficient of the corresponding monitoring indicator is generated according to multiple operating data differences of the same monitoring indicator and the corresponding weight coefficient.

[0047] In some embodiments of the present application, judging whether a steam soot blowing system fails according to the fault assessment coefficients of multiple monitoring indicators, and if a fault occurs, determining the cause of the fault and sending an alarm signal includes:

[0048] Comparing the fault assessment coefficients of the multiple monitoring indicators with the preset fault assessment coefficient thresholds, if there is a fault assessment coefficient greater than the preset fault assessment coefficient threshold, it is determined that the steam soot blowing system has a fault;

[0049] Determine the cause of the fault according to the monitoring indicator corresponding to the fault assessment coefficient greater than the preset fault assessment coefficient threshold;

[0050] If there are multiple fault causes, the priority of the fault causes is set according to the coefficient difference that the fault assessment coefficient is greater than the preset fault assessment coefficient threshold and the influence degree of the corresponding fault cause, and the corresponding alarm signal is sent according to the priority order.

[0051] Compared with the prior art, the fault monitoring method for a steam sootblowing system in the embodiment of the present application has the following beneficial effects:

[0052] By determining the system status data and the boiler status data, the application evaluation value of the corresponding historical operation data is calculated, and each monitoring indicator and the standard operation data set of the corresponding preset monitoring node are obtained according to the application evaluation value. The fault assessment coefficient of the corresponding monitoring indicator is generated according to the comparison result of the real-time operation data and the standard operation data set. It is judged whether the steam soot blowing system is faulty according to the fault assessment coefficient. If a fault occurs, the corresponding fault type is determined according to the monitoring indicator and an alarm signal is sent, thereby reducing the data processing volume, improving the monitoring efficiency of the operating status and operating efficiency of the steam soot blowing system, and improving the accuracy of fault monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a flow chart of a fault monitoring method for a steam soot blowing system in a preferred embodiment of the present application. DETAILED DESCRIPTION

[0054] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application but are not intended to limit the scope of the present application.

[0055] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0056] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0057] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0058] like Figure 1As shown, a fault monitoring method for a steam soot blowing system according to a preferred embodiment of the present application includes:

[0059] Step S101: presetting a plurality of monitoring indicators of a steam sootblowing system and preset monitoring nodes corresponding to the monitoring indicators;

[0060] Step S102: collecting the first historical operation data and the second historical operation data, filtering out the system status data in the first historical operation data and the boiler status data in the second historical operation data, and generating an application evaluation value corresponding to the first historical operation data according to the system status data and the boiler status data;

[0061] Step S103: determining the standard operation data of the steam sootblowing system according to the application evaluation value, and obtaining the standard operation data set of each monitoring indicator according to the correlation between the standard operation data and the monitoring indicator and the preset monitoring node;

[0062] Step S104: obtaining real-time operation data of each monitoring indicator at a preset monitoring node, comparing the real-time operation data with a standard operation data set of the corresponding monitoring indicator, and calculating a fault assessment coefficient of each monitoring indicator according to the comparison result;

[0063] Step S105: judging whether a fault occurs in the steam sootblowing system according to the fault assessment coefficients of the plurality of monitoring indicators; if a fault occurs, determining the cause of the fault and sending an alarm signal.

[0064] In this embodiment, by determining the system status data and the boiler status data, the application evaluation value of the corresponding historical operation data is calculated, and each monitoring indicator and the standard operation data set of the corresponding preset monitoring node are obtained according to the application evaluation value. The fault assessment coefficient of the corresponding monitoring indicator is generated according to the comparison result of the real-time operation data and the standard operation data set. It is judged whether the steam soot blowing system is faulty according to the fault assessment coefficient. If a fault occurs, the corresponding fault type is determined according to the monitoring indicator and an alarm signal is sent, thereby reducing the data processing volume, improving the monitoring efficiency of the operating status and operating efficiency of the steam soot blowing system, and improving the accuracy of fault monitoring.

[0065] In some embodiments of the present application, multiple monitoring indicators of the steam sootblowing system and preset monitoring nodes corresponding to the monitoring indicators are pre-set, including:

[0066] Obtain the historical fault maintenance log of the steam soot blowing system, and obtain the fault type, fault cause and corresponding fault characteristics of the steam soot blowing system according to the historical fault maintenance log;

[0067] Set multiple monitoring indicators according to different fault causes of the same fault type;

[0068] The corresponding historical characteristic fault data is determined according to the fault characteristics corresponding to each fault cause, and a preset monitoring node is set according to the source and collection method of the historical characteristic fault data. The preset monitoring node is provided with a corresponding monitoring device.

[0069] In this embodiment, the historical fault inspection and maintenance log includes the historical fault types, historical fault times, historical fault causes, etc. of the steam soot blowing system. The corresponding fault characteristics are determined according to the historical fault causes. The fault characteristics refer to the characteristics that can indicate the corresponding fault causes. The fault types of the steam soot blowing system include motor overload, soot blower cannot be retracted, internal leakage, severe air leakage at the seal, etc. The fault causes of each fault type may be different. For example, the fault causes of the soot blower cannot be retracted include travel switch failure, control circuit failure, mechanical transmission part jamming, soot blowing gun head spot welding point is too high, etc. Each fault cause is set as a corresponding monitoring indicator. Therefore, each fault type includes multiple monitoring indicators, thereby ensuring the accuracy of fault monitoring of the steam soot blowing system.

[0070] In some embodiments of the present application, filtering out the system status data in the first historical operation data and the boiler status data in the second historical operation data includes:

[0071] Acquire a historical operation log, wherein the historical operation log includes first historical operation data of the steam sootblowing system, first historical characteristic data of the steam sootblowing system, second historical operation data of the boiler, and second historical characteristic data of the boiler for multiple historical operation cycles;

[0072] Establish a time reference line according to the historical operation time of the same historical operation cycle, and set the collection time node on the time reference line based on the preset time interval;

[0073] Corresponding to each collection time node of the time reference line respectively, the first historical operation data and the second historical operation data of the same historical operation cycle are collected, and the first historical state coefficient of the corresponding collection time node is calculated according to the multiple first historical feature data of the corresponding historical operation cycle, and the second historical state coefficient of the corresponding collection time node is calculated according to the multiple second historical feature data of the corresponding historical operation cycle;

[0074] Perform a first change degree analysis on the first historical operation data and the first historical state coefficient mapped on the same time reference line, mark the acquisition time node whose first change degree of the first historical state coefficient is greater than the first preset change degree threshold, subtract the marked acquisition time node from the first historical operation data of the previous adjacent acquisition time node to obtain the first data change value of the corresponding first historical operation data, and set the first historical operation data whose first data change value is greater than the first preset data change threshold as the system state data;

[0075] A second change degree analysis is performed on the second historical operating data and the second historical state coefficient mapped on the same time reference line, and the acquisition time node whose second change degree of the second historical state coefficient is greater than the second preset change degree threshold is marked, and the marked acquisition time node is subtracted from the second historical operating data of the previous adjacent acquisition time node to obtain the second data change value of the corresponding second historical operating data, and the second historical operating data whose second data change value is greater than the second preset data change threshold is set as the boiler state data.

[0076] In this embodiment, the second historical operating data refers to data in the operating data of the boiler that changes according to the operation of the steam soot blowing system, the first historical characteristic data refers to data that can represent the operating status of the steam soot blowing system, such as steam pressure and temperature, etc., the second historical characteristic data refers to data that can represent the thermal efficiency of the boiler and the coal consumption of the boiler unit after the steam soot blowing system blows the ash-accumulated heating surface of the boiler, such as the evaporation amount, air intake, coal intake, etc. of the boiler, the system status data refers to data in the historical operating data that is associated with the operating status of the steam soot blowing system, and the boiler status data refers to data in the operating data of the boiler that is associated with the soot blowing effect of the steam soot blowing system and represents the boiler status.

[0077] In this embodiment, multiple first change absolute values ​​are obtained by subtracting multiple first historical characteristic data from the corresponding first historical characteristic data of the previous adjacent historical operation cycle. The first historical state coefficient is obtained based on the difference between the multiple first change absolute values ​​and the first preset change absolute value threshold and the corresponding weight coefficient. The first preset change absolute value threshold refers to the maximum change absolute value threshold of the first historical characteristic data of the adjacent historical operation cycles during normal operation. The larger the first historical state coefficient, the better the operating state of the steam soot blowing system, and vice versa.

[0078] In this embodiment, multiple second change absolute values ​​are obtained by subtracting multiple second historical feature data from the corresponding second historical feature data of the previous adjacent historical operation cycle. The second historical state coefficient is obtained based on the difference between the multiple second change absolute values ​​and the second preset change absolute value threshold and the corresponding weight coefficient. The first and second preset change absolute value thresholds refer to the maximum change absolute value thresholds of the second historical feature data of adjacent historical operation cycles when the boiler is operating normally. The larger the second historical state coefficient, the better the thermal efficiency and operating state of the boiler, and vice versa.

[0079] In this embodiment, the system status data and boiler status data are obtained through the first historical operation data, the first historical characteristic data, the second historical operation data and the second historical characteristic data of multiple historical operation cycles, as well as the change degree analysis results, thereby laying a data foundation for the subsequent calculation of the application evaluation value of the historical operation data corresponding to the historical operation cycle, reducing the data processing volume and improving the accuracy of the standard operation data, thereby improving the fault monitoring efficiency and operation efficiency.

[0080] In some embodiments of the present application, the credibility of the corresponding system status data is generated according to the frequency of occurrence of each system status data in multiple time reference lines and the total number of time reference lines, and the system status data with a credibility less than a preset credibility threshold is eliminated;

[0081] Generate the credibility of the corresponding boiler status data according to the frequency of occurrence of each boiler status data in multiple time reference lines and the total number of time reference lines, and eliminate the boiler status data with a credibility less than a preset credibility threshold;

[0082] An application evaluation value of the first historical operation data corresponding to the historical operation period is generated for the remaining system state data and the boiler state data.

[0083] In this embodiment, the credibility of the system status data is set according to the ratio of the number of occurrences of system status data in multiple historical operating cycles and the total number of time reference lines corresponding to the historical operating cycles, and the credibility of the boiler status data is set according to the ratio of the number of occurrences of boiler status data in multiple historical operating cycles and the total number of time reference lines corresponding to the historical operating cycles, thereby determining the final system status data and boiler status data.

[0084] In this embodiment, by improving the accuracy of system status data and boiler status data, the monitoring accuracy of the operating status of the steam sootblowing system, the sootblowing effect, the boiler thermal efficiency, and the unit coal consumption is improved, thereby laying a foundation for the subsequent calculation of the application evaluation value of the corresponding historical operating data and the determination of the standard operating data, reducing the data processing volume, and improving the fault monitoring efficiency and timeliness, thereby ensuring the safe and efficient operation of the steam sootblowing system and the boiler.

[0085] In some embodiments of the present application, generating an application evaluation value corresponding to the first historical operation data according to the system state data and the boiler state data includes:

[0086] Comparing the system status data with the corresponding preset standard system status data interval to obtain the number of system status data not in the preset standard system status data interval and the difference amount of the system status data not in the preset standard system status data interval;

[0087] Comparing the boiler status data with the corresponding preset standard boiler status data interval, obtaining the number of boiler status data not in the preset standard boiler status data interval and the difference amount of the boiler status data not in the preset standard boiler status data interval;

[0088] Generate an application evaluation value of first historical operation data of the same historical operation period according to the difference amount of system state data and the difference amount of boiler state data;

[0089] The calculation formula of the application evaluation value is:

[0090]

[0091] Wherein, Y is the application evaluation value, g is the application evaluation conversion coefficient, n is the number of system state data that are not in the preset standard system state data interval, |△ki| is the difference amount of the i-th system state data, ei is the weight coefficient of the i-th system state data difference amount, a1 is the first weight coefficient, m is the number of boiler state data that are not in the preset standard boiler state data interval, |△ps| is the difference amount of the s-th boiler state data, and a2 is the second weight coefficient;

[0092] The application evaluation values ​​of the first historical operation data of the plurality of historical operation cycles are sorted according to size, and the first historical operation data corresponding to the first ranked application evaluation value is set as the standard operation data.

[0093] In this embodiment, the preset standard system state data interval and the preset standard boiler state data interval are set in advance and are obtained by simulation based on the optimal operating state of the steam sootblowing system, the optimal sootblowing effect and the optimal thermal efficiency of the boiler.

[0094] In this embodiment, based on the application evaluation values ​​of the historical operation data corresponding to the system status data and the boiler status data of the same historical operation cycle, the historical operation data corresponding to the first-ranked application evaluation value is set as the standard operation data. The standard operation data is the data that ensures the normal operation of the steam soot blowing system, achieves the optimal soot blowing effect, improves the thermal efficiency of the boiler, and ensures the safe and efficient operation of the steam soot blowing system and the boiler.

[0095] In this embodiment, by determining system status data and boiler status data and determining standard operation data according to application evaluation values, the data processing volume is reduced, and the fault monitoring efficiency and soot blowing effect of the steam soot blowing system are improved.

[0096] In some embodiments of the present application, according to the correlation between the standard operation data and the monitoring indicator and the preset monitoring node, a standard operation data set for each monitoring indicator is obtained, including:

[0097] Generate a correlation coefficient based on the dependency and association between the standard operation data and the historical characteristic fault data corresponding to the fault characteristics of each monitoring indicator;

[0098] Determine the location information of the monitoring device corresponding to the standard operation data according to the acquisition source of the standard operation data;

[0099] A compensation coefficient is set according to the relative distance between the location information and a plurality of preset monitoring nodes in each monitoring indicator, and a correlation degree is generated according to the compensation coefficient and the correlation coefficient;

[0100] Pre-set relevance threshold;

[0101] If the correlation between the standard operating data and the monitoring indicator and the preset monitoring node is greater than the correlation threshold, the standard operating data is set as the standard operating data of the corresponding monitoring indicator, and the standard operating parameter set of the corresponding monitoring indicator is constructed based on the standard operating data of the same monitoring indicator.

[0102] In this embodiment, the dependency relationship refers to the degree to which the change of the historical operating data of the parameters corresponding to the standard operating data causes the change of the historical characteristic fault data, and the association relationship refers to the degree of mutual influence between the historical operating data of the parameters corresponding to the standard operating data and the historical characteristic fault data. The greater the degree of mutual influence, the greater the relationship coefficient. For example, when the historical operating data of the parameters corresponding to the standard operating data changes, causing the historical characteristic fault data of the same historical operating cycle to change, and the change amount exceeds the preset change threshold, then there is a dependency relationship between the standard operating data and the historical characteristic fault data, and the difference between the change amount and the preset change threshold sets the dependency coefficient. The larger the difference, the larger the dependency coefficient. The correlation coefficient = association coefficient * 0.6 + dependency coefficient * 0.4.

[0103] In this embodiment, the relative distance is obtained by averaging the distances between the location information of the monitoring device corresponding to the standard operation data and a plurality of preset monitoring nodes in the same monitoring indicator, and the correlation degree = correlation coefficient * compensation coefficient.

[0104] In some embodiments of the present application, the compensation coefficient is set according to the relative distance between the location information and a plurality of preset monitoring nodes in each monitoring indicator, including:

[0105] Presetting a first preset relative distance interval, a second preset relative distance interval, a third preset relative distance interval and a fourth preset relative distance interval;

[0106] When the relative distance is within the first preset relative distance interval, setting the compensation coefficient to a fourth preset compensation coefficient;

[0107] When the relative distance is within the second preset relative distance interval, setting the compensation coefficient to a third preset compensation coefficient;

[0108] When the relative distance is within a third preset relative distance interval, setting the compensation coefficient to the second preset compensation coefficient;

[0109] When the relative distance is in a fourth preset relative distance interval, the compensation coefficient is set to the first preset compensation coefficient.

[0110] In this embodiment, the first preset relative distance interval < the second preset relative distance interval < the third preset relative distance interval < the fourth preset relative distance interval, 0.75 < the first preset compensation coefficient < the second preset compensation coefficient < 1 < the third preset compensation coefficient < the fourth preset compensation coefficient < 1.25.

[0111] In some embodiments of the present application, the fault assessment coefficient of each monitoring indicator is calculated according to the comparison result, including:

[0112] Acquire real-time operation data of each monitoring indicator at a preset monitoring node, compare the real-time operation data with a standard operation data set of the corresponding monitoring indicator, and obtain a comparison result, wherein the comparison result includes an operation data difference between the real-time operation data and the corresponding standard operation data in the standard operation data set;

[0113] A fault assessment coefficient of the corresponding monitoring indicator is generated according to multiple operating data differences of the same monitoring indicator and the corresponding weight coefficient.

[0114] In this embodiment, the real-time operating data is compared with the corresponding standard operating data to obtain multiple operating data differences. The fault assessment coefficient is obtained according to the operating data difference and the weight coefficient of the corresponding standard operating data, so as to judge whether the corresponding monitoring indicator has a fault and the corresponding fault type, thereby improving the fault monitoring efficiency of the steam soot blowing system, and timely discovering faults, fault causes and fault types, thereby avoiding causing greater losses.

[0115] In some embodiments of the present application, judging whether a steam soot blowing system fails according to the fault assessment coefficients of multiple monitoring indicators, and if a fault occurs, determining the cause of the fault and sending an alarm signal includes:

[0116] Comparing the fault assessment coefficients of the multiple monitoring indicators with the preset fault assessment coefficient thresholds, if there is a fault assessment coefficient greater than the preset fault assessment coefficient threshold, it is determined that the steam soot blowing system has a fault;

[0117] Determine the cause of the fault according to the monitoring indicator corresponding to the fault assessment coefficient greater than the preset fault assessment coefficient threshold;

[0118] If there are multiple fault causes, the priority of the fault causes is set according to the coefficient difference that the fault assessment coefficient is greater than the preset fault assessment coefficient threshold and the influence degree of the corresponding fault cause, and the corresponding alarm signal is sent according to the priority order.

[0119] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present application. These improvements and substitutions should also be regarded as the scope of protection of the present application.

Claims

1. A fault monitoring method for a steam sootblowing system, characterized in that: include: Preset multiple monitoring indicators of the steam soot blowing system and preset monitoring nodes corresponding to the monitoring indicators; Collecting the first historical operation data and the second historical operation data, filtering out the system status data in the first historical operation data and the boiler status data in the second historical operation data, and generating an application evaluation value corresponding to the first historical operation data according to the system status data and the boiler status data; Determine the standard operation data of the steam soot blowing system according to the application evaluation value, and obtain the standard operation data set of each monitoring indicator according to the correlation between the standard operation data and the monitoring indicator and the preset monitoring node; Obtain the real-time operation data of each monitoring indicator at the preset monitoring node, compare the real-time operation data with the standard operation data set of the corresponding monitoring indicator, and calculate the fault assessment coefficient of each monitoring indicator according to the comparison result; The fault assessment coefficients of multiple monitoring indicators are used to determine whether the steam soot blowing system has a fault. If a fault occurs, the cause of the fault is determined and an alarm signal is sent.

2. The fault monitoring method for a steam sootblowing system according to claim 1, characterized in that: Multiple monitoring indicators of the steam sootblowing system and preset monitoring nodes corresponding to the monitoring indicators are pre-set, including: Obtain the historical fault maintenance log of the steam soot blowing system, and obtain the fault type, fault cause and corresponding fault characteristics of the steam soot blowing system according to the historical fault maintenance log; Set multiple monitoring indicators according to different fault causes of the same fault type; The corresponding historical characteristic fault data is determined according to the fault characteristics corresponding to each fault cause, and a preset monitoring node is set according to the source and collection method of the historical characteristic fault data. The preset monitoring node is provided with a corresponding monitoring device.

3. The fault monitoring method for a steam sootblowing system according to claim 2, characterized in that: Filter out the system status data in the first historical operation data and the boiler status data in the second historical operation data, including: Acquire a historical operation log, wherein the historical operation log includes first historical operation data of the steam sootblowing system, first historical characteristic data of the steam sootblowing system, second historical operation data of the boiler, and second historical characteristic data of the boiler for multiple historical operation cycles; Establish a time reference line according to the historical operation time of the same historical operation cycle, and set the collection time node on the time reference line based on the preset time interval; Corresponding to each collection time node of the time reference line respectively, the first historical operation data and the second historical operation data of the same historical operation cycle are collected, and the first historical state coefficient of the corresponding collection time node is calculated according to the multiple first historical feature data of the corresponding historical operation cycle, and the second historical state coefficient of the corresponding collection time node is calculated according to the multiple second historical feature data of the corresponding historical operation cycle; Perform a first change degree analysis on the first historical operation data and the first historical state coefficient mapped on the same time reference line, mark the acquisition time node whose first change degree of the first historical state coefficient is greater than the first preset change degree threshold, subtract the marked acquisition time node from the first historical operation data of the previous adjacent acquisition time node to obtain the first data change value of the corresponding first historical operation data, and set the first historical operation data whose first data change value is greater than the first preset data change threshold as the system state data; A second change degree analysis is performed on the second historical operating data and the second historical state coefficient mapped on the same time reference line, and the acquisition time node whose second change degree of the second historical state coefficient is greater than the second preset change degree threshold is marked, and the marked acquisition time node is subtracted from the second historical operating data of the previous adjacent acquisition time node to obtain the second data change value of the corresponding second historical operating data, and the second historical operating data whose second data change value is greater than the second preset data change threshold is set as the boiler state data.

4. The fault monitoring method for a steam sootblowing system according to claim 3, characterized in that: Generate the credibility of the corresponding system status data according to the frequency of occurrence of each system status data in multiple time reference lines and the total number of time reference lines, and eliminate the system status data with a credibility less than a preset credibility threshold; Generate the credibility of the corresponding boiler status data according to the frequency of occurrence of each boiler status data in multiple time reference lines and the total number of time reference lines, and eliminate the boiler status data with a credibility less than a preset credibility threshold; An application evaluation value of the first historical operation data corresponding to the historical operation period is generated for the remaining system state data and the boiler state data.

5. The fault monitoring method for a steam sootblowing system according to claim 4, characterized in that: Generating an application evaluation value corresponding to the first historical operation data according to the system state data and the boiler state data includes: Comparing the system status data with the corresponding preset standard system status data interval to obtain the number of system status data not in the preset standard system status data interval and the difference amount of the system status data not in the preset standard system status data interval; Comparing the boiler status data with the corresponding preset standard boiler status data interval, obtaining the number of boiler status data not in the preset standard boiler status data interval and the difference amount of the boiler status data not in the preset standard boiler status data interval; Generate an application evaluation value of first historical operation data of the same historical operation period according to the difference amount of system state data and the difference amount of boiler state data; The calculation formula of the application evaluation value is: Wherein, Y is the application evaluation value, g is the application evaluation conversion coefficient, n is the number of system state data that are not in the preset standard system state data interval, |△ki| is the difference amount of the i-th system state data, ei is the weight coefficient of the i-th system state data difference amount, a1 is the first weight coefficient, m is the number of boiler state data that are not in the preset standard boiler state data interval, |△ps| is the difference amount of the s-th boiler state data, and a2 is the second weight coefficient; The application evaluation values ​​of the first historical operation data of the plurality of historical operation cycles are sorted according to size, and the first historical operation data corresponding to the first ranked application evaluation value is set as the standard operation data.

6. The fault monitoring method for a steam sootblowing system according to claim 5, characterized in that: According to the correlation between the standard operation data and the monitoring indicators and the preset monitoring nodes, the standard operation data set for each monitoring indicator is obtained, including: Generate a correlation coefficient based on the dependency and association between the standard operation data and the historical characteristic fault data corresponding to the fault characteristics of each monitoring indicator; Determine the location information of the monitoring device corresponding to the standard operation data according to the acquisition source of the standard operation data; A compensation coefficient is set according to the relative distance between the location information and a plurality of preset monitoring nodes in each monitoring indicator, and a correlation degree is generated according to the compensation coefficient and the correlation coefficient; Pre-set relevance threshold; If the correlation between the standard operating data and the monitoring indicator and the preset monitoring node is greater than the correlation threshold, the standard operating data is set as the standard operating data of the corresponding monitoring indicator, and the standard operating parameter set of the corresponding monitoring indicator is constructed based on the standard operating data of the same monitoring indicator.

7. The fault monitoring method for a steam sootblowing system according to claim 6, characterized in that: The compensation coefficient is set according to the relative distance between the location information and multiple preset monitoring nodes in each monitoring indicator, including: Presetting a first preset relative distance interval, a second preset relative distance interval, a third preset relative distance interval and a fourth preset relative distance interval; When the relative distance is within the first preset relative distance interval, setting the compensation coefficient to a fourth preset compensation coefficient; When the relative distance is within the second preset relative distance interval, setting the compensation coefficient to a third preset compensation coefficient; When the relative distance is within a third preset relative distance interval, setting the compensation coefficient to the second preset compensation coefficient; When the relative distance is in a fourth preset relative distance interval, the compensation coefficient is set to the first preset compensation coefficient.

8. The fault monitoring method for a steam sootblowing system according to claim 7, characterized in that: The fault assessment coefficient of each monitoring indicator is calculated based on the comparison results, including: Acquire real-time operation data of each monitoring indicator at a preset monitoring node, compare the real-time operation data with a standard operation data set of the corresponding monitoring indicator, and obtain a comparison result, wherein the comparison result includes an operation data difference between the real-time operation data and the corresponding standard operation data in the standard operation data set; A fault assessment coefficient of the corresponding monitoring indicator is generated according to multiple operating data differences of the same monitoring indicator and the corresponding weight coefficient.

9. The fault monitoring method for a steam sootblowing system according to claim 8, characterized in that: Determine whether the steam soot blowing system has a fault based on the fault assessment coefficients of multiple monitoring indicators. If a fault occurs, determine the cause of the fault and send an alarm signal, including: Comparing the fault assessment coefficients of the multiple monitoring indicators with the preset fault assessment coefficient thresholds, if there is a fault assessment coefficient greater than the preset fault assessment coefficient threshold, it is determined that the steam soot blowing system has a fault; Determine the cause of the fault according to the monitoring indicator corresponding to the fault assessment coefficient greater than the preset fault assessment coefficient threshold; If there are multiple fault causes, the priority of the fault causes is set according to the coefficient difference that the fault assessment coefficient is greater than the preset fault assessment coefficient threshold and the influence degree of the corresponding fault cause, and the corresponding alarm signal is sent according to the priority order.

Images