CN110704964B - Steam turbine operation state diagnosis method and device, electronic device and storage medium - Google Patents
Steam turbine operation state diagnosis method and device, electronic device and storage medium Download PDFInfo
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Abstract
The application discloses a method and a device for diagnosing operating states of a steam turbine, electronic equipment and a storage medium, and relates to the technical field of machine learning. Receiving various operating state parameters of the steam turbine during operation, which are acquired and transmitted by a plurality of different types of sensors; generating a diagnosis result according to the obtained multiple operation state parameters, a pre-trained neural network 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 transmitted to the display terminal to be displayed, so that before the steam turbine breaks down, fault early warning is carried out on the steam turbine 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 steam turbine, the steam turbine is prevented from breaking down, and the working efficiency is improved.
Description
Technical Field
The application relates to the technical field of machine learning, in particular to a method and a device for diagnosing the operating state of a steam turbine, electronic equipment and a storage medium.
Background
The steam turbine is the core equipment of thermal power factory, and its normal operating is the basis of guaranteeing that whole production line normally operates. In actual operation, since the steam turbine is continuously operated under alternating pressure, high temperature and high pressure, and the like for a long period of time, the failure rate of the equipment increases with the accumulation of the operation time. A faulty shutdown of the turbine equipment will result in an unplanned shutdown of the power generating unit. Therefore, how to monitor and timely alarm the fault of the steam turbine is the guarantee of the safe operation of the generator set.
In the prior art, the fault monitoring method for the steam turbine generally comprises the following steps: and collecting the operating state parameters of the steam turbine in real time. For example, the operating state parameters include indexes such as turbine bearing temperature, turbine bearing vibration, turbine motor stator temperature, and the like, and when one of the indexes is not within a preset range, a fault alarm is performed on the turbine, however, the fault alarm is inaccurate, and advance warning cannot be performed.
Disclosure of Invention
In a first aspect, an embodiment of the present application provides a method for diagnosing an operating state of a steam turbine, including:
receiving various operating state parameters of the steam turbine during operation, which are acquired and transmitted by a plurality of different types of sensors;
generating a diagnosis result according to the obtained multiple operation state parameters, a pre-trained neural network diagnosis model and the correlation condition between the historical operation state parameters and the historical operation state parameters for determining the historical diagnosis result;
and transmitting the diagnosis result to a display terminal for displaying.
In a second aspect, an embodiment of the present application further provides a steam turbine operating state diagnostic apparatus, including:
the information receiving unit is configured to receive a plurality of operating state parameters, collected and transmitted by a plurality of different types of sensors, of the steam turbine during operation;
a diagnostic result generation unit configured to generate a diagnostic result according to the obtained multiple operating state parameters, a pre-trained neural network diagnostic model and a historical operating state parameter for determining a historical diagnostic result and an associated condition between the historical operating state parameters;
and the information output unit is configured to transmit the diagnosis result to the 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: the method comprises the steps of receiving various operating state parameters of a steam turbine during operation, which are acquired and transmitted by a plurality of sensors of different types; generating a diagnosis result according to the obtained multiple operation state parameters, a pre-trained neural network 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 transmitted to the display terminal to be displayed, so that before the steam turbine breaks down, fault early warning is carried out on the steam turbine 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 steam turbine, the steam turbine 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 an operating condition of a steam turbine according to an embodiment of the present application;
FIG. 3 is a flow chart of an embodiment of a method for diagnosing an operating condition of a steam turbine according to an embodiment of the present application;
FIG. 4 is a flow chart of an embodiment of a method for diagnosing an operating condition of a steam turbine according to an embodiment of the present application;
fig. 5 is a functional block diagram of a steam turbine operating condition diagnosis device according to an embodiment of the present application;
fig. 6 is a functional block diagram of a steam turbine operating condition diagnosis device according to an embodiment of the present application;
fig. 7 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 obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to 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 a method for diagnosing the operating state of a steam turbine, which is applied to an electronic device 101, wherein the electronic device 101 can be a server, and the server is respectively in communication connection with a display terminal 103 and a plurality of sensors 102 of different types installed on the steam turbine 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, and the like, which are not limited herein. As shown in fig. 2, the method includes:
s21: the method comprises the steps of receiving a plurality of operating state parameters of the steam turbine during operation, which are collected and transmitted by a plurality of different types of sensors 102.
Specifically, a plurality of different types of sensors 102 are installed at different locations of the steam turbine, and the plurality of different types of sensors 102 acquire a plurality of operating condition parameters of the steam turbine in real time. The method receives the data transmitted by the plurality of different types of sensors 102 and receives a plurality of operating state parameters of the steam turbine during operation, which are collected and 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 some sampled data may be missing due to an irresistible force. Thus, S21 may include: the method comprises the steps of periodically receiving various operation state parameters of the steam turbine during operation, which are collected and transmitted by a plurality of different types of sensors 102, and obtaining an average value of various historical operation state parameters before the current time if the collected data at the current sampling time is missing.
The plurality of operating condition parameters may include, for example only, a rotational speed of the turbine, a motor current, shaft vibration, surge, a motor vibration frequency, a motor cooling water temperature, a flow rate of the lubricating oil flowing through the motor, an oil temperature, and the like.
S22: and generating a diagnosis result according to the obtained multiple operation state parameters, a pre-trained neural network diagnosis model and the correlation condition between the historical operation state parameters and the historical operation state parameters for determining the historical diagnosis result.
S23: and transmitting the diagnosis result to the display terminal 103 for display.
Specifically, after the diagnosis result is generated, the diagnosis result is sent to the display terminal 103 for displaying, so as to remind a worker that the current steam turbine may have a fault and that the current operation state of the steam turbine needs to be adjusted.
According to the steam turbine operation state diagnosis method provided by the embodiment of the application, the diagnosis result is generated according to the obtained multiple operation state parameters, the pre-trained neural network diagnosis model and the correlation condition between the historical operation state parameters and the historical operation state parameters for determining the historical diagnosis result, so that before the steam turbine fails, the steam turbine is subjected to fault early warning 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 steam turbine, the steam turbine is prevented from failing, and the working efficiency is improved.
Optionally, the neural network diagnosis model is trained in advance by using 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 as training samples.
The neural network diagnostic model is a self-adaptive model inspired by biology, and completes the learning of training data by simulating the operation of neurons in the human brain. Under the inspiration of neurons in biology, each neuron in a neural network is a calculation module, each input in the module has a weight, all the inputs are weighted and summed, the obtained value is subtracted by a threshold value of the neuron, a value to be excited is obtained, the value to be excited is input into an activation function, the value after excitation is calculated, and finally whether the neuron outputs positive excitation or negative excitation is judged according to the magnitude of the excitation value. The above is the calculation and output process of the neurons in the neural network model in a general form, and it can be seen that the process mainly consists of weighting and biasing the inputs (subtracting the threshold), calculating the excitation value by the activation function, and judging whether to activate or inhibit.
The neural network diagnosis model is constructed as follows: the neural network is initialized, including all weights and thresholds for the neural network size and random initialization that need to be set manually. The embodiment of the application adopts 0-1 Gaussian distribution (namely standard normal distribution) to carry out the random initialization of the threshold and the weight, and the method is adopted to consider a prerequisite assumption of the machine learning algorithm on statistics, namely that all samples are mutually independent, and in the case of enough samples, the distribution of the samples is subjected to Gaussian distribution. This assumption is premised on the central limit theorem. And inputting the value to be excited into the excitation function through a feedforward function to obtain an excitation value through calculation, and transmitting the activation information to the next neuron. And calculating the error between the output of the neural network and the training sample label under the conditions of the current weight and the threshold through a reverse propagation function, and performing reverse propagation to the next process or finishing the training by taking the error as a return value. The core function of the neural network is trained by a stochastic gradient descent function. After gradient descent is applied to each small batch of samples through the updating function, the weight and the threshold of the whole neural network are updated according to the calculated gradient, and the function is called by the random gradient descent function.
Alternatively, as shown in fig. 3, S22 includes:
s31: and judging whether the obtained correlation condition among the multiple operation state parameters is the same as the correlation condition among the multiple historical operation state parameters of the same type, and whether the probability of the fault hidden danger is larger than a preset threshold value according to the historical diagnosis result corresponding to the correlation condition among the multiple historical operation state parameters of the same type, if so, executing S32, and if not, generating S33.
S32: and generating a diagnosis result representing the hidden trouble of the steam turbine.
For example, the plurality of operating state parameters may include a free end temperature of a motor of the steam turbine, a drive end temperature, and a temperature of the lubricating oil flowing through the drive end, and the associated condition may be that a vibration direction of the motor of the steam turbine is inconsistent 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. If the motor vibration direction of the steam turbine is not consistent with the preset vibration direction historically, the temperature of the free end is increased, the temperature of the driving end is unchanged, and the probability of the fault occurrence caused by the temperature increase of the lubricating oil flowing through the driving end is larger than a preset threshold (such as 70%), a diagnosis result representing the fault hidden danger of the steam turbine is generated.
S33: and generating a diagnosis result representing the normal operation of the steam turbine.
Based on the above, if historically the vibration direction of the motor of the turbine is inconsistent with the preset vibration direction, the temperature of the free end is increased, the temperature of the drive end is unchanged, and the probability of the fault occurring due to the temperature increase of the lubricating oil flowing through the drive end is greater than the preset threshold (e.g., 70%), a diagnosis result representing the hidden fault danger of the turbine is generated. If the current conditions associated with the various operating state parameters are that the vibration direction of the motor of the steam turbine is inconsistent with the preset vibration direction, the temperature of the free end is unchanged, the temperature of the driving end is unchanged, and the temperature of the lubricating oil flowing through the driving end is increased, a diagnosis result representing the normal operation of the steam turbine is generated. Moreover, if the vibration direction of the motor of the steam turbine is not consistent with the preset vibration direction historically, the temperature of the free end is increased, the temperature of the driving end is unchanged, and the probability of the temperature increase of the lubricating oil flowing through the driving end being in fault is smaller than a preset threshold (such as 60%), a diagnosis result representing the normal operation of the steam turbine is generated.
Optionally, as shown in fig. 4, the method further includes:
s23: and generating a diagnosis result representing the fault hidden danger of the steam turbine, and 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, the weight coefficient of the free end temperature of the motor of the steam turbine may be set to 0.4, 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 driving end temperature was set to 0.3, the weight coefficient in temperature interval a was 1.3, the weight coefficient in temperature interval B was 1.2, and the weight coefficient in temperature interval C was 1.1. The temperature of the lubricating oil flowing through the drive end was set to 0.3, the weight coefficient in the temperature interval a was 1.3, the weight coefficient in the temperature interval B was 1.2, and the weight coefficient in the temperature interval C was 1.1.
When the multiple operating state parameters include the temperature of the free end of the motor of the steam turbine and the temperature of the free end of the motor of the steam turbine is in a temperature interval A, the temperature of the driving end of the motor of the steam turbine and the temperature of the lubricating oil flowing through the driving end of the steam turbine are in a temperature interval B, and the temperature of the lubricating oil flowing through the driving end of the steam turbine is in a temperature interval C, the severity of the generated fault is 0.4X1.3+0.3X1.2+0.3X1.1=0.99.
Referring to fig. 5, an embodiment of the present application further provides a steam turbine operating state diagnostic apparatus 500, which 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 steam turbine operating condition diagnosing apparatus 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, the corresponding contents in the above embodiment can be referred to for the parts of this embodiment that are not mentioned. The apparatus 500 includes an information receiving unit 501, a diagnosis result generating unit 502, and an information output unit 503. Wherein,
the information receiving unit 501 is configured to receive various operating state parameters of the steam turbine during operation, which are collected and transmitted by a plurality of different types of sensors.
The diagnostic result generation unit 502 is configured to generate a diagnostic result according to the obtained plurality of operating state parameters, a neural network diagnostic model trained in advance, and a historical operating state parameter and a correlation condition between the historical operating state parameters that determine the historical diagnostic result.
The information output unit 503 is configured to transmit the diagnosis result to a display terminal for display.
The neural network diagnosis model is formed by pre-training a training sample according to historical operating state parameters, historical fault diagnosis results and correlation conditions among the historical operating state parameters for determining the historical fault diagnosis results.
When the steam turbine operating state diagnosing apparatus 500 provided in the embodiment of the present application is executed, the following functions may be implemented: the diagnosis result is generated according to the obtained multiple operation state parameters, the pre-trained neural network diagnosis model and the correlation condition between the historical operation state parameters and the historical operation state parameters for determining the historical diagnosis result, so that before the steam turbine fails, the steam turbine is subjected to fault early warning 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 steam turbine, the steam turbine is prevented from failing, and the working efficiency is improved.
Alternatively, the information receiving unit 501 may be specifically configured to periodically receive a plurality of operating state parameters of the steam turbine during operation, which are collected and transmitted by a plurality of different types of sensors, and obtain an average value of a plurality of historical operating state parameters before the current time if the collected data at the current sampling time is missing.
Alternatively, the diagnostic result generating unit 502 may be specifically configured to generate a diagnostic result representing a potential fault of the steam turbine if the obtained association condition between the multiple operating state parameters is the same as the association condition between the multiple historical operating state parameters of the same type, and the probability that the potential fault occurs in the historical diagnostic result corresponding to the association condition between the multiple historical operating state parameters of the same type is greater than a preset threshold.
Optionally, as shown in fig. 6, the apparatus 500 further includes: and a fault severity generation unit 504 configured to generate a fault severity according to the first weight coefficient of each operating state parameter and the second weight coefficient of the threshold range in which each operating state parameter is located, while generating a diagnosis result representing the potential fault of the steam turbine.
When the diagnosis result is a diagnosis result representing that the steam turbine has fault hidden danger, the multiple operation state parameters comprise the temperature of the free end of the motor of the steam turbine, the temperature of the driving end and the temperature of the lubricating oil flowing through the driving end, and the related conditions are that the vibration direction of the motor of the steam turbine 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 temperature of the lubricating oil flowing through the driving 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 of specific embodiments has been presented for purposes of illustration and description. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can 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. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application. Referring to fig. 7, 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. 7, but this 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 the corresponding computer program from the nonvolatile memory into the memory and then operates the computer program to form the steam turbine operating state diagnosis device on the logic level. The processor is used for executing the program stored in the memory and is specifically used for executing the following operations:
receiving various operating state parameters of the steam turbine during operation, which are acquired and transmitted by a plurality of different types of sensors;
and generating a diagnosis result according to the obtained multiple operation state parameters, a pre-trained neural network 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 method executed by the steam turbine operating condition diagnosing apparatus according to the embodiment shown in fig. 5 of the present application may 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 can also execute the method of fig. 2 and implement the functions of the steam turbine operation state diagnosis device in the embodiments shown in fig. 2, fig. 3, and fig. 4, which are not described herein again in this application embodiment.
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 further 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 embodiment shown in fig. 2, 3, and 4, and in particular to perform the following operations:
receiving various operating state parameters of the steam turbine during operation, which are acquired and transmitted by a plurality of different types of sensors;
and generating a diagnosis result according to the obtained multiple operation state parameters, a pre-trained neural network diagnosis model and the correlation condition between the historical operation state parameters and the historical operation state parameters for determining the historical diagnosis result.
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 permanent and non-permanent, removable and non-removable media, may implement the 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 phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of other like elements in a process, method, article, or apparatus comprising the element.
All 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 other embodiments. In particular, as for the system embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and reference may be made to the partial description of the method embodiment for relevant points.
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 (7)
1. A method of diagnosing an operating condition of a steam turbine, comprising:
receiving various operating state parameters of the steam turbine during operation, which are acquired and transmitted by a plurality of different types of sensors;
generating a diagnosis result according to the obtained multiple operation state parameters, a pre-trained neural network diagnosis model and the correlation condition between the historical operation state parameters and the historical operation state parameters for determining the historical diagnosis result;
transmitting the diagnosis result to a display terminal for displaying;
the step of generating the diagnosis result according to the obtained multiple operation state parameters, the pre-trained neural network diagnosis model and the correlation condition between the historical operation state parameters and the historical operation state parameters for determining the historical diagnosis result comprises the following steps:
if the obtained correlation condition among the multiple running state parameters is the same as the correlation condition among the multiple historical running state parameters of the same type, and the probability of the fault hidden danger occurring in the historical diagnosis result corresponding to the correlation condition among the multiple historical running state parameters of the same type is larger than a preset threshold value, generating a diagnosis result representing the fault hidden danger occurring in the steam turbine;
the multiple operation state parameters comprise the temperature of the free end of a motor of the steam turbine, the temperature of the driving end, the temperature of lubricating oil flowing through the driving end and the vibration direction of the motor of the steam turbine, and the association conditions comprise that the vibration direction of the motor of the steam turbine 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 temperature of the lubricating oil flowing through the driving end is increased.
2. The method of claim 1, further comprising: and generating a diagnosis result representing the fault hidden danger of the steam turbine, and generating the fault severity according to the first weight coefficient of each operation state parameter and the second weight coefficient of the threshold range of each operation state parameter.
3. The method according to claim 1, wherein the neural network 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.
4. The method of claim 1, wherein receiving the plurality of operating condition parameters of the steam turbine during operation collected and transmitted by the plurality of different types of sensors comprises:
the method comprises the steps of periodically receiving various operation state parameters of the steam turbine during operation, which are acquired and transmitted by a plurality of different types of sensors, and obtaining an average value of various historical operation state parameters before the current moment if the acquired data at the current sampling moment is missing.
5. A steam turbine operating condition diagnosing apparatus, comprising:
the information receiving unit is configured to receive a plurality of operating state parameters, collected and transmitted by a plurality of different types of sensors, of the steam turbine during operation;
a diagnostic result generation unit configured to generate a diagnostic result according to the obtained multiple operating state parameters, a pre-trained neural network diagnostic model and a historical operating state parameter for determining a historical diagnostic result and an associated condition between the historical operating state parameters;
the information output unit is configured to transmit the diagnosis result to a display terminal for displaying;
the diagnostic result generating unit is specifically configured to generate a diagnostic result representing the hidden trouble of the steam turbine if the obtained correlation condition between the multiple operation state parameters is the same as the correlation condition between the multiple historical operation state parameters of the same type, and the historical diagnostic result corresponding to the correlation condition between the multiple historical operation state parameters of the same type is that the probability of the hidden trouble of the steam turbine is greater than a preset threshold value;
the multiple operation state parameters comprise the temperature of the free end of a motor of the steam turbine, the temperature of the driving end, the temperature of lubricating oil flowing through the driving end and the vibration direction of the motor of the steam turbine, and the association conditions comprise that the vibration direction of the motor of the steam turbine 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 temperature of the lubricating oil flowing through the driving end is increased.
6. 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 4.
7. 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 4.
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