Detection Method, Device, Electronic Device, Storage Medium and System for Gas Turbine

Abstract

The present disclosure relates to a detection method, device, electronic device, storage medium and system for a gas turbine, and relates to the field of thermal control automation. The method includes: obtaining first data collected by a first detection device, where the first detection device is any one of a plurality of detection devices installed on the gas turbine, and the first data is infrared temperature data, temperature field data or ultrasonic field data; comparing the first data with corresponding sample data in a negative sample database; in the case where it is determined that the deviation between the first data and the corresponding sample data in the negative sample database is greater than a first preset value, comparing the first data with corresponding sample data in a positive sample database; in the case where it is determined that the deviation between the first data and the corresponding sample data in the positive sample database is greater than a second preset value, determining that the first data is abnormal, and being able to effectively and accurately detect a gas leakage point.

Description

Technical Field

[0001] The present disclosure relates to the field of thermal control automation, and more particularly, to a detection method, device, electronic device, storage medium and system for a gas turbine. Background Art

[0002] Heavy-duty gas turbines used in power generation have complex extraction pipeline layouts, mostly using flange connections. High-temperature and high-pressure gases flow through the extraction pipelines. The flange gaskets at the connections are prone to aging and damage due to factors such as the vibration of the heavy-duty gas turbine body, off-design conditions, and poor installation, which can easily cause leakage of high-temperature and high-pressure gases, thereby adversely affecting the safety operation of inspection personnel and auxiliary equipment and instruments. Moreover, the leakage points in the gas turbine housing are scattered, and the temperature and noise in the gas turbine housing are relatively high. Therefore, conventional detection methods cannot effectively and accurately detect gas leakage points. Summary of the Invention

[0003] The purpose of the present disclosure is to provide a detection method, device, electronic device, storage medium and system for a gas turbine to solve the problem that the gas turbine cannot effectively and accurately detect gas leakage points.

[0004] To achieve the above purpose, the present disclosure provides a detection method for a gas turbine, which is applied to a gas turbine and includes:

[0005] Obtain first data collected by a first detection device, where the first detection device is any one of a plurality of detection devices installed on the gas turbine, and the first data is infrared temperature data, temperature field data or ultrasonic field data;

[0006] Compare the first data with corresponding sample data in a negative sample database;

[0007] When it is determined that the deviation between the first data and the corresponding sample data in the negative sample database is greater than a first preset value, compare the first data with the corresponding sample data in a positive sample database;

[0008] When it is determined that the deviation between the first data and the corresponding sample data in the positive sample database is greater than a second preset value, determine that the first data is abnormal.

[0009] Optionally, the method further includes:

[0010] Obtain historical detection data, where the historical detection data includes: multiple groups of infrared temperature data, multiple groups of temperature field data, and multiple groups of ultrasonic field data;

[0011] Store the multiple groups of infrared temperature data, the multiple groups of temperature field data, and the multiple groups of ultrasonic field data in a database in matrix form, where the database includes the negative sample database and the positive sample database;

[0012] Among them, the multiple groups of infrared temperature data are the temperature data of the combustor casing obtained by multiple infrared imaging devices installed on the combustor through multiple acquisitions according to a preset first acquisition period. The multiple groups of temperature field data are obtained by multiple thermocouples installed on the combustor through multiple acquisitions according to a preset second acquisition period. The multiple groups of ultrasonic field data are obtained by multiple ultrasonic sensors installed on the combustor through multiple acquisitions according to a preset third acquisition period.

[0013] Optionally, storing the multiple groups of infrared temperature data, the multiple groups of temperature field data, and the multiple groups of ultrasonic field data in a database in matrix form, where the database includes the negative sample database and the positive sample database, includes:

[0014] Establish a spatial temperature distribution matrix of the combustor casing based on the spatial distribution of the multiple infrared imaging devices and the multiple groups of infrared temperature data of the combustor casing, and store the spatial temperature distribution matrix in the database;

[0015] Establish an ambient temperature field matrix based on the spatial distribution of the multiple thermocouples and the multiple groups of temperature field data, and store the ambient temperature field matrix in the database;

[0016] Establish multiple spatial sound field matrices corresponding to multiple frequency bands based on the spatial distribution of the multiple ultrasonic sensors and the multiple groups of ultrasonic data, and store the multiple spatial sound field matrices in the database.

[0017] Calculate a matrix model of an irregular spatial distribution through mathematical operations, and store the matrix model in the database.

[0018] Optionally, the method further includes:

[0019] When it is determined that the deviation between the first data and the corresponding sample data in the negative sample database is less than the first preset value, extract the solution corresponding to the first data in the negative sample database.

[0020] Optionally, the method further includes:

[0021] When it is determined that the first data is abnormal, obtain the on-site situation information inside the combustor casing;

[0022] According to the on-site situation information, store the first data in the positive sample database or the negative sample database.

[0023] Optionally, the on-site situation information includes the content of a specified gas inside the housing. Based on the on-site situation information, storing the first data in the positive sample database or the negative sample database includes:

[0024] When the content of the specified gas inside the housing is within a preset content range, storing the first data in the positive sample database;

[0025] When the content of the specified gas inside the housing is not within the content range, storing the first data in the negative sample database and outputting an alarm message indicating that there is an abnormality in the gas turbine.

[0026] According to a second aspect of the embodiments of the present disclosure, there is provided a gas leakage detection device applied to a heavy-duty gas turbine. The device includes:

[0027] An acquisition module configured to acquire first data collected by a first detection device, where the first detection device is any one of a plurality of detection devices installed on the gas turbine, and the first data is infrared temperature data, temperature field data, or ultrasonic field data;

[0028] A first comparison module configured to compare the first data with corresponding sample data in the negative sample database;

[0029] A second comparison module configured to, when it is determined that the deviation between the first data and the corresponding sample data in the negative sample database is greater than a first preset value, compare the first data with corresponding sample data in the positive sample database;

[0030] A determination module configured to, when it is determined that the deviation between the first data and the corresponding sample data in the positive sample database is greater than a second preset value, determine that the first data is abnormal.

[0031] In a third aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium having a computer program stored thereon, and when the program is executed by a processor, the steps of the method described in the first aspect are implemented.

[0032] In a fourth aspect of the present disclosure, there is provided an electronic device, including:

[0033] A memory having a computer program stored thereon;

[0034] A processor for executing the computer program in the memory to implement the steps of the method described in the first aspect.

[0035] In a fifth aspect of the present disclosure, there is provided a detection system for a gas turbine, including: the electronic device described in the fourth aspect above, and a plurality of infrared imaging devices, a plurality of thermocouples, and a plurality of ultrasonic sensors provided on the gas turbine.

[0036] Through the above technical solution, by obtaining first data collected by a first detection device, where the first detection device is any one of a plurality of detection devices installed on the gas turbine, and the first data is infrared temperature data, temperature field data, or ultrasonic field data; comparing the first data with corresponding sample data in a negative sample database; when it is determined that the deviation between the first data and the corresponding sample data in the negative sample database is greater than a first preset value, comparing the first data with the corresponding sample data in a positive sample database; when it is determined that the deviation between the first data and the corresponding sample data in the positive sample database is greater than a second preset value, determining that the first data is abnormal. Through the above technical solution, a positive and negative sample database of the gas turbine is established, and by detecting and comparing the gas content in the gas turbine casing with the deviation of the positive and negative sample databases, it can be determined whether the gas content in the casing is abnormal, which can effectively and accurately detect gas leakage points, and by combining infrared temperature data, temperature field data, and ultrasonic field data, positions that cannot be detected manually can be effectively detected, which can effectively improve the comprehensiveness of detection.

[0037] Other features and advantages of the present disclosure will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific implementation, they are used to explain the present disclosure, but do not constitute a limitation to the present disclosure. In the drawings:

[0039] Figure 1 is a flowchart of a method for detecting a gas turbine according to an exemplary embodiment of the present disclosure;

[0040] Figure 2 is a flowchart of another method for detecting a gas turbine according to an exemplary embodiment of the present disclosure;

[0041] Figure 3 is a block diagram of a detection device for a gas turbine according to an exemplary embodiment of the present disclosure;

[0042] Figure 4 is a block diagram of an electronic device according to an exemplary embodiment of the present disclosure;

[0043] Figure 5 is a block diagram of another electronic device according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] The following is a detailed description of the specific embodiments of the present disclosure in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only for explaining and illustrating the present disclosure, and are not used to limit the present disclosure.

[0045] It should be noted that all actions of obtaining signals, information, or data in the present disclosure are carried out on the premise of complying with the corresponding data protection regulations and policies of the country where it is located and obtaining the authorization given by the owner of the corresponding device.

[0046] Figure 1 is a flowchart of a detection method for a gas turbine provided according to an exemplary embodiment of the present disclosure. Refer to Figure 1 and the method may include the following steps:

[0047] Step S101, obtain first data collected by a first detection device, where the first detection device is any one of multiple detection devices installed on the gas turbine, and the first data is infrared temperature data, temperature field data, or ultrasonic field data.

[0048] Among them, the above first data can be stored in a database in matrix form. For example, the first data includes a temperature distribution matrix composed of the temperature data of the infrared imaging device acquisition device and the surface temperature of the casing, an ambient temperature field matrix composed of the spatial distribution of thermocouples, and multiple spatial sound field matrices collected by distributed ultrasonic sensors and split according to frequency bands. Optionally, in one implementation, if there is an irregular spatial distribution in the gas turbine pipeline, matrix models (i.e., the above temperature distribution matrix, ambient temperature field matrix, and spatial sound field matrix) can be completed through mathematical operations, such as the mathematical operations may include but are not limited to taking the average value.

[0049] The spatial temperature distribution matrix collected by the infrared imaging device is a set of temperature data of the gas turbine casing collected by the infrared imaging device; the temperature field matrix collected by the thermocouple is a set of gas temperature data of the gas turbine casing collected by the thermocouple; the sound field matrix collected by the distributed ultrasonic sensor is a set of data composed of the vibration frequencies inside the gas turbine casing.

[0050] Step S102, compare the first data with the corresponding sample data in the negative sample database.

[0051] Among them, the above negative sample database refers to a data set formed by accumulating the gas content in the case of gas leakage of the gas turbine.

[0052] Step S103, in the case where it is determined that the deviation between the first data and the corresponding sample data in the negative sample database is greater than a first preset value, compare the first data with the corresponding sample data in the positive sample database.

[0053] Among them, the deviation between the first data and the corresponding sample data in the negative sample database being greater than a first preset value can be understood as: the deviation between the first data and each sample data in the negative sample database is greater than the first preset value. The positive sample database is a data set formed by pre-collecting the gas content under various operating conditions of the unit in the non-leakage case.

[0054] Step S104, when it is determined that the deviation between the first data and the corresponding sample data in the positive sample database is greater than a second preset value, determine that the first data is abnormal.

[0055] Among them, the deviation between the first data and the corresponding sample data in the positive sample database being greater than a first preset value can be understood as the deviation between the first data and each sample data in the positive sample database is greater than the second preset value.

[0056] Through the above technical solution, by obtaining the first data collected by the first detection device, the first detection device being any one of a plurality of detection devices installed on the gas turbine, the first data being infrared temperature data, temperature field data or ultrasonic field data; comparing the first data with the corresponding sample data in the negative sample database; when it is determined that the deviation between the first data and the corresponding sample data in the negative sample database is greater than a first preset value, comparing the first data with the corresponding sample data in the positive sample database; when it is determined that the deviation between the first data and the corresponding sample data in the positive sample database is greater than a second preset value, determining that the first data is abnormal. Through the above technical solution, positive and negative sample databases of the gas turbine are established, and by detecting and comparing the deviation between the gas content in the gas turbine casing and the positive and negative sample databases, it is determined whether the gas content in the casing is abnormal, which can effectively and accurately detect the gas leakage point, and by combining infrared temperature data, temperature field data and ultrasonic field data, positions that cannot be detected manually can be effectively detected, and the comprehensiveness of detection can be effectively improved.

[0057] Figure 2 is a flowchart of another detection method for a gas turbine provided according to an exemplary embodiment of the present disclosure. Refer to Figure 2 and this method includes the following steps:

[0058] First, historical detection data can be obtained. The historical detection data includes: multiple groups of infrared temperature data, multiple groups of temperature field data, and multiple groups of ultrasonic field data. Then, the multiple groups of infrared temperature data, multiple groups of temperature field data, and multiple groups of ultrasonic field data are stored in a database in matrix form. The database includes the negative sample database and the positive sample data. Exemplarily, the following steps S201 to S203 may be included.

[0059] Step S201: Based on the spatial distribution of multiple infrared imaging devices, establish a spatial temperature distribution matrix of the casing of the gas turbine according to the multiple groups of infrared temperature data of the casing of the gas turbine, and store the spatial temperature distribution matrix in the database.

[0060] Among them, multiple groups of infrared temperature data are obtained by collecting the temperature data of the casing of the gas turbine multiple times through multiple infrared imaging devices arranged on the gas turbine according to a preset first collection period.

[0061] Exemplarily, the infrared imaging device detects the infrared energy or heat of the gas turbine through non-contact, converts it into an electrical signal, and then generates the temperature value of the gas turbine. Multiple groups of infrared temperature data can be obtained by collecting the temperature values of the gas turbine multiple times.

[0062] Step S202: Based on the spatial distribution of multiple thermocouples and the multiple groups of temperature field data, establish an ambient temperature field matrix, and store the ambient temperature field matrix in the database.

[0063] Among them, multiple groups of temperature field data are obtained by collecting multiple times through multiple thermocouples arranged on the gas turbine according to a preset second collection period.

[0064] Exemplarily, a thermocouple is a temperature-sensing element. The thermocouple can directly measure the gas temperature inside the casing of the gas turbine and convert the gas temperature signal into a thermoelectromotive force signal, and then convert the thermoelectromotive force signal into the gas temperature inside the casing of the gas turbine through a specific instrument. Multiple groups of temperature field data can be obtained by collecting the gas temperature inside the casing of the gas turbine multiple times.

[0065] Step S203: Based on the spatial distribution of multiple ultrasonic sensors and the multiple groups of ultrasonic data, establish multiple spatial sound field matrices corresponding to multiple frequency bands, and store the multiple spatial sound field matrices in the database.

[0066] Among them, multiple groups of ultrasonic field data are obtained by collecting multiple times through multiple ultrasonic sensors on the gas turbine according to a preset third collection period. The above first collection period, second collection period, and third collection period are the same or different. The collection devices of the above data matrix models have all been explosion-proof processed, and the installation positions of the multiple infrared imaging devices, multiple thermocouples, and multiple ultrasonic sensors set on different gas turbine devices can be the same, partially the same, or different.

[0067] Exemplarily, when it is determined that the gas turbine is normal, that is, there is no gas leakage, multiple spatial sound field data can be obtained by detecting the vibration frequency of the space where the gas turbine is located multiple times through ultrasonic sensors.

[0068] Optionally, the matrix models obtained in steps S201 - S203 can also be supplemented and improved by the method described in step S204.

[0069] Step S204: Calculate a matrix model with an irregular spatial distribution through mathematical operations, and store the matrix model in the database.

[0070] Exemplarily, when there is an irregular spatial distribution in the gas turbine pipeline and there are at least two pieces of data detected at the same point, the data at this point is determined by taking the average of the at least two pieces of data.

[0071] Step S205: Determine whether the deviation between the first data and the corresponding sample data in the negative sample database is less than a first preset value. If the deviation is less than the first preset value, determine that the first data is abnormal, and then execute step S210; if the deviation is greater than the first preset value, execute S206.

[0072] It can be understood that when the first data is infrared temperature data, it can be determined whether the data is abnormal by determining whether the deviation between the infrared temperature data and the infrared temperature data in the negative sample database is less than the first preset value corresponding to the infrared temperature data; when the first data is temperature field data, it can be determined whether the data is abnormal by determining whether the deviation between the temperature field data and the temperature field data in the negative sample database is less than the first preset value corresponding to the temperature field data; when the first data is spatial sound field data, it can be determined whether the data is abnormal by determining whether the deviation between the spatial sound field data and the spatial sound field data in the negative sample database is less than the first preset value corresponding to the spatial sound field data. Among them, the first preset value corresponding to the infrared temperature data, the first preset value corresponding to the temperature field data, and the first preset value corresponding to the spatial sound field data can be the same or different.

[0073] For example, if the temperature of the gas turbine during normal operation is 900 °C and the corresponding first preset value is the preset temperature deviation, which is 100 °C, then when the temperature of the gas turbine is within the range of 800 °C - 1000 °C, the temperature data is positive sample data, and the gas turbine temperature data at this temperature is determined to be normal. When the temperature of the gas turbine is not within the range of 800 °C - 1000 °C, the data is negative sample data, and the gas turbine temperature data at this temperature is determined to be abnormal; if the gas temperature inside the gas turbine housing during normal operation is 1200 °C and the preset temperature deviation is 1000 °C, then when the gas temperature inside the gas turbine housing is within the range of 1100 °C - 1300 °C, the temperature data is positive sample data, and the gas turbine temperature data at this temperature is determined to be normal. When the temperature of the gas turbine is not within the range of 1100 °C - 1300 °C, the data is negative sample data, and the gas turbine temperature data at this temperature is determined to be abnormal; for the ultrasonic data, the corresponding first preset value is the preset vibration frequency deviation. If the vibration frequency of the space where the gas turbine is located is 70 Hz and the preset vibration frequency deviation is 10 Hz, then when the vibration frequency of the space where the gas turbine is located is within the range of 60 Hz - 80 Hz, the frequency data is positive sample data, and the gas turbine data at this vibration frequency is determined to be normal. When the vibration frequency of the space where the gas turbine is located is not within the range of 60 Hz - 80 Hz, the frequency data is negative sample data, and the gas turbine data at this vibration frequency is determined to be abnormal.

[0074] Step S206, determine whether the deviation between the first data and the corresponding sample data in the positive sample database is less than the second preset value. If the deviation is less than the second preset value, determine that the first data is normal and execute step S211. If the deviation is greater than the second preset value, determine that the first data is abnormal and execute S207.

[0075] Step S207, determine whether the on-site situation is abnormal.

[0076] Exemplarily, in the case of determining that the first data is abnormal, the on-site situation information inside the housing of the gas turbine can be obtained, and based on this on-site situation information, it is determined whether the on-site situation is abnormal.

[0077] In one implementation, the on-site situation information may include the specified gas content inside the housing. When the specified gas content inside the housing is within the preset content range, it is determined that the on-site situation is normal. When the specified gas content inside the housing is not within this content range, it is determined that the on-site situation is abnormal. If the on-site situation is abnormal, then execute step S209. If the on-site situation is normal, then execute step S208.

[0078] Among them, whether the first data is normal can characterize whether there is an abnormality at the gas turbine site. When it is determined that the first data is abnormal, it is possible to manually use a temperature detector, a mobile ultrasonic detector, or any device that can be used to detect gas leakage to detect whether there is gas leakage in the gas turbine. It is also possible to control the inspection robot or inspection drone to reach the corresponding site to detect gas leakage. The inspection robot or inspection drone can be equipped with a temperature detector, an ultrasonic detector, an infrared detection device, or any other device that can be used to detect gas leakage.

[0079] Step S208: Store the first data in the negative sample database.

[0080] Exemplarily, if it is determined manually that there is gas leakage in the gas turbine, the gas content data at this time is stored in the negative sample database.

[0081] Step S209: Store the first data in the negative sample database and output an alarm message indicating that there is an abnormality in the gas turbine.

[0082] Among them, outputting an alarm message indicating that there is an abnormality in the gas turbine refers to the warning signal emitted by the gas turbine when it is determined that there is gas leakage in the gas turbine, including but not limited to beeping and speakers. It is also possible to send an alarm message to the bound electronic device through wireless communication, such as an alarm text message, or push an alarm message through the relevant APP.

[0083] Step S210: Extract the solution corresponding to the first data from the negative sample database.

[0084] Exemplarily, the negative sample database can include different solutions corresponding to different gas content concentrations, so that when an abnormality occurs, different solutions are adopted for the gas turbine according to different gas content concentrations. For example, when the concentration reaches a certain threshold range, the gas turbine leakage is relatively serious at this time, and the solution can include closing relevant valves, controlling the gas turbine to shut down, and other solutions.

[0085] Exemplarily, when the user determines that there is gas leakage in the gas turbine, the above-mentioned gas turbine will emit an alarm signal. At this time, the corresponding solution in the negative sample database can be extracted to perform corresponding processing on the gas turbine and the leaked gas.

[0086] Step S211: The process ends.

[0087] Through the above technical solution, by obtaining the first data collected by the first detection device, where the first detection device is any one of multiple detection devices installed on the gas turbine, and the first data is infrared temperature data, temperature field data, or ultrasonic field data; comparing the first data with the corresponding sample data in the negative sample database; when it is determined that the deviation between the first data and the corresponding sample data in the negative sample database is greater than a first preset value, comparing the first data with the corresponding sample data in the positive sample database; when it is determined that the deviation between the first data and the corresponding sample data in the positive sample database is greater than a second preset value, determining that the first data is abnormal. Through the above technical solution, a positive and negative sample database of the gas turbine is established, and by detecting and comparing the deviation of the gas content in the gas turbine casing from the positive and negative sample databases, it is determined whether the gas content in the casing is abnormal, which can effectively and accurately detect the gas leakage point, and by combining infrared temperature data, temperature field data, and ultrasonic field data, it can effectively detect positions that cannot be detected manually, effectively improving the comprehensiveness of detection.

[0088] Figure 3 is a block diagram of a detection device for a gas turbine provided according to an exemplary embodiment of the present disclosure. Refer to Figure 3 The device 300 may include: an acquisition module 301, a first comparison module 302, a second comparison module 303, and a determination module 304.

[0089] The acquisition module 301 is configured to acquire the first data collected by the first detection device, where the first detection device is any one of multiple detection devices installed on the gas turbine, and the first data is infrared temperature data, temperature field data, or ultrasonic field data.

[0090] The first comparison module 302 is configured to compare the first data with the corresponding sample data in the negative sample database.

[0091] The second comparison module 303 is configured to, when it is determined that the deviation between the first data and the corresponding sample data in the negative sample database is greater than a first preset value, compare the first data with the corresponding sample data in the positive sample database.

[0092] The determination module 304 is configured to, when it is determined that the deviation between the first data and the corresponding sample data in the positive sample database is greater than a second preset value, determine that the first data is abnormal.

[0093] Optionally, the device 300 may further include:

[0094] A historical data acquisition module, configured to acquire historical detection data, where the historical detection data includes: multiple groups of infrared temperature data, multiple groups of temperature field data, and multiple groups of ultrasonic field data;

[0095] The first storage module is used to store the multiple groups of infrared temperature data, the multiple groups of temperature field data, and the multiple groups of ultrasonic field data in a matrix form into a database, where the database includes the negative sample database and the positive sample data;

[0096] Among them, the multiple groups of infrared temperature data are obtained by multiple acquisitions of the temperature data of the combustor casing by multiple infrared imaging devices arranged on the combustor according to a preset first acquisition period, the multiple groups of temperature field data are obtained by multiple acquisitions by multiple thermocouples arranged on the combustor according to a preset second acquisition period, and the multiple groups of ultrasonic field data are obtained by multiple acquisitions by multiple ultrasonic sensors arranged on the combustor according to a preset third acquisition period.

[0097] Optionally, the first storage module may include:

[0098] The first storage sub-module is used to establish a spatial temperature distribution matrix of the combustor casing based on the spatial distribution of the multiple infrared imaging devices and the multiple groups of infrared temperature data of the combustor casing, and store the spatial temperature distribution matrix into the database;

[0099] The second storage sub-module is used to establish an environmental temperature field matrix based on the spatial distribution of the multiple thermocouples and the multiple groups of temperature field data, and store the environmental temperature field matrix into the database;

[0100] The third storage sub-module is used to establish multiple spatial sound field matrices corresponding to multiple frequency bands based on the spatial distribution of the multiple ultrasonic sensors and the multiple groups of ultrasonic data, and store the multiple spatial sound field matrices into the database.

[0101] The fourth storage sub-module is used to calculate a matrix model with an irregular spatial distribution through mathematical operations and store the matrix model into the database.

[0102] Optionally, the device 300 may further include:

[0103] The solution determination module is used to extract the solution corresponding to the first data in the negative sample database when it is determined that the deviation between the first data and the corresponding sample data in the negative sample database is less than the first preset value.

[0104] Optionally, the device 300 may further include:

[0105] The on-site information acquisition module is used to acquire the on-site situation information inside the combustor casing when it is determined that the first data is abnormal.

[0106] The second storage module is used to store the first data into the positive sample database when the content of the specified gas inside the casing is within a preset content range.

[0107] Optionally, the on-site situation information includes the specified gas content inside the housing, and the second storage module may include:

[0108] A fifth storage sub-module, configured to store the first data into the positive sample database when the specified gas content inside the housing is within a preset content range;

[0109] A sixth storage sub-module, when the specified gas content inside the housing is not within the content range, stores the first data into the negative sample database and outputs an alarm message indicating that there is an abnormality in the gas turbine.

[0110] Through the above technical solution, by acquiring the first data collected by the first detection device, where the first detection device is any one of a plurality of detection devices installed on the gas turbine, and the first data is infrared temperature data, temperature field data, or ultrasonic field data; comparing the first data with the corresponding sample data in the negative sample database; when it is determined that the deviation between the first data and the corresponding sample data in the negative sample database is greater than a first preset value, comparing the first data with the corresponding sample data in the positive sample database; when it is determined that the deviation between the first data and the corresponding sample data in the positive sample database is greater than a second preset value, determining that the first data is abnormal. Through the above technical solution, a positive and negative sample database of the gas turbine is established, and by detecting and comparing the deviation between the gas content inside the gas turbine housing and the positive and negative sample databases, it is determined whether the gas content inside the housing is abnormal, which can effectively and accurately detect gas leakage points, and by combining infrared temperature data, temperature field data, and ultrasonic field data, positions that cannot be detected manually can be effectively detected, which can effectively improve the comprehensiveness of detection.

[0111] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment related to the method, and will not be elaborated here.

[0112] Figure 4 is a block diagram of an electronic device 400 shown according to an exemplary embodiment. As Figure 4 shown, the electronic device 400 may include: a processor 401, a memory 402. The electronic device 400 may further include one or more of a multimedia component 403, an input / output (I / O) interface 404, and a communication component 405.

[0113] Among them, the processor 401 is used to control the overall operation of the electronic device 400 to complete all or part of the steps in the above gas turbine detection method. The memory 402 is used to store various types of data to support the operation of the electronic device 400. These data may include, for example, instructions for any application or method operating on the electronic device 400, as well as application-related data, such as contact data, received and sent messages, pictures, audio, video, and so on. The memory 402 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The multimedia component 403 may include a screen and an audio component. Among them, the screen may be a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 402 or sent through the communication component 405. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 404 provides an interface between the processor 401 and other interface modules, and the above other interface modules may be a keyboard, a mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 405 is used for wired or wireless communication between the electronic device 400 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IoT, eMTC, or other 5G, etc., or a combination of one or more of them is not limited here. Therefore, the corresponding communication component 405 may include: a Wi-Fi module, a Bluetooth module, an NFC module, and so on.

[0114] In an exemplary embodiment, the electronic device 400 may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components, and is used to execute the above-mentioned gas turbine detection method.

[0115] In another exemplary embodiment, a computer-readable storage medium including program instructions is further provided. When the program instructions are executed by a processor, the steps of the above-mentioned gas turbine detection method are implemented. For example, the computer-readable storage medium may be the above-mentioned memory 402 including program instructions, and the above-mentioned program instructions may be executed by the processor 401 of the electronic device 400 to complete the above-mentioned gas turbine detection method.

[0116] Figure 5 FIG. is a block diagram of an electronic device 1500 shown according to an exemplary embodiment. For example, the electronic device 1500 may be provided as a server. Referring to Figure 5 , the electronic device 1500 includes a processor 1522, the number of which may be one or more, and a memory 1532 for storing computer programs executable by the processor 1522. The computer programs stored in the memory 1532 may include one or more modules each corresponding to a set of instructions. In addition, the processor 1522 may be configured to execute the computer program to execute the above-mentioned gas turbine detection method.

[0117] In addition, the electronic device 1500 may further include a power supply component 1526 and a communication component 1550. The power supply component 1526 may be configured to perform power management of the electronic device 1500, and the communication component 1550 may be configured to implement communication of the electronic device 1500, for example, wired or wireless communication. In addition, the electronic device 1500 may further include an input / output (I / O) interface 1558. The electronic device 1500 may operate based on an operating system stored in the memory 1532, such as Windows Server TM , Mac OS X TM , Unix TM , Linux TM and so on.

[0118] In another exemplary embodiment, a computer-readable storage medium including program instructions is further provided. When the program instructions are executed by a processor, the steps of the above-described detection method for a gas turbine are implemented. For example, the non-transitory computer-readable storage medium may be the above-described memory 1532 including program instructions, and the above program instructions may be executed by the processor 1522 of the electronic device 1500 to complete the above-described detection method for a gas turbine.

[0119] In another exemplary embodiment, a computer program product is further provided. The computer program product includes a computer program executable by a programmable device, and the computer program has a code portion for executing the above-described detection method for a gas turbine when executed by the programmable device.

[0120] The preferred embodiments of the present disclosure have been described in detail above in conjunction with the accompanying drawings. However, the present disclosure is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present disclosure, various simple modifications can be made to the technical solutions of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0121] In addition, it should be noted that, in the above specific embodiments, the various specific technical features described can be combined in any suitable manner without conflict. To avoid unnecessary repetition, the present disclosure does not separately describe various possible combination methods.

[0122] Furthermore, any combination can be made between various different embodiments of the present disclosure as long as it does not violate the idea of the present disclosure, and it should also be regarded as the content disclosed by the present disclosure.

Claims

1. A detection method for a gas turbine, characterized in that, applied to a gas turbine, it includes: Obtaining first data collected by a first detection device, where the first detection device is any one of multiple detection devices installed on the gas turbine, and the first data is infrared temperature data, temperature field data, or ultrasonic field data; Comparing the first data with corresponding sample data in a negative sample database; When it is determined that the deviation between the first data and the corresponding sample data in the negative sample database is greater than a first preset value, comparing the first data with the corresponding sample data in a positive sample database; When it is determined that the deviation between the first data and the corresponding sample data in the positive sample database is greater than a second preset value, determining that the first data is abnormal; Obtaining historical detection data, where the historical detection data includes: multiple groups of infrared temperature data, multiple groups of temperature field data, and multiple groups of ultrasonic field data; among them, the multiple groups of infrared temperature data are temperature data of the gas turbine casing obtained by multiple infrared imaging devices installed on the gas turbine through multiple acquisitions according to a preset first acquisition period, the multiple groups of temperature field data are obtained by multiple thermocouples installed on the gas turbine through multiple acquisitions according to a preset second acquisition period, and the multiple groups of ultrasonic field data are obtained by multiple ultrasonic sensors installed on the gas turbine through multiple acquisitions according to a preset third acquisition period; Establishing a spatial temperature distribution matrix of the gas turbine casing based on the spatial distribution of the multiple infrared imaging devices and the multiple groups of infrared temperature data of the gas turbine casing, and storing the spatial temperature distribution matrix in the database, where the database includes the negative sample database and the positive sample database; Establishing an ambient temperature field matrix based on the spatial distribution of the multiple thermocouples and the multiple groups of temperature field data, and storing the ambient temperature field matrix in the database; Establishing multiple spatial sound field matrices corresponding to multiple frequency bands based on the spatial distribution of the multiple ultrasonic sensors and the multiple groups of ultrasonic field data, and storing the multiple spatial sound field matrices in the database; Calculating a matrix model with an irregular spatial distribution through mathematical operations, and storing the matrix model in the database.

2. The method according to claim 1, characterized in that, the method further includes: When it is determined that the deviation between the first data and the corresponding sample data in the negative sample database is less than the first preset value, extracting a solution corresponding to the first data in the negative sample database.

3. The method according to claim 1, characterized in that, the method further includes: When it is determined that the first data is abnormal, obtaining on-site situation information inside the casing of the gas turbine; According to the on-site situation information, storing the first data in the positive sample database or the negative sample database.

4. The method according to claim 3, characterized in that, The on-site situation information includes the specified gas content inside the housing. According to the on-site situation information, storing the first data in the positive sample database or the negative sample database includes: When the specified gas content inside the housing is within a preset content range, storing the first data in the positive sample database; When the specified gas content inside the housing is not within the content range, storing the first data in the negative sample database and outputting an alarm message indicating that there is an abnormality in the gas turbine.

5. A gas leakage detection device, characterized in that, applied to a heavy-duty gas turbine, the device includes: An acquisition module configured to acquire first data collected by a first detection device, where the first detection device is any one of a plurality of detection devices installed on the gas turbine, and the first data is infrared temperature data, temperature field data, or ultrasonic field data; A first comparison module configured to compare the first data with corresponding sample data in the negative sample database; A second comparison module configured to, when it is determined that the deviation between the first data and the corresponding sample data in the negative sample database is greater than a first preset value, compare the first data with corresponding sample data in the positive sample database; A determination module configured to, when it is determined that the deviation between the first data and the corresponding sample data in the positive sample database is greater than a second preset value, determine that the first data is abnormal; A historical data acquisition module for acquiring historical detection data, where the historical detection data includes: multiple groups of infrared temperature data, multiple groups of temperature field data, and multiple groups of ultrasonic field data; among them, the multiple groups of infrared temperature data are obtained by multiple infrared imaging devices installed on the gas turbine collecting the temperature data of the gas turbine housing according to a preset first acquisition period, the multiple groups of temperature field data are obtained by multiple thermocouples installed on the gas turbine collecting multiple times according to a preset second acquisition period, and the multiple groups of ultrasonic field data are obtained by multiple ultrasonic sensors installed on the gas turbine collecting multiple times according to a preset third acquisition period; A first storage module for: Establishing a spatial temperature distribution matrix of the gas turbine housing based on the spatial distribution of the multiple infrared imaging devices and the multiple groups of infrared temperature data of the gas turbine housing, and storing the spatial temperature distribution matrix in the database, where the database includes the negative sample database and the positive sample database; Establishing an ambient temperature field matrix based on the spatial distribution of the multiple thermocouples and the multiple groups of temperature field data, and storing the ambient temperature field matrix in the database; Establishing multiple spatial sound field matrices corresponding to multiple frequency bands based on the spatial distribution of the multiple ultrasonic sensors and the multiple groups of ultrasonic field data, and storing the multiple spatial sound field matrices in the database; Calculating a matrix model with an irregular spatial distribution through mathematical operations and storing the matrix model in the database.

6. A non-transitory computer-readable storage medium, on which a computer program is stored, characterized in that, When the program is executed by a processor, it implements the steps of the method according to any one of claims 1-4.

7. An electronic device, characterized in that it comprises: a memory on which a computer program is stored; a processor for executing the computer program in the memory to implement the steps of the method according to any one of claims 1-4.

8. A detection system for a gas turbine, characterized in that it comprises: the electronic device according to claim 7, and a plurality of infrared imaging devices, a plurality of thermocouples and a plurality of ultrasonic sensors arranged on the gas turbine.

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