Fault diagnosis method and device for gas turbine system

Abstract

The invention relates to the technical field of gas turbines, and discloses a fault diagnosis method and device for a gas turbine system, the fault diagnosis method and device are applied to turbine equipment of the gas turbine system, and turbine blades in the turbine equipment correspond to initial cleaning time. The method comprises the following steps: acquiring monitoring time sequence data of a plurality of specified parameters of the turbine blade; based on the monitoring time sequence data of the plurality of specified parameters, performing single parameter feature extraction on a single time scale to obtain parameter related features of each specified parameter; performing multi-parameter feature extraction on a plurality of time scales to obtain multi-parameter relevance features; and generating maintenance measures suitable for the turbine blade according to the parameter related characteristics and the multi-parameter relevance characteristics of each specified parameter. According to the method, the scaling condition of the turbine blade in the gas turbine system is subjected to multi-parameter and multi-time scale analysis, and the initial cleaning time is adjusted in real time according to the scaling condition of the turbine blade, so that the fault risk and the maintenance cost of the gas turbine system are effectively reduced.

Description

Technical Field

[0001] The present application relates to the technical field of gas turbines, and in particular to a method and device for diagnosing faults in a gas turbine system. Background Art

[0002] A gas turbine is a thermodynamic mechanical device that converts high-temperature and high-pressure gas generated by burning gas into usable mechanical energy by driving the turbine to rotate. It has the advantages of high thermal efficiency, light weight, small size, high power and fast start-up, and has been widely used in aerospace, ship power and energy generation. The gas turbine system has a complex structure, and its main components usually work under harsh conditions of high temperature and high pressure. Due to frequent start-stop and load changes, the actual operating conditions of the gas turbine system are complex and changeable, which increases the probability of failure of the gas turbine system and affects the normal operation of the gas turbine system.

[0003] Common fault types of gas turbine systems include compressor faults, combustion chamber faults and turbine equipment faults, among which turbine equipment faults include turbine blade fouling. In order to eliminate turbine blade fouling in gas turbine systems, ultrasonic and other methods are usually used for inspection and cleaning in related technologies. However, the inspection and maintenance methods in related technologies need to be improved. Summary of the invention

[0004] The present application provides a fault diagnosis method and device for a gas turbine system, which performs multi-parameter and multi-time scale analysis on the fouling condition of turbine blades in the gas turbine system, and adjusts the cleaning time of the turbine blades in real time according to the fouling condition of the turbine blades, thereby generating maintenance measures that fit the actual fouling condition on the turbine blades, thereby effectively reducing the failure risk and maintenance cost of the gas turbine system.

[0005] In order to achieve the above objectives, the main technical solutions adopted in this application include: In a first aspect, an embodiment of the present application provides a fault diagnosis method for a gas turbine system, which is applied to a turbine device of the gas turbine system, wherein turbine blades in the turbine device have an initial cleaning time corresponding thereto; the method comprises: Acquiring monitoring time series data of a plurality of designated parameters of the turbine blade; wherein the designated parameters are parameters that are affected or changed by the fouling condition on the turbine blade; Based on the monitoring time series data of the multiple specified parameters, single parameter feature extraction is performed on a single time scale to obtain parameter-related features of each specified parameter; and multi-parameter feature extraction is performed on multiple time scales to obtain multi-parameter correlation features; wherein the parameter-related features are used to characterize the trend features of the monitoring time series data of the corresponding specified parameters, and the multi-parameter correlation features are used to characterize the correlation between the specified parameters on multiple time scales; A maintenance measure suitable for the turbine blade is generated according to the parameter-related characteristics of each designated parameter and the multi-parameter correlation characteristics; wherein the maintenance measure includes an adjustment suggestion for the initial cleaning time.

[0006] The fault diagnosis method of the gas turbine system proposed in the embodiment of the present application performs single parameter feature extraction on a single time scale based on multiple monitoring time series data of the turbine blades, and performs multi-parameter feature extraction on multiple time scales based on multiple monitoring time series data, thereby obtaining data features of each of the multiple specified parameters and correlation features on different time scales between the specified parameters. Based on the parameter correlation features of each of the specified parameters and the multi-parameter correlation features of the multiple specified parameters, the scaling of the turbine blades is analyzed in multiple parameters and multiple time scales, thereby improving the accuracy of the fault diagnosis of the gas turbine system, so that corresponding maintenance measures can be taken for the gas turbine system to reduce the failure probability of the gas turbine system. Compared with the related art, the present application performs multi-parameter and multi-time scale analysis on the scaling of the turbine blades, and generates corresponding maintenance measures according to the scaling of the turbine blades, so as to adjust the initial cleaning time of the turbine blades in real time, effectively reducing the risk of failure of the gas turbine system caused by scaling of the turbine blades, while reducing the probability of unnecessary turbine blade cleaning events, reducing the cost waste for cleaning turbine blades, and reducing the maintenance cost of the gas turbine system to a certain extent.

[0007] Optionally, the plurality of specified parameters include flow, pressure and temperature at a corresponding monitoring position of the turbine device; the monitoring time series data includes flow time series data, pressure time series data and temperature time series data; The monitoring time series data based on the multiple specified parameters are used to extract single parameter features on a single time scale to obtain parameter-related features of each specified parameter, including: Extracting a single parameter feature on a single time scale according to the flow time series data to obtain flow-related features of the flow time series data; Extracting a single parameter feature on a single time scale according to the pressure time series data to obtain pressure-related features of the pressure time series data; A single parameter feature is extracted on a single time scale according to the temperature time series data to obtain temperature-related features of the temperature time series data; wherein the parameter-related features include the flow-related features, the pressure-related features and the temperature-related features.

[0008] Optionally, the monitoring time series data based on the multiple specified parameters is used to extract multi-parameter features on multiple time scales to obtain multi-parameter correlation features, including: constructing multi-parameter time series data using the flow time series data, the pressure time series data and the temperature time series data; Multi-parameter feature extraction is performed on multiple time scales according to the multi-parameter time series data to obtain the multi-parameter correlation feature.

[0009] Optionally, generating maintenance measures suitable for the turbine blade according to the parameter-related features of each designated parameter and the multi-parameter correlation features includes: Classifying the fouling level of the turbine blade based on the parameter-related feature of each designated parameter and the multi-parameter correlation feature to obtain the fouling level of the turbine blade; Maintenance measures for the turbine blades are determined based on the fouling level of the turbine blades.

[0010] Optionally, determining the maintenance measures of the turbine blade according to the fouling level of the turbine blade includes any one of the following situations: If the fouling level belongs to the first level, a maintenance measure is generated to maintain the initial cleaning time unchanged; wherein the fouling situation represented by the first level has an effect on the performance of the turbine blade within an allowable range; If the fouling level belongs to the second level, determine the recommended cleaning time corresponding to the second level, and generate maintenance measures to shorten the initial cleaning time based on the recommended cleaning time; wherein the fouling condition represented by the second level has an impact on the performance of the turbine blade that exceeds the allowable range.

[0011] Optionally, generating maintenance measures suitable for the turbine blade according to the parameter-related features of each designated parameter and the multi-parameter correlation features includes: Predicting the scaling condition of the turbine blade based on the parameter-related characteristics of each designated parameter and the multi-parameter correlation characteristics to obtain the predicted scaling condition of the turbine blade; If the predicted scaling condition meets the infrared thermal imaging activation condition, the blade temperature distribution is obtained; wherein the blade temperature distribution is obtained by scanning the turbine blade with an infrared thermal imaging device; Determine appropriate maintenance measures for the turbine blades based on the blade temperature distribution.

[0012] Optionally, the monitoring time series data of the multiple specified parameters are constructed as multi-parameter time series data; The monitoring time series data based on the multiple specified parameters, extracting single parameter features on a single time scale to obtain parameter-related features of each specified parameter; and extracting multi-parameter features on multiple time scales to obtain multi-parameter correlation features, including: Inputting the monitoring time series data of the plurality of designated parameters into the corresponding single parameter feature extraction branches respectively to perform single parameter feature extraction, thereby obtaining parameter-related features of each designated parameter; The multi-parameter time series data is input into a multi-parameter feature extraction branch to perform multi-parameter feature extraction to obtain the multi-parameter correlation feature.

[0013] In a second aspect, an embodiment of the present application provides a fault diagnosis device for a gas turbine system, which is applied to a turbine device of the gas turbine system, wherein turbine blades in the turbine device have an initial cleaning time corresponding thereto; the device comprises: A time series data acquisition module, used to acquire monitoring time series data of a plurality of specified parameters of the turbine blade; wherein the specified parameters are parameters that are affected or changed by the scaling condition on the turbine blade; A parameter feature extraction module, for performing single parameter feature extraction on a single time scale based on the monitoring time series data of the multiple specified parameters to obtain parameter-related features of each specified parameter; and performing multi-parameter feature extraction on multiple time scales to obtain multi-parameter correlation features; wherein the parameter-related features are used to characterize the trend features of the monitoring time series data of the corresponding specified parameters, and the multi-parameter correlation features are used to characterize the correlation between the specified parameters on multiple time scales; A maintenance measure generation module is used to generate maintenance measures suitable for the turbine blades based on the parameter-related characteristics of each specified parameter and the multi-parameter correlation characteristics; wherein the maintenance measures include adjustment suggestions for the initial cleaning time.

[0014] In a third aspect, an embodiment of the present application provides a fault diagnosis device for a gas turbine system, comprising a monitoring unit and a data processing unit; The monitoring unit is used to monitor the turbine blades of the turbine equipment of the gas turbine system to obtain monitoring time series data of multiple specified parameters of the turbine blades; wherein the specified parameters are parameters that are affected or changed by the fouling condition on the turbine blades; The data processing unit is used to extract single parameter features on a single time scale based on the monitoring time series data of the multiple specified parameters to obtain parameter-related features of each specified parameter; and to extract multi-parameter features on multiple time scales to obtain multi-parameter correlation features; A maintenance measure suitable for the turbine blade is generated according to the parameter-related characteristics of each designated parameter and the multi-parameter correlation characteristics; wherein the maintenance measure includes an adjustment suggestion for the initial cleaning time of the turbine blade.

[0015] Optionally, the fault diagnosis device further includes an infrared thermal imaging device; The data processing unit is further used to predict the scaling condition of the turbine blade based on the parameter-related characteristics of each designated parameter and the multi-parameter correlation characteristics to obtain the predicted scaling condition of the turbine blade; The infrared thermal imaging device is used to scan the turbine blade to obtain the blade temperature distribution if the predicted scaling condition reaches the infrared thermal imaging activation condition; The data processing unit is also used to determine maintenance measures suitable for the turbine blades according to the temperature distribution of the blades. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 A step diagram of a method for diagnosing a fault of a gas turbine system provided in an embodiment of the present application; Figure 2 A diagram showing the steps for obtaining parameter-related features in an embodiment of the present application; Figure 3 A schematic diagram of a monitoring position in a gas turbine in an embodiment of the present application; Figure 4 A diagram showing the steps of obtaining multi-parameter correlation characteristics in an embodiment of the present application; Figure 5 This is a diagram of the first step of determining maintenance measures in an embodiment of the present application; Figure 6 A step diagram for generating maintenance measures regarding the initial cleaning time in an embodiment of the present application; Figure 7 A diagram showing the second step of determining maintenance measures in an embodiment of the present application; Figure 8A diagram showing the steps of extracting features using a feature extraction model in an embodiment of the present application; Fig. 9 A module diagram of a fault diagnosis device for a gas turbine system provided in an embodiment of the present application; Fig.10 A structural diagram of a fault diagnosis device for a gas turbine system provided in an embodiment of the present application; Fig.11 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0019] A gas turbine is a thermodynamic mechanical device that converts high-temperature and high-pressure gas generated by burning gas into usable mechanical energy by driving the turbine to rotate. It has the advantages of high thermal efficiency, light weight, small size, high power and fast start-up, and has been widely used in aerospace, ship power and energy generation. The gas turbine system has a complex structure, and its main components usually work under harsh conditions of high temperature and high pressure. Due to frequent start-stop and load changes, the actual operating conditions of the gas turbine system are complex and changeable, which increases the probability of failure of the gas turbine system and affects the normal operation of the gas turbine system.

[0020] Common fault types of gas turbine systems include compressor faults, combustion chamber faults and turbine equipment faults, among which turbine equipment faults include turbine blade fouling, turbine blade corrosion, turbine blade wear and turbine equipment mechanical damage. For faults such as turbine blade corrosion, turbine blade wear and turbine equipment mechanical damage, it is usually necessary to repair or replace the turbine blades or turbine equipment to repair the defective parts of the turbine blades or turbine equipment. For turbine blade fouling, the relevant technology usually adopts the method of regular cleaning of the turbine blades to remove the fouling on the turbine blades.

[0021] However, in actual situations, due to the frequent start and stop and load fluctuations of the gas turbine system, the scaling of the turbine blades in the gas turbine system does not change in a fixed periodic manner. In the regular cleaning process, the turbine blades may only have slight scaling during regular cleaning, and this slight scaling does not affect the actual operation for a period of time, resulting in a waste of maintenance costs; in other cases, the turbine blades may have serious scaling before regular cleaning, which in turn affects the normal operation of the gas turbine system. Therefore, the method of simply relying on regular cleaning to remove turbine blade scaling in the related art not only fails to reduce the risk of failure of the gas turbine system, but also causes unnecessary waste of maintenance costs.

[0022] Based on the above problems, the present application provides a fault diagnosis method and device for a gas turbine system, which is applied to a turbine equipment of a gas turbine system, and the turbine blades in the turbine equipment correspond to an initial cleaning time; the method includes: obtaining monitoring time series data of multiple specified parameters of the turbine blades; based on the monitoring time series data of multiple specified parameters, performing single parameter feature extraction on a single time scale to obtain parameter-related features of each specified parameter; and, performing multi-parameter feature extraction on multiple time scales to obtain multi-parameter correlation features; generating maintenance measures suitable for the turbine blades according to the parameter-related features and multi-parameter correlation features of each specified parameter.

[0023] The fault diagnosis method for the gas turbine system proposed in the present application extracts single parameter features on a single time scale based on multiple monitoring time series data of turbine blades, and extracts multi-parameter features on multiple time scales based on multiple monitoring time series data, thereby obtaining data features of each of the multiple specified parameters and correlation features on different time scales between the specified parameters. Based on the parameter correlation features of each specified parameter and the multi-parameter correlation features of the multiple specified parameters, the fouling of the turbine blades is analyzed on a multi-parameter and multi-time scale basis, thereby improving the accuracy of the fault diagnosis of the gas turbine system, so that corresponding maintenance measures can be taken for the gas turbine system and the failure probability of the gas turbine system can be reduced.

[0024] Compared with the related art, the present application performs multi-parameter and multi-time scale analysis on the scaling of turbine blades, and generates corresponding maintenance measures according to the scaling of turbine blades, so as to adjust the initial cleaning time of turbine blades in real time, thereby improving the cleaning effect of turbine blades, effectively reducing the failure risk of gas turbine system, and at the same time reducing the cost waste for cleaning turbine blades and reducing the maintenance cost of gas turbine system.

[0025] The fault diagnosis method of the gas turbine system provided in this specification can be applied to the turbine equipment of the gas turbine system to diagnose the fouling of the turbine blades in the turbine equipment and generate corresponding maintenance measures. It is understandable that the present application can also be used to diagnose faults of other parts of the turbine equipment and other main components in the gas turbine system, such as the compressor or combustion chamber in the gas turbine system, after adaptive modification.

[0026] According to an embodiment of the present application, an embodiment of a fault diagnosis method for a gas turbine system is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0027] Reference Figure 1 As shown, in this embodiment, a fault diagnosis method for a gas turbine system is provided, which is applied to a turbine device of the gas turbine system, and the turbine blades in the turbine device have an initial cleaning time corresponding thereto; the method comprises: S100. Acquire monitoring time series data of a plurality of designated parameters of the turbine blades; wherein the designated parameters are parameters that are affected by or cause changes in the scaling conditions on the turbine blades.

[0028] S200. Based on the monitoring time series data of multiple specified parameters, single parameter feature extraction is performed on a single time scale to obtain parameter-related features of each specified parameter; and multi-parameter feature extraction is performed on multiple time scales to obtain multi-parameter correlation features; wherein the parameter-related features are used to characterize the trend characteristics of the monitoring time series data of the corresponding specified parameters, and the multi-parameter correlation features are used to characterize the correlation between the specified parameters on multiple time scales.

[0029] S300. Generate maintenance measures suitable for turbine blades according to parameter-related characteristics and multi-parameter correlation characteristics of each specified parameter; wherein the maintenance measures include adjustment suggestions for initial cleaning time.

[0030] The initial cleaning time may be a pre-set time for regular cleaning of turbine blades of the gas turbine system, and the initial cleaning time may be adjusted according to the generated maintenance measures. The time scale may be a time interval for sampling the monitoring time series data, and the time scale may be determined according to the time length of the monitoring time series data, or according to the change of the monitoring time series data, and different time scales may be independent of each other or may be interrelated.

[0031] Specifically, the present application is applied to the turbine equipment of the gas turbine system, and the scaling of the turbine blades is analyzed by acquiring the monitoring time series data of multiple specified parameters of the turbine blades in the turbine equipment. The specified parameters can be determined based on the sensitivity of each parameter to the scaling of the turbine blades and the ability of each parameter to reflect the working state of the turbine blades. It can be understood that when scaling occurs on the turbine blades, the values ​​of the specified parameters will be affected by scaling and change significantly, and the trend characteristics of the values ​​of the specified parameters affected by scaling follow specific rules. The trend characteristics of the specified parameters are analyzed according to the known specific rules, so that the scaling of the turbine blades can be diagnosed.

[0032] The type and selection reason of the specified parameters are described exemplarily. The specified parameters may include at least one of the flow rate, pressure, temperature, vibration, noise and energy conversion efficiency of the turbine equipment. When scaling occurs on the turbine blades, the scaling will change the gas flow characteristics on the surface of the turbine blades, resulting in an increase in the flow resistance of the gas when passing through the turbine blades, thereby affecting the energy conversion efficiency of the turbine blades, causing a decrease in the gas flow rate and gas pressure in the turbine equipment, and causing the temperature of the turbine equipment to rise. At the same time, the scaling of the turbine blades will affect the rotation of the turbine blades, causing the turbine blades to vibrate and generate mechanical noise during operation, affecting the structural stability of the turbine equipment.

[0033] Furthermore, based on the monitoring time series data of multiple specified parameters, the monitoring time series data of each specified parameter is respectively subjected to single parameter feature extraction on a single time scale to obtain parameter-related features of each specified parameter. The parameter-related features may be one or more of the features such as the maximum value, slope, rate of change, fluctuation frequency and fluctuation amplitude of the monitoring time series data. It is understandable that the scaling of turbine blades is usually uneven, so the scaling of turbine blades will cause fluctuating changes in the specified parameters. The fluctuating changes of each specified parameter can characterize the scaling of turbine blades from the perspective of a single parameter.

[0034] Furthermore, based on the monitoring time series data of all specified parameters, multi-parameter feature extraction is performed on multiple time scales to obtain multi-parameter correlation features. The method of multi-parameter feature extraction can be any of the methods such as convolution operation, autocorrelation and cross-correlation analysis, recursive neural network or long short-term memory network. The multi-parameter correlation features obtained by multi-parameter feature extraction characterize the correlation characteristics between the monitoring time series data of the specified parameters, thereby providing a more comprehensive data basis for the fouling analysis of turbine blades and improving the accuracy of gas turbine system fault diagnosis.

[0035] It should be noted that multiple time scales can be determined based on the changes in the monitoring time series data, for example, by analyzing the periodicity of the monitoring time series data, and determining multiple time scales based on the periodicity analysis results. By extracting features at different time scales, the short-term correlation features and long-term correlation features of the monitoring time series data of multiple specified parameters are obtained, thereby capturing the correlation of the monitoring time series data at different time scales.

[0036] Furthermore, by combining the parameter-related characteristics of each specified parameter and the multi-parameter correlation characteristics on multiple time scales, the scaling of turbine blades is analyzed on a multi-parameter and multi-time scale basis based on the complex correlation between different specified parameters and time scales, thereby improving the analysis accuracy and efficiency of turbine blade scaling and thereby improving the accuracy of gas turbine system fault diagnosis.

[0037] Furthermore, after obtaining the parameter-related features and multi-parameter correlation features of each specified parameter, the scaling of the turbine blades is analyzed and predicted according to the parameter-related features and multi-parameter correlation features to obtain the scaling of the turbine blades at the current moment. Based on the scaling of the turbine blades at the current moment, the initial cleaning time of the turbine blades is adjusted. For example, if the turbine blades are only slightly scaled at this moment, the initial cleaning time can be appropriately postponed. If the turbine blades are severely scaled at this moment, the initial cleaning time needs to be advanced to clean the turbine blades in time.

[0038] Compared with the related art, the present application can accurately analyze and predict the fouling of turbine blades in the gas turbine system according to the parameter-related characteristics and multi-parameter correlation characteristics of each specified parameter, thereby adjusting the initial cleaning time of the turbine blades in real time according to the fouling of the turbine blades to adapt to the actual operating conditions of the gas turbine system, effectively improve the cleaning efficiency of the turbine blades, reduce the failure risk of the gas turbine system, and also reduce the maintenance cost of the gas turbine system.

[0039] The fault diagnosis method of the gas turbine system provided in this embodiment performs single parameter feature extraction on a single time scale based on multiple monitoring time series data of turbine blades, and performs multi-parameter feature extraction on multiple time scales based on multiple monitoring time series data, thereby obtaining data features of each of the multiple specified parameters and correlation features on different time scales between the specified parameters. Based on the parameter correlation features of each of the specified parameters and the multi-parameter correlation features of the multiple specified parameters, the scaling of the turbine blades is analyzed on a multi-parameter and multi-time scale basis, thereby improving the accuracy of the fault diagnosis of the gas turbine system, so that corresponding maintenance measures can be taken for the gas turbine system, and the failure probability of the gas turbine system can be reduced.

[0040] Compared with the related art, the present application performs multi-parameter and multi-time scale analysis on the scaling of turbine blades, and generates corresponding maintenance measures according to the scaling of turbine blades, so as to adjust the initial cleaning time of turbine blades in real time, thereby improving the cleaning effect of turbine blades, effectively reducing the failure risk of gas turbine system, and at the same time reducing the cost waste for cleaning turbine blades and reducing the maintenance cost of gas turbine system.

[0041] Reference Figure 2 As shown, as an embodiment of the present application, the multiple specified parameters include flow, pressure and temperature at the corresponding monitoring position of the turbine equipment; the monitoring time series data includes flow time series data, pressure time series data and temperature time series data; based on the monitoring time series data of the multiple specified parameters, a single parameter feature extraction is performed on a single time scale to obtain parameter-related features of each specified parameter, including: S210. Perform single parameter feature extraction on a single time scale based on the flow time series data to obtain flow-related features of the flow time series data.

[0042] S220. Perform single parameter feature extraction on a single time scale based on the pressure time series data to obtain pressure-related features of the pressure time series data.

[0043] S230. Perform single parameter feature extraction on a single time scale based on the temperature time series data to obtain temperature-related features of the temperature time series data; wherein the parameter-related features include flow-related features, pressure-related features and temperature-related features.

[0044] Reference Figure 3As shown, multiple system sections are selected in the gas turbine system for parameter monitoring, including ① gas turbine system air inlet, ② compressor inlet, ③ compressor exhaust port, ④ compressor outlet, ⑤ combustion chamber inlet, ⑥ combustion chamber outlet, ⑦ turbine equipment inlet and ⑧ turbine equipment outlet, where the pressure and temperature at the gas turbine system air inlet are the same as those of the atmosphere, and the flow rate is related to the operating conditions of the gas turbine system. The corresponding monitoring positions of the turbine equipment include the turbine equipment inlet and the turbine equipment outlet. When the gas turbine system is operating normally, the flow rates at the turbine equipment inlet and the turbine equipment outlet are the same and are related to the flow rate of the system section before the turbine equipment. Since there is energy exchange between the gas and the turbine blades after entering the turbine equipment, the pressure at the turbine equipment outlet is usually lower than the pressure at the turbine equipment inlet, and the temperature at the turbine equipment outlet is usually lower than the temperature at the turbine equipment inlet. Similarly, the pressure and temperature at the turbine equipment inlet and the turbine equipment outlet are also related to the pressure and temperature of the system section before the turbine equipment. Therefore, the flow, pressure and temperature at the turbine equipment inlet and turbine equipment outlet not only characterize the fault condition of the turbine equipment itself, but also can characterize the overall fault condition of the gas turbine system to a certain extent.

[0045] Specifically, the parameter-related features include flow-related features, pressure-related features, and temperature-related features. The parameter-related features may be one or more of the features such as the maximum value, slope, rate of change, fluctuation frequency, and fluctuation amplitude of the monitoring time series data. Among them, the slope of the monitoring time series data can be used to analyze the change trend of the monitoring time series data to identify the failure risk of the gas turbine system. The rate of change of the monitoring time series data represents the instantaneous change speed of the monitoring time series data, so as to be able to analyze the sudden failure of the gas turbine system. The single parameter feature extraction is performed on the flow time series data, the pressure time series data, and the temperature time series data on a single time scale, so as to analyze and predict the failure of the turbine equipment from the perspective of multiple parameters, thereby improving the accuracy and precision of fault diagnosis.

[0046] Reference Figure 4 As shown, as an embodiment of the present application, based on the monitoring time series data of multiple specified parameters, multi-parameter feature extraction is performed on multiple time scales to obtain multi-parameter correlation features, including: S240. Construct multi-parameter time series data using flow time series data, pressure time series data and temperature time series data.

[0047] S250. Perform multi-parameter feature extraction on multiple time scales based on the multi-parameter time series data to obtain multi-parameter correlation features.

[0048] Specifically, the process of constructing multi-parameter time series data can be: according to the sampling frequencies of the time series data, the flow time series data, the pressure time series data and the temperature time series data are respectively represented as matrix sequences of corresponding lengths. The matrix sequences obtained according to the flow time series data, the pressure time series data and the temperature time series data are merged, and the missing data due to the different lengths of the time series data are filled with null values ​​to construct the multi-parameter time series data.

[0049] Furthermore, data sampling is performed on the multi-parameter time series data at multiple time scales to obtain multi-parameter time series data at different time scales. Multi-parameter feature extraction is performed on the multi-parameter time series data at different time scales to obtain multi-parameter correlation features. Among them, the method of multi-parameter feature extraction can be any one of the methods such as convolution operation, autocorrelation and cross-correlation analysis, recursive neural network or long short-term memory network. It should be noted that at different time scales, the flow time series data, pressure time series data and temperature time series data may have different correlations. By extracting multi-parameter features from the multi-parameter time series data at multiple time scales, the correlation of the monitoring time series data at different time scales can be learned according to the multi-parameter correlation features, effectively capturing the correlation characteristics between the monitoring time series data, and improving the accuracy of analyzing and predicting the scaling of turbine blades.

[0050] Furthermore, by combining the parameter-related characteristics of flow, pressure and temperature and the multi-parameter correlation characteristics on multiple time scales, the scaling of turbine blades is analyzed on a multi-parameter and multi-time scale basis based on the complex correlation between multiple parameters and time scales, thereby improving the analysis accuracy and efficiency of turbine blade scaling and thereby improving the accuracy of gas turbine system fault diagnosis.

[0051] Reference Figure 5 As shown, as an embodiment of the present application, a maintenance measure suitable for a turbine blade is generated according to the parameter-related features and multi-parameter correlation features of each specified parameter, including: S310. Classify the fouling level of the turbine blade based on the parameter-related characteristics and multi-parameter correlation characteristics of each designated parameter to obtain the fouling level of the turbine blade.

[0052] S320. Determine maintenance measures for turbine blades based on the scaling level of turbine blades.

[0053] Specifically, a classification model can be pre-built according to the parameter-related features and multi-parameter correlation features of the specified parameters, and the type of the classification model can be any one of the models such as logistic regression, support vector machine, decision tree and naive Bayes classifier. The parameter-related features and multi-parameter correlation features of the specified parameters are input into the classification model, and the scaling level of the turbine blade is classified according to the parameter-related features and multi-parameter correlation features by using the classification model to obtain the scaling level of the turbine blade as a basis for generating maintenance measures. The scaling level of the turbine blade can be pre-set, and is usually related to the thickness, material type and coverage of the scaling layer on the turbine blade.

[0054] The scaling level of turbine blades is exemplified. The scaling level of turbine blades can be divided into four levels according to the scaling characteristics, including slight scaling, moderate scaling, severe scaling and extreme scaling. Among them, the scaling characteristic corresponding to slight scaling is that the scaling layer thickness is thin, which has almost no obvious effect on the performance of turbine blades. The scaling characteristic corresponding to moderate scaling is that the scaling layer thickness increases significantly, and the scaling layer covers a large area on the turbine blades, which has a certain impact on the performance of turbine blades. The scaling characteristic corresponding to severe scaling is that the scaling layer thickness is thick and covers most of the area of ​​the turbine blades, which obviously affects the performance of the turbine blades. The scaling characteristic corresponding to extreme scaling is that the scaling layer is thick and hard, and may cause damage to the turbine blades, which has a great impact on the operation of turbine equipment and gas turbine systems. For slight scaling and moderate scaling, regular cleaning can be performed to ensure the normal operation of the gas turbine system, while for severe scaling and extreme scaling, the scaling of the turbine blades needs to be removed by thorough cleaning or replacement of the turbine blades.

[0055] Reference Figure 6 As shown, as an embodiment of the present application, the maintenance measures of the turbine blades are determined according to the fouling level of the turbine blades, including any one of the following situations: S322. If the fouling level belongs to the first level, generate maintenance measures to maintain the initial cleaning time unchanged; wherein the fouling situation characterized by the first level has an impact on the performance of the turbine blades within an allowable range.

[0056] S324. If the scaling level belongs to the second level, determine the recommended cleaning time corresponding to the second level, and generate maintenance measures to shorten the initial cleaning time according to the recommended cleaning time; wherein the scaling situation characterized by the second level has an impact on the performance of the turbine blades that exceeds the allowable range.

[0057] Specifically, the first level in this embodiment may include slight scaling and moderate scaling, at which point the scaling layer has little effect on the performance of the turbine blades, and the scaling on the turbine blades can be removed by regular cleaning to ensure the normal operation of the gas turbine system. The second level in this embodiment may include severe scaling and extreme scaling, at which point the scaling layer has a greater effect on the performance of the turbine blades, and regular cleaning alone cannot effectively ensure the normal operation of the gas turbine system, and the turbine blades need to be cleaned immediately, and the cleaning intensity is greater than that of the turbine blades of the first level. In some embodiments, if the impact of the scaling layer on the performance of the turbine blades exceeds the allowable range, the impact of the scaling layer can also be eliminated by replacing the turbine blades.

[0058] Reference Figure 7 As shown, as an embodiment of the present application, a maintenance measure suitable for a turbine blade is generated according to the parameter-related features and multi-parameter correlation features of each specified parameter, including: S330. Predict the scaling condition of the turbine blade based on the parameter-related characteristics and multi-parameter correlation characteristics of each designated parameter to obtain the predicted scaling condition of the turbine blade.

[0059] S340. If the predicted scaling condition reaches the condition for enabling infrared thermal imaging, the blade temperature distribution is obtained; wherein the blade temperature distribution is obtained by scanning the turbine blade with infrared thermal imaging equipment.

[0060] S350. Determine appropriate maintenance measures for turbine blades based on blade temperature distribution.

[0061] Specifically, when scaling occurs on turbine blades, the heat exchange efficiency at the location covered by the scaling layer may decrease, which in turn causes the local temperature of the turbine blade to rise, forming hot spots on the surface of the turbine blade. The local heat accumulation of the turbine blade will lead to increased thermal stress in the turbine blade. If this condition persists for a long time, the turbine blade will deform, crack or suffer other structural damage, shortening the service life of the turbine blade and affecting the normal operation of the turbine equipment and gas turbine system.

[0062] Based on these issues, in this embodiment, the scaling of the turbine blades is predicted based on the parameter-related characteristics and multi-parameter correlation characteristics of each specified parameter, the predicted scaling of the turbine blades is obtained, and the judgment is made based on the infrared thermal imaging activation conditions. If the scaling on the turbine blades is relatively mild, it will not affect the heat exchange efficiency of the turbine blades. At this time, the turbine blades will not have the problem of uneven temperature distribution, so there is no need to activate the infrared thermal imaging equipment. If the scaling on the turbine blades is relatively serious, the turbine blades may have local heat accumulation. Therefore, in order to accurately analyze the status of the turbine blades, it is necessary to activate the infrared thermal imaging equipment to scan the turbine blades to obtain the blade temperature distribution. The scaling location on the turbine blades can be determined based on the blade temperature distribution, so that targeted cleaning and maintenance can be carried out to optimize the working condition of the turbine blades.

[0063] Reference Figure 8 As shown, as an embodiment of the present application, the monitoring time series data of multiple specified parameters are constructed as multi-parameter time series data; based on the monitoring time series data of multiple specified parameters, single parameter feature extraction is performed on a single time scale to obtain parameter-related features of each specified parameter; and multi-parameter feature extraction is performed on multiple time scales to obtain multi-parameter correlation features, including: S260. Input the monitoring time series data of multiple specified parameters into the corresponding single parameter feature extraction branches respectively to perform single parameter feature extraction, and obtain parameter-related features of each specified parameter.

[0064] S270. Input the multi-parameter time series data into the multi-parameter feature extraction branch to perform multi-parameter feature extraction to obtain multi-parameter correlation features.

[0065] Specifically, a feature extraction model can be pre-constructed based on parameter-related features and multi-parameter correlation features of multiple specified parameters, and the feature extraction model includes a single-parameter feature extraction branch and a multi-parameter feature extraction branch. Among them, there are multiple single-parameter feature extraction branches, each corresponding to each specified parameter, for performing single-parameter feature extraction on the monitoring time series data of the specified parameter, and the multi-parameter feature extraction branch is used to perform multi-parameter feature extraction on the multi-parameter time series data. The single-parameter feature extraction branch and the multi-parameter feature extraction branch are parallel structures, and the feature data output by the two are used together to analyze and predict the scaling of turbine blades.

[0066] Furthermore, the monitoring time series data of each designated parameter is respectively input into the corresponding single parameter feature extraction branch to perform single parameter feature extraction on a single time scale to obtain parameter-related features of each designated parameter. The multi-parameter time series data is input into the multi-parameter feature extraction branch to perform multi-parameter feature extraction on multiple time scales to obtain the multi-parameter correlation features. The scaling condition of the turbine blade is analyzed and predicted by the parameter-related features and the multi-parameter correlation features to obtain the scaling condition of the turbine blade, thereby generating corresponding maintenance measures.

[0067] Accordingly, refer to Fig. 9 As shown, an embodiment of the present application provides a fault diagnosis device for a gas turbine system, which is applied to a turbine device of the gas turbine system, and the turbine blades in the turbine device have an initial cleaning time corresponding thereto; the device comprises: The time series data acquisition module 910 is used to acquire monitoring time series data of multiple specified parameters of the turbine blades; wherein the specified parameters are parameters that are affected or changed by the scaling conditions on the turbine blades.

[0068] The parameter feature extraction module 920 is used to perform single parameter feature extraction on a single time scale based on the monitoring time series data of multiple specified parameters to obtain parameter-related features of each specified parameter; and to perform multi-parameter feature extraction on multiple time scales to obtain multi-parameter correlation features; wherein the parameter correlation features are used to characterize the trend features of the monitoring time series data of the corresponding specified parameters, and the multi-parameter correlation features are used to characterize the correlation between the specified parameters on multiple time scales.

[0069] The maintenance measure generation module 930 is used to generate maintenance measures suitable for turbine blades according to the parameter-related characteristics and multi-parameter correlation characteristics of each specified parameter; wherein the maintenance measures include adjustment suggestions for the initial cleaning time.

[0070] In some optional embodiments, the plurality of specified parameters include flow, pressure and temperature at corresponding monitoring positions of the turbine equipment; the monitoring time series data include flow time series data, pressure time series data and temperature time series data; the parameter feature extraction module 920 includes: The flow-related feature extraction unit is used to extract a single parameter feature on a single time scale according to the flow time series data to obtain the flow-related features of the flow time series data.

[0071] The pressure-related feature extraction unit is used to extract a single parameter feature on a single time scale according to the pressure time series data to obtain the pressure-related features of the pressure time series data.

[0072] The temperature-related feature extraction unit is used to extract a single parameter feature on a single time scale according to the temperature time series data to obtain the temperature-related features of the temperature time series data; wherein the parameter-related features include flow-related features, pressure-related features and temperature-related features.

[0073] In some optional implementations, the parameter feature extraction module 920 further includes: The multi-parameter time series data construction unit is used to construct the multi-parameter time series data using the flow time series data, the pressure time series data and the temperature time series data.

[0074] The multi-parameter correlation feature extraction unit is used to extract multi-parameter features on multiple time scales according to the multi-parameter time series data to obtain multi-parameter correlation features.

[0075] In some optional implementations, the maintenance measure generation module 930 includes: The turbine blade fouling grade classification unit is used to classify the turbine blade fouling grade based on the parameter-related characteristics and multi-parameter correlation characteristics of each specified parameter to obtain the turbine blade fouling grade.

[0076] The maintenance measure determination unit is used to determine the maintenance measures of the turbine blades according to the fouling level of the turbine blades.

[0077] In some optional implementations, the maintenance measure determination unit includes: The first maintenance measure generating subunit is used to generate maintenance measures. If the fouling level belongs to the first level, a maintenance measure is generated to maintain the initial cleaning time unchanged; wherein the fouling situation represented by the first level has an impact on the performance of the turbine blades within an allowable range.

[0078] The second maintenance measure generation subunit is used to generate maintenance measures. If the scaling level belongs to the second level, the recommended cleaning time corresponding to the second level is determined, and maintenance measures for shortening the initial cleaning time are generated according to the recommended cleaning time; wherein the scaling condition represented by the second level has an impact on the performance of the turbine blades beyond the allowable range.

[0079] In some optional implementations, the maintenance measure generation module 930 further includes: The turbine blade fouling prediction unit is used to predict the fouling condition of the turbine blade based on the parameter-related characteristics and multi-parameter correlation characteristics of each specified parameter to obtain the predicted fouling condition of the turbine blade.

[0080] The temperature distribution acquisition unit is used to acquire the temperature distribution. If the predicted scaling condition reaches the infrared thermal imaging activation condition, the blade temperature distribution is acquired; wherein the blade temperature distribution is obtained by scanning the turbine blade with an infrared thermal imaging device.

[0081] The maintenance measure determination unit is used to determine the maintenance measures suitable for the turbine blades according to the temperature distribution of the blades.

[0082] In some optional implementations, the monitoring time series data of multiple specified parameters are constructed as multi-parameter time series data; the parameter feature extraction module 920 further includes: The single parameter feature extraction unit is used to input the monitoring time series data of multiple specified parameters into the corresponding single parameter feature extraction branch to perform single parameter feature extraction to obtain parameter-related features of each specified parameter.

[0083] The multi-parameter feature extraction unit is used to input the multi-parameter time series data into the multi-parameter feature extraction branch to perform multi-parameter feature extraction to obtain multi-parameter correlation features.

[0084] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0085] The fault diagnosis device of the gas turbine system in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0086] Reference Fig.10 As shown, an embodiment of the present application further provides a fault diagnosis device for a gas turbine system, including a monitoring unit 1010 and a data processing unit 1020 .

[0087] The monitoring unit 1010 is used to monitor the turbine blades of the turbine equipment of the gas turbine system to obtain monitoring time series data of multiple specified parameters of the turbine blades; wherein the specified parameters are parameters that are affected or changed by the fouling on the turbine blades.

[0088] The data processing unit 1020 is used to perform single parameter feature extraction on a single time scale based on the monitoring time series data of multiple specified parameters to obtain parameter-related features of each specified parameter; and to perform multi-parameter feature extraction on multiple time scales to obtain multi-parameter correlation features; generate maintenance measures suitable for turbine blades based on the parameter-related features and multi-parameter correlation features of each specified parameter; wherein the maintenance measures include adjustment suggestions for the initial cleaning time of the turbine blades.

[0089] In some embodiments, the fault diagnosis apparatus further includes an infrared thermal imaging device 1030 .

[0090] The data processing unit 1020 is further used to predict the scaling condition of the turbine blade based on the parameter-related characteristics and multi-parameter correlation characteristics of each designated parameter to obtain the predicted scaling condition of the turbine blade.

[0091] The infrared thermal imaging device 1030 is used to scan the turbine blades. If the predicted scaling condition reaches the infrared thermal imaging activation condition, the turbine blades are scanned to obtain the blade temperature distribution.

[0092] The data processing unit 1020 is also used to determine maintenance measures suitable for the turbine blades according to the temperature distribution of the blades.

[0093] See also Fig.11 , Fig.11 It is a structural diagram of a computer device provided by an embodiment of the present application. As shown in the figure, the computer device includes: one or more processors 10, a memory 20, and an interface for connecting various components, including a high-speed interface and a low-speed interface. Each component uses different buses to communicate with each other, and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Fig.11 A processor 10 is taken as an example.

[0094] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0095] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0096] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0097] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0098] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0099] The embodiment of the present application also provides a computer-readable storage medium. The above method according to the embodiment of the present application can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0100] The embodiment of the present application provides a computer program product, which includes computer instructions, which are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method of any embodiment of the present application.

[0101] Although the embodiments of the present application are described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations are all within the scope defined by the appended claims.

[0102] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, 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.

[0103] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0104] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0105] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0106] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0107] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0108] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0109] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0110] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

[0111] Although the embodiments of the present application have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for fault diagnosis of a gas turbine system, characterized in that: The turbine device applied to the gas turbine system has a turbine blade in the turbine device corresponding to an initial cleaning time; the method comprises: Acquiring monitoring time series data of a plurality of designated parameters of the turbine blade; wherein the designated parameters are parameters that are affected or changed by the fouling condition on the turbine blade; Based on the monitoring time series data of the multiple specified parameters, single parameter feature extraction is performed on a single time scale to obtain parameter-related features of each specified parameter; and multi-parameter feature extraction is performed on multiple time scales to obtain multi-parameter correlation features; wherein the parameter-related features are used to characterize the trend features of the monitoring time series data of the corresponding specified parameters, and the multi-parameter correlation features are used to characterize the correlation between the specified parameters on multiple time scales; A maintenance measure suitable for the turbine blade is generated according to the parameter-related characteristics of each designated parameter and the multi-parameter correlation characteristics; wherein the maintenance measure includes an adjustment suggestion for the initial cleaning time.

2. The method according to claim 1, characterized in that The plurality of specified parameters include flow, pressure and temperature at the corresponding monitoring position of the turbine device; the monitoring time series data includes flow time series data, pressure time series data and temperature time series data; The monitoring time series data based on the multiple specified parameters are used to extract single parameter features on a single time scale to obtain parameter-related features of each specified parameter, including: Extracting a single parameter feature on a single time scale according to the flow time series data to obtain flow-related features of the flow time series data; Extracting a single parameter feature on a single time scale according to the pressure time series data to obtain pressure-related features of the pressure time series data; A single parameter feature is extracted on a single time scale according to the temperature time series data to obtain temperature-related features of the temperature time series data; wherein the parameter-related features include the flow-related features, the pressure-related features and the temperature-related features.

3. The method according to claim 2, characterized in that The monitoring time series data based on the multiple specified parameters is used to extract multi-parameter features on multiple time scales to obtain multi-parameter correlation features, including: constructing multi-parameter time series data using the flow time series data, the pressure time series data and the temperature time series data; Multi-parameter feature extraction is performed on multiple time scales according to the multi-parameter time series data to obtain the multi-parameter correlation feature.

4. The method according to claim 1, characterized in that: Generating maintenance measures suitable for the turbine blade according to the parameter-related features of each designated parameter and the multi-parameter correlation features includes: Classifying the fouling level of the turbine blade based on the parameter-related feature of each designated parameter and the multi-parameter correlation feature to obtain the fouling level of the turbine blade; Maintenance measures for the turbine blades are determined based on the fouling level of the turbine blades.

5. The method according to claim 4, characterized in that Determining the maintenance measures of the turbine blade according to the fouling level of the turbine blade includes any one of the following situations: If the fouling level belongs to the first level, a maintenance measure is generated to maintain the initial cleaning time unchanged; wherein the fouling situation represented by the first level has an effect on the performance of the turbine blade within an allowable range; If the fouling level belongs to the second level, determine the recommended cleaning time corresponding to the second level, and generate maintenance measures to shorten the initial cleaning time based on the recommended cleaning time; wherein the fouling condition represented by the second level has an impact on the performance of the turbine blade that exceeds the allowable range.

6. The method according to claim 1, characterized in that Generating maintenance measures suitable for the turbine blade according to the parameter-related features of each designated parameter and the multi-parameter correlation features includes: Predicting the scaling condition of the turbine blade based on the parameter-related characteristics of each designated parameter and the multi-parameter correlation characteristics to obtain the predicted scaling condition of the turbine blade; If the predicted scaling condition meets the infrared thermal imaging activation condition, the blade temperature distribution is obtained; wherein the blade temperature distribution is obtained by scanning the turbine blade with an infrared thermal imaging device; Determine appropriate maintenance measures for the turbine blades based on the blade temperature distribution.

7. The method according to claim 1, characterized in that The monitoring time series data of the multiple specified parameters are constructed as multi-parameter time series data; The monitoring time series data based on the multiple specified parameters are used to extract single parameter features on a single time scale to obtain parameter-related features of each specified parameter; Furthermore, multi-parameter feature extraction is performed on multiple time scales to obtain multi-parameter correlation features, including: Inputting the monitoring time series data of the plurality of designated parameters into the corresponding single parameter feature extraction branches respectively to perform single parameter feature extraction, thereby obtaining parameter-related features of each designated parameter; The multi-parameter time series data is input into a multi-parameter feature extraction branch to perform multi-parameter feature extraction to obtain the multi-parameter correlation feature.

8. A fault diagnosis device for a gas turbine system, characterized in that: A turbine device applied to the gas turbine system, wherein the turbine blades in the turbine device have an initial cleaning time corresponding thereto; The device comprises: A time series data acquisition module, used to acquire monitoring time series data of a plurality of specified parameters of the turbine blade; wherein the specified parameters are parameters that are affected or changed by the scaling condition on the turbine blade; A parameter feature extraction module, for performing single parameter feature extraction on a single time scale based on the monitoring time series data of the multiple specified parameters to obtain parameter-related features of each specified parameter; and performing multi-parameter feature extraction on multiple time scales to obtain multi-parameter correlation features; wherein the parameter-related features are used to characterize the trend features of the monitoring time series data of the corresponding specified parameters, and the multi-parameter correlation features are used to characterize the correlation between the specified parameters on multiple time scales; A maintenance measure generation module is used to generate maintenance measures suitable for the turbine blades based on the parameter-related characteristics of each specified parameter and the multi-parameter correlation characteristics; wherein the maintenance measures include adjustment suggestions for the initial cleaning time.

9. A fault diagnosis device for a gas turbine system, characterized in that: including a monitoring unit and a data processing unit; The monitoring unit is used to monitor the turbine blades of the turbine equipment of the gas turbine system to obtain monitoring time series data of multiple specified parameters of the turbine blades; wherein the specified parameters are parameters that are affected or changed by the fouling condition on the turbine blades; The data processing unit is used to perform single parameter feature extraction on a single time scale based on the monitoring time series data of the multiple specified parameters to obtain parameter-related features of each specified parameter; and to perform multi-parameter feature extraction on multiple time scales to obtain multi-parameter correlation features; generate maintenance measures suitable for the turbine blades based on the parameter-related features of each specified parameter and the multi-parameter correlation features; wherein the maintenance measures include adjustment suggestions for the initial cleaning time of the turbine blades.

10. The device according to claim 9, characterized in that The fault diagnosis device also includes an infrared thermal imaging device; The data processing unit is further used to predict the scaling condition of the turbine blade based on the parameter-related characteristics of each designated parameter and the multi-parameter correlation characteristics to obtain the predicted scaling condition of the turbine blade; The infrared thermal imaging device is used to scan the turbine blades, and if the predicted scaling condition meets the infrared thermal imaging activation condition, the turbine blades are scanned to obtain the blade temperature distribution; The data processing unit is also used to determine maintenance measures suitable for the turbine blades according to the temperature distribution of the blades.

Images