CN110646229A - Air preheater fault diagnosis method and device, electronic equipment and storage medium - Google Patents
Air preheater fault diagnosis method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application discloses an air preheater fault diagnosis method and device, electronic equipment and a storage medium, and relates to the technical field of machine learning. Analyzing by an extreme gradient boost diagnosis model to generate a diagnosis result representing the hidden trouble of the air preheater if the obtained association condition among the multiple operation state parameters is the same as the association condition among the multiple historical operation state parameters of the same type and the historical diagnosis result corresponding to the association condition among the multiple historical operation state parameters of the same type is that the probability of the hidden trouble of the air preheater is greater than a preset threshold value; the diagnosis result is transmitted to the display terminal to be displayed, so that fault early warning is carried out on the air preheater before the air preheater breaks down, the fault early warning accuracy is high, a reference basis is provided for maintenance personnel to adjust the running state of the air preheater, and the air preheater is prevented from breaking down.
Description
Technical Field
The application relates to the technical field of machine learning, in particular to a method and a device for diagnosing faults of an air preheater, electronic equipment and a storage medium.
Background
The air preheater is the key auxiliary machinery equipment of thermal power factory, and its thermal power factory is the basis of guaranteeing whole production line normal operating. In the working process, the air preheater continuously works under the conditions of alternating pressure and the like for a long time, and the failure rate is high. A malfunctioning shutdown of an air preheater is likely to result in an unplanned shutdown of the power generating unit of a thermal power plant. Therefore, how to monitor and timely alarm the fault of the air preheater is the guarantee of the normal operation of the air preheater.
In the prior art, the fault monitoring method for the air preheater generally comprises the following steps: and acquiring the running state parameters of the air preheater in real time. For example, the operation state parameters include indexes such as air preheater bearing temperature, air preheater bearing vibration, air preheater motor stator temperature, and when one of the indexes is not within a preset range, a fault alarm is performed on the air preheater, but the fault alarm is inaccurate and cannot perform early warning in advance.
Disclosure of Invention
In a first aspect, an embodiment of the present application provides an air preheater fault diagnosis method, including:
receiving a plurality of operating state parameters acquired and transmitted by a plurality of different types of sensors during the operation of the air preheater;
inputting various operation state parameters into a pre-trained extreme gradient lifting diagnosis model, and generating a diagnosis result representing the hidden trouble of the air preheater if the obtained association condition among the various operation state parameters is the same as the association condition among the various historical operation state parameters of the same type and the historical diagnosis result corresponding to the association condition among the various historical operation state parameters of the same type is that the probability of the hidden trouble of the air preheater is greater than a preset threshold value through the analysis of the extreme gradient lifting diagnosis model;
and transmitting the diagnosis result to a display terminal for displaying.
In a second aspect, an embodiment of the present application further provides an air preheater fault diagnosis apparatus, including:
the information receiving unit is configured to receive a plurality of operation state parameters acquired and transmitted by a plurality of different types of sensors when the air preheater operates;
the fault diagnosis unit is configured to input multiple operation state parameters into a pre-trained extreme gradient boost diagnosis model, and if the obtained association conditions among the multiple operation state parameters are the same as the association conditions among multiple historical operation state parameters of the same type and the historical diagnosis results corresponding to the association conditions among the multiple historical operation state parameters of the same type are analyzed by the extreme gradient boost diagnosis model, the diagnosis result representing the fault hidden danger of the air preheater is generated when the probability of the fault hidden danger is greater than a preset threshold value;
and the information output unit is configured to transmit the diagnosis result to a display terminal for displaying.
In a third aspect, an embodiment of the present application further provides an electronic device, including:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to implement the steps of the method according to the first aspect of the embodiments of the present application.
In a fourth aspect, this application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is configured to, when executed by a processor, implement the steps of the method according to the first aspect of this application.
The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects: analyzing by an extreme gradient boost diagnosis model to generate a diagnosis result representing the hidden trouble of the air preheater if the obtained association condition among the multiple operation state parameters is the same as the association condition among the multiple historical operation state parameters of the same type and the historical diagnosis result corresponding to the association condition among the multiple historical operation state parameters of the same type is that the probability of the hidden trouble of the air preheater is greater than a preset threshold value; the diagnosis result is transmitted to the display terminal to be displayed, before the air preheater breaks down, fault early warning is carried out on the air preheater through mutual action among various operation state parameters, the fault early warning accuracy is high, a reference basis is provided for maintenance personnel to adjust the operation state of the air preheater, the air preheater is prevented from breaking down, and the working efficiency is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic diagram of interaction between a server and a display terminal and interaction between the server and a plurality of different types of sensors, respectively, according to an embodiment of the present disclosure;
FIG. 2 is a flow chart of an embodiment of a method for diagnosing a fault in an air preheater provided in an embodiment of the present application;
FIG. 3 is a flow chart of one embodiment of a method for diagnosing air preheater faults provided in embodiments of the present application;
FIG. 4 is a functional block diagram of an air preheater fault diagnosis apparatus provided in an embodiment of the present application;
FIG. 5 is a functional block diagram of an air preheater fault diagnosis apparatus provided in an embodiment of the present application;
fig. 6 is a circuit connection block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
The embodiment of the application provides an air preheater fault diagnosis method, which is applied to an electronic device 101, wherein the electronic device 101 can be a server, and the server is in communication connection with a display terminal 103 and a plurality of sensors 102 of different types installed on the air preheater respectively so as to realize data interaction. As shown in fig. 1, the plurality of sensors 102 of different types include a vibration sensor, a temperature sensor, a rotation speed sensor, a volume sensor, and the like, which are not limited herein. As shown in fig. 2, the method includes:
s21: a plurality of operating condition parameters of the air preheater during operation are received and transmitted from a plurality of different types of sensors 102.
Specifically, a plurality of different types of sensors 102 are installed at different locations of the air preheater, and the plurality of different types of sensors 102 acquire a plurality of operating state parameters of the air preheater in real time. The plurality of operating condition parameters of the air preheater during operation collected and transmitted by the plurality of different types of sensors 102 are received by receiving data transmitted by the plurality of different types of sensors 102. It will be appreciated that each sensor 102 may periodically collect data based on the sampling time, and that certain sampled data may be missing due to force-inefficacy. Thus, S21 may include: the method comprises the steps of periodically receiving multiple running state parameters of the air preheater during running, collected and transmitted by multiple different types of sensors 102, and obtaining an average value of the multiple historical running state parameters before the current moment if the collected data at the current sampling moment is missing.
The plurality of operating state parameters may include, for example only, a shaft temperature of a motor of the air preheater, a bearing vibration displacement, a noise value, a concentration of inlet and outlet flue gas, a free end temperature of the motor of the air preheater, a drive end temperature, a temperature of lubricating oil flowing through the drive end, and the like.
S22: inputting various operation state parameters into a pre-trained extreme gradient lifting diagnosis model, and analyzing by the extreme gradient lifting diagnosis model to generate a diagnosis result representing the occurrence of the hidden trouble of the air preheater if the obtained association condition among the various operation state parameters is the same as the association condition among the various historical operation state parameters of the same type and the historical diagnosis result corresponding to the association condition among the various historical operation state parameters of the same type is that the probability of the occurrence of the hidden trouble is greater than a preset threshold value.
S23: and transmitting the diagnosis result to the display terminal 103 for display.
Specifically, the diagnosis result is sent to the display terminal 103 for displaying after the diagnosis result is generated, so as to remind a worker that the current air preheater may have a fault and the current operation state of the air preheater needs to be adjusted.
According to the air preheater fault diagnosis method provided by the embodiment of the application, the extreme gradient boost diagnosis model analyzes that if the obtained correlation condition among multiple operation state parameters is the same as the correlation condition among multiple historical operation state parameters of the same type, and the historical diagnosis result corresponding to the correlation condition among the multiple historical operation state parameters of the same type is that the probability of the occurrence of the fault hidden danger is greater than the preset threshold value, a diagnosis result representing the occurrence of the fault hidden danger of the air preheater is generated; the diagnosis result is transmitted to the display terminal to be displayed, before the air preheater breaks down, fault early warning is carried out on the air preheater through mutual action among various operation state parameters, the fault early warning accuracy is high, a reference basis is provided for maintenance personnel to adjust the operation state of the air preheater, the air preheater is prevented from breaking down, and the working efficiency is improved.
Optionally, the extreme gradient boost diagnostic model is trained in advance as a training sample according to the historical operating state parameters, the historical fault diagnosis results and the association conditions between the historical operating state parameters for determining the historical fault diagnosis results.
The basic construction process of the extreme gradient boost diagnostic model can be as follows: firstly, constructing a basic tree model with n trees, wherein each iteration of the model is a tree model; then, starting the second round of calculation, wherein each round of calculation is used for training and inputting a residual error between a predicted value and a true value in the previous round; for a single tree, selecting an optimal splitting mode to split according to information gain or information gain ratio or a kini index when a new node is split each time; each tree is split according to the steps until the training samples are correctly classified at a certain node or the maximum depth of the tree is reached; and after the model calculation is finished, adding the calculation results of all the rounds to obtain a final diagnosis result.
In addition, the parameter tuning process of the extreme gradient boost diagnostic model is as follows: firstly, selecting a higher learning rate, giving a proper decision tree quantity corresponding to the learning rate, and training to obtain an ideal decision tree quantity; then, using a GridSearchCV function to adjust the parameter of the sum of the maximum depth of the tree and the minimum sample weight in the node, and selecting an optimal parameter; the "minimum penalty function degradation value required for node splitting" value is then adjusted. The value range of the minimum sample weight sum in the node is large, and coarse adjustment and fine adjustment can be carried out firstly; then, adjusting parameters of controlling the proportion of random sampling of each tree and controlling the proportion of each splitting of each level of the tree to the characteristic sampling, in order to prevent under-fitting, setting the values of controlling the proportion of random sampling of each tree and controlling the proportion of each splitting of each level of the tree to the characteristic sampling to be more than 0.5, and then gradually adjusting to obtain an optimal parameter value; secondly, two regularization parameters of reg _ alpha and reg _ lambda are adjusted to effectively reduce overfitting, the value ranges of the two regularization parameters are large, coarse tuning can be carried out firstly, after a corresponding suboptimum value is found, fine tuning is carried out near the suboptimum value, and an optimal value is obtained; and finally, readjusting the learning rate to obtain a smaller learning rate value.
As one of the embodiments: the multiple operation state parameters can comprise the shaft temperature of a motor of the air preheater, the vibration displacement of the bearing, the noise value and the concentration of inlet and outlet flue gas, and the correlation conditions are that the shaft temperature of the motor is greater than a preset threshold value, the vibration displacement of the bearing, the noise value is greater than a preset threshold value and the concentration of the inlet and outlet flue gas is greater than a preset threshold value. If the shaft temperature of the motor is higher than a preset threshold value historically, the bearing vibration displacement occurs, the noise value is higher than the preset threshold value historically, and the probability that the inlet and outlet flue gas concentration is higher than the preset threshold value and fails is higher than a preset threshold value (for example, 65%), a diagnosis result representing the hidden trouble of the air preheater is generated.
As one of the embodiments: the multiple operation state parameters can also comprise the temperature of the free end of a motor of the air preheater, the temperature of the driving end and the temperature of lubricating oil flowing through the driving end, and the associated conditions can be that the vibration direction of the motor of the air preheater is inconsistent with a preset vibration direction, the temperature of the free end is increased, the temperature of the driving end is unchanged, and the temperature of the lubricating oil flowing through the driving end is increased. If historically the vibration direction of the motor of the air preheater is inconsistent with the preset vibration direction, the temperature of the free end is increased, the temperature of the driving end is unchanged, and the probability of the fault caused by the temperature increase of the lubricating oil flowing through the driving end is greater than a preset threshold (such as 65%), a diagnosis result representing the hidden fault danger of the air preheater is generated.
It can be understood that, for different operating state parameters and different associated conditions, the corresponding diagnosis results for representing the fault hidden danger of the air preheater are different.
Optionally, as shown in fig. 3, the method further includes:
s24: and generating a diagnosis result representing the fault hidden danger of the air preheater, and simultaneously generating the fault severity according to the first weight coefficient of each operation state parameter and the second weight coefficient of the threshold range in which each operation state parameter is positioned.
For example, according to the first embodiment, the weight coefficient of the shaft temperature of the motor of the air preheater may be set to 0.2, the weight coefficient in the temperature zone a may be 1.3, the weight coefficient in the temperature zone B may be 1.2, and the weight coefficient in the temperature zone C may be 1.1. The weight coefficient of the bearing vibration displacement is set to 0.2, the weight coefficient in the displacement section a is 1.3, the weight coefficient in the displacement section B is 1.2, and the weight coefficient in the displacement section C is 1.1. The weighting factor of the noise value is set to 0.3, the weighting factor in the decibel interval A is 1.3, the weighting factor in the decibel interval B is 1.2, and the weighting factor in the decibel interval C is 1.1; the weight coefficient of the concentration of the inlet and outlet flue gas is set to be 0.3, the weight coefficient in the concentration interval A is 1.3, the weight coefficient in the concentration interval B is 1.2, and the weight coefficient in the concentration interval C is 1.1.
When the multiple operation state parameters include the shaft temperature of the motor of the air preheater and are in a temperature interval a, the bearing vibrates and is displaced and is in a displacement interval B, the noise value and is in a decibel interval C, and the concentration of the inlet and outlet flue gas and is in a concentration interval C, the severity of the generated fault is 0.2X1.3+0.2X1.2+0.3X1.1 ═ 1.16.
Referring to fig. 4, an air preheater failure diagnosis apparatus 500 is further provided in an embodiment of the present application, and is applied to an electronic device 101, where the electronic device 101 may be a server. It should be noted that the basic principle and the technical effects of the air preheater fault diagnosis device 500 provided in the embodiment of the present application are the same as those of the above embodiment, and for the sake of brief description, reference may be made to the corresponding contents in the above embodiment for the part of this embodiment that is not mentioned. The apparatus 500 includes an information receiving unit 501, a fault diagnosing unit 502, and an information outputting unit 503. Wherein the content of the first and second substances,
the information receiving unit 501 is configured to receive a plurality of operating state parameters of the air preheater in operation, which are collected and transmitted by a plurality of different types of sensors.
The fault diagnosis unit 502 is configured to input multiple operation state parameters into a pre-trained extreme gradient boost diagnosis model, and generate a diagnosis result representing that the air preheater has a fault hidden danger if the obtained association condition between the multiple operation state parameters is the same as the association condition between multiple historical operation state parameters of the same type and the historical diagnosis result corresponding to the association condition between the multiple historical operation state parameters of the same type is greater than a preset threshold value.
The information output unit 503 is configured to transmit the diagnosis result to a display terminal for display.
The extreme gradient boost diagnosis model is formed by pre-training a training sample according to historical operating state parameters, historical fault diagnosis results and the association conditions among the historical operating state parameters for determining the historical fault diagnosis results.
When the air preheater fault diagnosis device 500 provided by the embodiment of the application is executed, the following functions can be realized: according to the multiple operation state parameters, the pre-trained extreme gradient promotion diagnosis model and the correlation condition between the historical operation state parameters and the historical operation state parameters for determining the historical diagnosis result, the diagnosis result is generated, therefore, before the air preheater breaks down, fault early warning is carried out on the air preheater through the mutual action among the multiple operation state parameters, the fault early warning accuracy is high, a reference basis is provided for maintenance personnel to adjust the operation state of the air preheater, the air preheater is prevented from breaking down, and the working efficiency is improved.
Alternatively, the information receiving unit 501 may be specifically configured to periodically receive multiple operating state parameters of the air preheater during operation, which are collected and transmitted by multiple different types of sensors, and if the collected data at the current sampling time is missing, obtain an average value of the multiple historical operating state parameters before the current sampling time.
Optionally, as shown in fig. 5, the apparatus 500 further includes: and the fault severity generation unit 504 is configured to generate a fault severity according to the first weight coefficient of each operation state parameter and the second weight coefficient of the threshold range in which each operation state parameter is positioned while generating a diagnosis result representing that the air preheater has a fault hidden trouble.
As an embodiment, the multiple operating state parameters include a shaft temperature of a motor of the air preheater, a bearing vibration displacement, a noise value, and concentrations of inlet and outlet flue gas, and the associated conditions are that the shaft temperature of the motor is greater than a preset threshold, the bearing vibration displacement occurs, the noise value is greater than a preset threshold, and the concentrations of the inlet and outlet flue gas are greater than a preset threshold.
As another embodiment, when the diagnosis result is a diagnosis result indicating that the air preheater has a fault hidden trouble, the plurality of operation state parameters include a free end temperature of a motor of the air preheater, a drive end temperature, and a temperature of the lubricating oil flowing through the drive end, and the correlation condition is that a vibration direction of the motor of the air preheater is not consistent with a preset vibration direction, the free end temperature is increased, the drive end temperature is unchanged, and the temperature of the lubricating oil flowing through the drive end is increased.
It should be noted that the execution subjects of the steps of the method provided in embodiment 1 may be the same device, or different devices may be used as the execution subjects of the method. For example, the execution subject of steps 21 and 22 may be device 1, and the execution subject of step 23 may be device 2; for another example, the execution subject of step 21 may be device 1, and the execution subjects of steps 22 and 23 may be device 2; and so on.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application. Referring to fig. 6, at a hardware level, the electronic device includes a processor, and optionally further includes an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (peripheral component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 6, but that does not indicate only one bus or one type of bus.
And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.
The processor reads a corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to form the air preheater fault diagnosis device on a logic level. The processor is used for executing the program stored in the memory and is specifically used for executing the following operations:
receiving a plurality of operating state parameters acquired and transmitted by a plurality of different types of sensors during the operation of the air preheater;
inputting various operation state parameters into a pre-trained extreme gradient lifting diagnosis model, and generating a diagnosis result representing the hidden trouble of the air preheater if the obtained association condition among the various operation state parameters is the same as the association condition among the various historical operation state parameters of the same type and the historical diagnosis result corresponding to the association condition among the various historical operation state parameters of the same type is that the probability of the hidden trouble of the air preheater is greater than a preset threshold value through the analysis of the extreme gradient lifting diagnosis model;
and transmitting the diagnosis result to a display terminal for displaying.
The method performed by the air preheater fault diagnosis device according to the embodiment shown in fig. 4 of the present application can be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
The electronic device may also execute the method shown in fig. 2, and implement the functions of the air preheater fault diagnosis apparatus in the embodiments shown in fig. 2 and fig. 3, which are not described herein again in this application.
Of course, besides the software implementation, the electronic device of the present application does not exclude other implementations, such as a logic device or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may also be hardware or a logic device.
Embodiments of the present application also propose a computer-readable storage medium storing one or more programs, the one or more programs including instructions, which when executed by a portable electronic device including a plurality of application programs, enable the portable electronic device to perform the method of the embodiments shown in fig. 2 and 3, and are specifically configured to:
receiving a plurality of operating state parameters acquired and transmitted by a plurality of different types of sensors during the operation of the air preheater;
inputting various operation state parameters into a pre-trained extreme gradient lifting diagnosis model, and generating a diagnosis result representing the hidden trouble of the air preheater if the obtained association condition among the various operation state parameters is the same as the association condition among the various historical operation state parameters of the same type and the historical diagnosis result corresponding to the association condition among the various historical operation state parameters of the same type is that the probability of the hidden trouble of the air preheater is greater than a preset threshold value through the analysis of the extreme gradient lifting diagnosis model;
and transmitting the diagnosis result to a display terminal for displaying.
In short, the above description is only a preferred embodiment of the present application, and is not intended to limit the scope of the present application. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
Claims (10)
1. An air preheater fault diagnostic method, comprising:
receiving a plurality of operating state parameters acquired and transmitted by a plurality of different types of sensors during the operation of the air preheater;
inputting various operation state parameters into a pre-trained extreme gradient lifting diagnosis model, and generating a diagnosis result representing the hidden trouble of the air preheater if the obtained association condition among the various operation state parameters is the same as the association condition among the various historical operation state parameters of the same type and the historical diagnosis result corresponding to the association condition among the various historical operation state parameters of the same type is that the probability of the hidden trouble of the air preheater is greater than a preset threshold value through the analysis of the extreme gradient lifting diagnosis model;
and transmitting the diagnosis result to a display terminal for displaying.
2. The method of claim 1, further comprising: and generating a diagnosis result representing the fault hidden danger of the air preheater, and simultaneously generating the fault severity according to the first weight coefficient of each operation state parameter and the second weight coefficient of the threshold range in which each operation state parameter is positioned.
3. The method according to claim 1, wherein the plurality of operating state parameters comprise a shaft temperature of a motor of the air preheater, a bearing vibration displacement, a noise value, and a concentration of inlet and outlet flue gas, and the correlation condition is that the shaft temperature of the motor is greater than a preset threshold, the bearing vibration displacement occurs, the noise value is greater than a preset threshold, and the concentration of the inlet and outlet flue gas is greater than a preset threshold.
4. The method of claim 1, wherein the plurality of operating condition parameters includes a free end temperature, a drive end temperature, and a temperature of the lubrication oil flowing through the drive end of the motor of the air preheater, and the associated conditions are that a vibration direction of the motor of the air preheater does not coincide with a preset vibration direction, the free end temperature increases, the drive end temperature does not change, and the temperature of the lubrication oil flowing through the drive end increases.
5. The method according to claim 1, wherein the extreme gradient boost diagnostic model is trained in advance as training samples according to historical operating state parameters, historical fault diagnosis results and correlation conditions between the historical operating state parameters for determining the historical fault diagnosis results.
6. The method of claim 1, wherein receiving the plurality of operating condition parameters of the air preheater during operation collected and transmitted by the plurality of different types of sensors comprises:
the method comprises the steps of periodically receiving multiple running state parameters of the air preheater during running, collected and transmitted by multiple sensors of different types, and obtaining an average value of the multiple historical running state parameters before the current moment if the collected data at the current sampling moment is missing.
7. An air preheater fault diagnostic apparatus, comprising:
the information receiving unit is configured to receive a plurality of operation state parameters acquired and transmitted by a plurality of different types of sensors when the air preheater operates;
the fault diagnosis unit is configured to input multiple operation state parameters into a pre-trained extreme gradient boost diagnosis model, and if the obtained association conditions among the multiple operation state parameters are the same as the association conditions among multiple historical operation state parameters of the same type and the historical diagnosis results corresponding to the association conditions among the multiple historical operation state parameters of the same type are analyzed by the extreme gradient boost diagnosis model, the diagnosis result representing the fault hidden danger of the air preheater is generated when the probability of the fault hidden danger is greater than a preset threshold value;
and the information output unit is configured to transmit the diagnosis result to a display terminal for displaying.
8. The apparatus of claim 7, further comprising: and the fault severity generation unit is configured to generate a fault severity according to the first weight coefficient of each operation state parameter and the second weight coefficient of the threshold range in which each operation state parameter is positioned while generating a diagnosis result representing that the air preheater has a fault hidden trouble.
9. An electronic device, comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to carry out the steps of the method of any one of claims 1 to 6.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
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