Induced draft fan fault monitoring method and system, electronic equipment and storage medium

Abstract

The invention relates to an induced draft fan fault monitoring method which comprises the steps that operation data of an electric induced draft fan are collected, the operation data comprise linear parameter operation data and nonlinear parameter operation data, and the operation process of the electric induced draft fan is divided into different stages; and obtaining tripping thresholds of the linear parameter and the nonlinear parameter in the current operation stage according to the operation data, and predicting the tripping time of the linear parameter and the nonlinear parameter through different prediction models according to the tripping thresholds and the operation data. According to the method and the device, the problem of low fault monitoring accuracy of the induced draft fan is solved, the operation process is divided into different stages, different threshold values are set, meanwhile, the operation data is divided into linear parameters and nonlinear parameters, tripping time prediction is performed through different models, and the prediction accuracy is improved.

Description

Technical Field

[0001] The present application relates to the field of fault diagnosis, and in particular to a method, system, electronic device and storage medium for monitoring induced draft fan faults. Background Art

[0002] Existing monitoring methods for electric induced draft fan systems typically use set alarm or protection thresholds to issue warnings when equipment operating parameters reach these thresholds. However, these fixed alarm and protection thresholds cannot adapt to environmental changes, resulting in low fault monitoring accuracy. Furthermore, it is impossible to determine whether equipment performance continues to deteriorate or stabilize after the warning is triggered.

[0003] Currently, no effective solution has been proposed to address the problem of low accuracy in fault monitoring of induced draft fans in related technologies. Summary of the Invention

[0004] The embodiments of the present application provide an induced draft fan fault monitoring method, system, electronic device, and storage medium to at least solve the problem of low accuracy in induced draft fan fault monitoring in the related art.

[0005] In a first aspect, an embodiment of the present application provides a method for monitoring induced draft fan faults, the method comprising:

[0006] Collecting operating data of the electric induced draft fan, wherein the operating data includes operating data of linear parameters and nonlinear parameters;

[0007] Dividing the operation process of the electric induced draft fan into different stages, and obtaining the tripping thresholds of the linear parameters and the nonlinear parameters in the current operation stage according to the operation data;

[0008] According to the tripping threshold and the operating data, the tripping time of the linear parameter and the tripping time of the nonlinear parameter are predicted by using different prediction models respectively.

[0009] In some embodiments, predicting the trip time of the linear parameter and the nonlinear parameter using different prediction models respectively includes:

[0010] analyzing the change rates of the linear parameter and the nonlinear parameter according to the operating data;

[0011] predicting a trip time of the linear parameter based on operating data, a rate of change, and a trip threshold of the linear parameter by a linear extrapolation method;

[0012] The nonlinear model is used to predict the tripping time of the nonlinear parameter according to the operating data, the rate of change and the tripping threshold of the nonlinear parameter.

[0013] In some embodiments, analyzing the changing rates of the linear parameter and the nonlinear parameter according to the operating data includes:

[0014] Based on the operating data, average value analysis, interpolation analysis and filtering analysis are performed on the numerical changes of each linear parameter and the nonlinear parameter within a preset time period to obtain the corresponding change rate of each linear parameter and the nonlinear parameter.

[0015] In some embodiments, the method further comprises:

[0016] When the nonlinear parameters conform to preset variation rules, the nonlinear model is obtained by fitting differential equations based on historical operating data of the electric induced draft fan. The preset variation rules include exponential variation, logarithmic variation, temperature diffusion rule and mechanical fatigue rule.

[0017] In some embodiments, obtaining the tripping thresholds of the linear parameter and the nonlinear parameter in the current operation stage includes:

[0018] Analyzing the operation data using a hidden Markov model to determine the current operation stage;

[0019] Acquire the tripping threshold of the linear parameter and the tripping threshold of the nonlinear parameter in the current operation stage.

[0020] In some embodiments, the method further comprises:

[0021] After predicting the trip times of the linear parameter and the nonlinear parameter, classifying the prediction results into stable predictions and highly uncertain predictions, and determining confidence intervals;

[0022] Through different early warning methods, early warnings are issued for the prediction results of the stable prediction and the high uncertainty prediction respectively.

[0023] In some embodiments, the operating data includes: furnace pressure, motor temperature, oil system data, and bearing system data.

[0024] In a second aspect, an embodiment of the present application provides an induced draft fan fault monitoring system, the system comprising:

[0025] An acquisition module, configured to acquire operating data of the electric induced draft fan, wherein the operating data includes operating data of linear parameters and nonlinear parameters;

[0026] a threshold determination module, configured to divide the operation process of the electric induced draft fan into different stages, and obtain trip thresholds of the linear parameter and the nonlinear parameter in the current operation stage according to the operation data;

[0027] A prediction module is used to predict the tripping time of the linear parameter and the nonlinear parameter using different prediction models according to the tripping threshold and the operating data.

[0028] In a third aspect, an embodiment of the present application provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the induced draft fan fault monitoring method as described in the first aspect above is implemented.

[0029] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the induced draft fan fault monitoring method as described in the first aspect above.

[0030] Compared to related technologies, the induced draft fan fault monitoring method provided in the embodiments of the present application collects operating data of the electric induced draft fan, including operating data of linear parameters and nonlinear parameters, divides the operating process of the electric induced draft fan into different stages, obtains tripping thresholds of the linear parameters and nonlinear parameters in the current operating stage based on the operating data, and predicts the tripping time of the linear parameters and nonlinear parameters using different prediction models based on the tripping thresholds and operating data, thereby solving the problem of low accuracy in induced draft fan fault monitoring. By dividing the operating process into different stages, setting different thresholds, and dividing the operating data into linear parameters and nonlinear parameters, and predicting the tripping time using different models, the accuracy of the prediction is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0032] Figure 1 is a flow chart of a method for monitoring induced draft fan faults according to an embodiment of the present application;

[0033] Figure 2 is a structural block diagram of an induced draft fan fault monitoring system according to an embodiment of the present application;

[0034] Figure 3 Schematic diagram of the internal structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is described and illustrated below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely used to explain this application and are not intended to limit this application. Based on the embodiments provided in this application, all other embodiments obtained by those of ordinary skill in the art without making any creative efforts are within the scope of protection of this application.

[0036] Obviously, the drawings described below are merely examples or embodiments of the present application. Those skilled in the art can, without inventive effort, apply the present application to other similar scenarios based on these drawings. Furthermore, it is also understood that, although the effort involved in such a development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, changes in design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as an insufficiency of the content disclosed in this application.

[0037] References to "embodiments" in this application mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it refer to independent or alternative embodiments that are mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described in this application may be combined with other embodiments unless there is a conflict.

[0038] Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by a person of ordinary skill in the technical field to which this application belongs. The words "one", "a", "the" and the like used in this application do not indicate a limit on quantity and may indicate the singular or plural. The terms "include", "comprise", "have" and any variations thereof used in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units that are inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The word "multiple" used in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, "A and / or B" can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific order for the objects.

[0039] This embodiment provides a method for monitoring induced draft fan faults. Figure 1 is a flow chart of a method for monitoring induced draft fan faults according to an embodiment of the present application. Figure 1 As shown, the process includes the following steps:

[0040] Step S101 : collecting operating data of the electric induced draft fan, where the operating data includes operating data of linear parameters and nonlinear parameters.

[0041] This embodiment predicts the tripping time by analyzing the operating data of the electric induced draft fan, that is, the estimated time after which the fan will trip according to the current abnormal state, prompting the staff to take emergency measures in time to control the accident and ensure orderly and stable changes in the production state.

[0042] In some embodiments, the operating data includes: furnace pressure, motor temperature, oil system data, and bearing system data.

[0043] Situations that may trigger the electric induced draft fan to trip protection include but are not limited to: furnace pressure lower than the specified value, the air preheater on the same side is out of service, the inlet or outlet damper is closed after the induced draft fan is running, the induced draft fan oil system (lubrication, hydraulics) is abnormal, the induced draft fan motor temperature (winding, bearing) protection, the induced draft fan bearing system (vibration, temperature) protection, and the induced draft fan inverter serious fault (variable frequency induced draft fan).

[0044] This embodiment determines whether the furnace pressure is lower than the specified value by monitoring the furnace pressure analog measurement point, determines whether the induced draft fan oil system (lubrication, hydraulic pressure) is abnormal by monitoring the lubricating oil and power oil analog measurement points, determines whether the induced draft fan motor temperature (winding, bearing) is abnormal by monitoring the winding and bearing temperature measurement points, and determines whether the induced draft fan bearing system (vibration, temperature) is abnormal by monitoring the fan vibration and temperature measurement points.

[0045] Monitor the furnace pressure, lubricating oil, hydraulic oil, temperature, and vibration measuring points of the induced draft fan to detect abnormal fan failures in advance and adjust the induced draft fan production process system in a timely manner.

[0046] Step S102 : Divide the operation process of the electric induced draft fan into different stages, and obtain the tripping thresholds of the linear parameters and the nonlinear parameters of the current operation stage according to the operation data.

[0047] In some embodiments, obtaining the trip thresholds of the linear parameters and the nonlinear parameters in the current operation stage in step S102 includes:

[0048] Step S1021 , analyzing the operation data through a hidden Markov model to determine the current operation stage.

[0049] Step S1022: Acquire the tripping threshold of the linear parameter and the tripping threshold of the nonlinear parameter in the current operation stage.

[0050] For more complex and typical equipment fault characterization parameters, the parameter change process can be divided into multiple stages (such as acceleration period, stable period, and decay period). The current stage can be identified through the hidden Markov model (HMM), and the prediction model can be dynamically switched to obtain a more accurate tripping time.

[0051] Step S103 : predicting the tripping time of the linear parameter and the nonlinear parameter using different prediction models according to the tripping threshold and the operating data.

[0052] In some embodiments, step S103 specifically includes:

[0053] Step S1031 : analyzing the changing rates of the linear parameters and the nonlinear parameters according to the operating data.

[0054] In some embodiments, step S1031 includes: based on the operating data, performing average value analysis, interpolation analysis and filtering analysis on the numerical changes of the linear parameters and the nonlinear parameters within a preset time period to obtain the corresponding change rates of the linear parameters and the nonlinear parameters.

[0055] In order to ensure the accuracy of trip time prediction, the rate of change process is calculated by average value, interpolation and filtering to prevent the rate from decreasing, which may cause a large deviation between the prediction result and the actual result.

[0056] Step S1032: predicting the tripping time of the linear parameter according to the operating data, change rate and tripping threshold of the linear parameter by linear extrapolation.

[0057] For linear parameters with a constant rate of change, the trip time is calculated using the linear extrapolation method. The calculation formula is:

[0058] T=(V 跳闸 -V 当前 ) / R 当前

[0059] Among them, R is the current parameter change rate (change per unit time), V 跳闸 is the trip threshold, V 当前 is the current parameter value, and T is the trip countdown (the predicted time to reach the trip threshold).

[0060] Step S1033 : predicting the tripping time of the nonlinear parameter according to the operating data, the rate of change and the tripping threshold of the nonlinear parameter through the nonlinear model.

[0061] In some embodiments, the method further includes: when the nonlinear parameters conform to preset change laws, fitting differential equations based on historical operating data of the electric induced draft fan to obtain a nonlinear model, and the preset change laws include exponential change, logarithmic change, temperature diffusion law and mechanical fatigue law.

[0062] If the parameter changes follow exponential, logarithmic or other nonlinear laws (such as temperature diffusion, mechanical fatigue), it is necessary to fit the differential equation based on historical data:

[0063] d V / dt=f(V,t)

[0064] Where t is the unit time and V is the parameter value. The time to reach the trip threshold is predicted by numerical integration (e.g., Euler method or Runge-Kutta method).

[0065] Different time prediction logics are used for different parameters. The choice between linear and nonlinear methods depends on the abnormal trends of key parameters of equipment operation, which can be obtained through fault data diagnosis.

[0066] The change in countdown time (the time to reach the tripping threshold) can indicate whether the equipment is deteriorating rapidly and whether the deterioration trend is effectively suppressed, and can be used as a characteristic parameter to provide feedback to operators on whether the treatment is effective.

[0067] Through the above steps, operating data of the electric induced draft fan is collected, including operating data for linear and nonlinear parameters. The operating process of the electric induced draft fan is divided into different stages, and the tripping thresholds of the linear and nonlinear parameters in the current operating stage are obtained. Based on the tripping thresholds and operating data, different prediction models are used to predict the tripping times of the linear and nonlinear parameters, respectively. This solves the problem of low accuracy in induced draft fan fault monitoring. By dividing the operating process into different stages, setting different thresholds, and separating the operating data into linear and nonlinear parameters, and using different models to predict tripping times, the accuracy of the prediction is improved.

[0068] In some embodiments, the method further comprises:

[0069] Step S104: After predicting the tripping time of the linear parameters and the nonlinear parameters, the prediction results are divided into stable predictions and highly uncertain predictions, and confidence intervals are determined.

[0070] Step S105: issuing early warnings for the prediction results of stable predictions and high uncertainty predictions respectively through different early warning methods.

[0071] On the early warning display interface, it is necessary to distinguish between "stable forecast" and "high uncertainty forecast". For example, stable forecast is displayed in green, and high uncertainty forecast is displayed in flashing red, and the confidence interval is marked (such as "30±5 minutes remaining") to provide operators with specific conclusions and suggestions to facilitate the adoption of emergency measures.

[0072] It should be noted that the steps shown in the above process or the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0073] This embodiment also provides an induced draft fan fault monitoring system, which is used to implement the above-mentioned embodiments and preferred implementations. Details already described are omitted for clarity. As used below, terms such as "module," "unit," and "subunit" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0074] Figure 2 is a structural block diagram of an induced draft fan fault monitoring system according to an embodiment of the present application, such as Figure 2 As shown, the system includes:

[0075] The acquisition module 21 is used to collect the operating data of the electric induced draft fan, the operating data including the operating data of linear parameters and nonlinear parameters;

[0076] A threshold determination module 22 is used to divide the operation process of the electric induced draft fan into different stages and obtain the tripping thresholds of the linear parameters and nonlinear parameters of the current operation stage according to the operation data;

[0077] The prediction module 23 is used to predict the tripping time of linear parameters and nonlinear parameters through different prediction models according to the tripping threshold and the operating data.

[0078] In some embodiments, the prediction module 23 includes:

[0079] The rate calculation module is used to analyze the change rates of linear parameters and nonlinear parameters according to the operating data.

[0080] The linear prediction module is used to predict the tripping time of the linear parameter according to the operating data, change rate and tripping threshold of the linear parameter through linear extrapolation.

[0081] The nonlinear prediction module is used to predict the tripping time of the nonlinear parameter according to the operating data, change rate and tripping threshold of the nonlinear parameter through a nonlinear model.

[0082] In some embodiments, the rate calculation module is specifically used to: based on the operating data, perform average value analysis, interpolation analysis and filtering analysis on the numerical changes of the linear parameters and nonlinear parameters within a preset time period to obtain the corresponding change rates of the linear parameters and nonlinear parameters.

[0083] In some embodiments, the induced draft fan fault monitoring system also includes: a nonlinear model construction module, which is used to fit the differential equation based on the historical operating data of the electric induced draft fan to obtain a nonlinear model when the nonlinear parameters conform to the preset change law. The preset change law includes exponential change, logarithmic change, temperature diffusion law and mechanical fatigue law.

[0084] In some embodiments, the threshold determination module 22 includes:

[0085] The stage judgment module is used to analyze the operation data through the hidden Markov model to determine the current operation stage.

[0086] The threshold acquisition module is used to obtain the tripping threshold of the linear parameter and the tripping threshold of the nonlinear parameter in the current operation stage.

[0087] In some embodiments, the induced draft fan fault monitoring system further includes:

[0088] The prediction classification module is used to classify the prediction results into stable predictions and highly uncertain predictions after predicting the tripping time of linear parameters and nonlinear parameters, and to determine the confidence intervals.

[0089] The early warning module is used to issue early warnings for the prediction results of stable predictions and high uncertainty predictions through different early warning methods.

[0090] In some embodiments, the operating data includes: furnace pressure, motor temperature, oil system data, and bearing system data.

[0091] Through the above system, the acquisition module 21 collects operating data of the electric induced draft fan, including operating data for linear and nonlinear parameters. The threshold determination module 22 divides the operating process of the electric induced draft fan into different stages and obtains the tripping thresholds of the linear and nonlinear parameters in the current operating stage. The prediction module 23 uses different prediction models to predict the tripping time of the linear and nonlinear parameters based on the tripping thresholds and operating data, respectively. This solves the problem of low accuracy in induced draft fan fault monitoring. By dividing the operating process into different stages and setting different thresholds, and by separating the operating data into linear and nonlinear parameters, and using different models to predict the tripping time, the accuracy of the prediction is improved.

[0092] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0093] This embodiment further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0094] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0095] Optionally, in this embodiment, the processor may be configured to execute the following steps through a computer program:

[0096] S1, collecting operating data of the electric induced draft fan, the operating data including operating data of linear parameters and nonlinear parameters.

[0097] S2, the operation process of the electric induced draft fan is divided into different stages, and the tripping thresholds of the linear parameters and nonlinear parameters of the current operation stage are obtained according to the operation data.

[0098] S3, based on the tripping threshold and operating data, the tripping time of linear parameters and nonlinear parameters are predicted through different prediction models.

[0099] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be repeated here.

[0100] In one embodiment, Figure 3 is a schematic diagram of the internal structure of an electronic device according to an embodiment of the present application, such as Figure 3 As shown, an electronic device is provided, which may be a server, and its internal structure diagram may be as shown in FIG. Figure 3 As shown. The electronic device includes a processor, a memory, a network interface, and a database connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the electronic device is used to store data. The network interface of the electronic device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for monitoring induced draft fan faults is implemented.

[0101] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the electronic device to which the solution of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0102] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, which can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0103] Those skilled in the art should understand that the various technical features of the above-described embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0104] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for monitoring induced draft fan faults, characterized in that: The method comprises: Collecting operating data of the electric induced draft fan, wherein the operating data includes operating data of linear parameters and nonlinear parameters; Dividing the operation process of the electric induced draft fan into different stages, and obtaining the tripping thresholds of the linear parameters and the nonlinear parameters in the current operation stage according to the operation data; According to the tripping threshold and the operating data, the tripping time of the linear parameter and the tripping time of the nonlinear parameter are predicted by using different prediction models respectively.

2. The method according to claim 1, characterized in that The predicting of the tripping time of the linear parameter and the nonlinear parameter by using different prediction models respectively includes: analyzing the change rates of the linear parameter and the nonlinear parameter according to the operating data; predicting a trip time of the linear parameter based on operating data, a rate of change, and a trip threshold of the linear parameter by a linear extrapolation method; The nonlinear model is used to predict the tripping time of the nonlinear parameter according to the operating data, the rate of change and the tripping threshold of the nonlinear parameter.

3. The method according to claim 2, characterized in that Analyzing the change rates of the linear parameter and the nonlinear parameter according to the operating data includes: Based on the operating data, average value analysis, interpolation analysis and filtering analysis are performed on the numerical changes of each linear parameter and the nonlinear parameter within a preset time period to obtain the corresponding change rate of each linear parameter and the nonlinear parameter.

4. The method according to claim 2, characterized in that The method further comprises: When the nonlinear parameters conform to preset variation rules, the nonlinear model is obtained by fitting differential equations based on historical operating data of the electric induced draft fan. The preset variation rules include exponential variation, logarithmic variation, temperature diffusion rule and mechanical fatigue rule.

5. The method according to claim 1, wherein The step of obtaining the tripping thresholds of the linear parameter and the nonlinear parameter in the current operation stage includes: Analyzing the operation data using a hidden Markov model to determine the current operation stage; Acquire the tripping threshold of the linear parameter and the tripping threshold of the nonlinear parameter in the current operation stage.

6. The method according to claim 1, characterized in that The method further comprises: After predicting the trip times of the linear parameter and the nonlinear parameter, classifying the prediction results into stable predictions and highly uncertain predictions, and determining confidence intervals; Through different early warning methods, early warnings are issued for the prediction results of the stable prediction and the high uncertainty prediction respectively.

7. The method according to claim 1, characterized in that The operating data includes: furnace pressure, motor temperature, oil system data, and bearing system data.

8. A induced draft fan fault monitoring system, characterized in that: The system comprises: An acquisition module, configured to acquire operating data of the electric induced draft fan, wherein the operating data includes operating data of linear parameters and nonlinear parameters; a threshold determination module, configured to divide the operation process of the electric induced draft fan into different stages, and obtain trip thresholds of the linear parameter and the nonlinear parameter in the current operation stage according to the operation data; A prediction module is used to predict the tripping time of the linear parameter and the nonlinear parameter respectively through different prediction models according to the tripping threshold and the operating data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the induced draft fan fault monitoring method according to any one of claims 1 to 7 is implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the induced draft fan fault monitoring method according to any one of claims 1 to 7 is implemented.

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