State monitoring method, device and equipment for key transfer equipment of power station and storage medium

Abstract

The invention discloses a state monitoring method, device and equipment for power station key transfer equipment and a storage medium, and relates to the technical field of thermal power production, and the state monitoring method for the power station key transfer equipment comprises the steps: determining a steam turbine state monitoring result according to key index parameters, a preset discrimination algorithm and an abnormal criterion rule base; determining a gearbox state monitoring result according to the gearbox vibration image and a heterogeneous calculation optimization mode; determining a state monitoring result of the rotary auxiliary machine according to the vibration data of the rotary auxiliary machine and a fault evaluation database; and obtaining a state monitoring result of the key rotating machine equipment of the power station according to the state monitoring result of the steam turbine, the state monitoring result of the gearbox and the state monitoring result of the rotating auxiliary machine. By monitoring the health states of a steam turbine, a gas turbine auxiliary gear box and a key rotating auxiliary machine, the safe reliability and the adaptive capacity of key rotating machine equipment of a unit are improved, and online monitoring and early warning of the key rotating machine equipment of a power station are achieved.

Description

Technical Field

[0001] The present application relates to the field of thermal power generation technology, and in particular to a method, device, equipment and storage medium for status monitoring of key switching equipment in a power station. Background Art

[0002] In recent years, researchers have begun to combine visual measurement technology with the field of vibration monitoring to explore the application and development of visual vibration measurement and monitoring systems. At present, some research teams have begun to explore the application of visual vibration measurement and monitoring systems in the engineering field. For example, in the field of civil engineering, machine vision sensing technology is used to perform multi-point synchronous measurement of building structural health and large-scale structural monitoring. However, at present, visual vibration measurement and monitoring systems are still blank in the field of safety monitoring of thermal power units, and related applications are still in the exploratory stage. There are a series of challenges and obstacles. Therefore, how to achieve online monitoring and early warning of key transfer equipment in power plants has become an urgent problem to be solved. Summary of the invention

[0003] The main purpose of the present application is to provide a method, device, equipment and storage medium for status monitoring of key transition equipment in a power plant, aiming to solve the technical problem of online monitoring and early warning of key transition equipment in a power plant.

[0004] To achieve the above purpose, the present application proposes a method for monitoring the status of key transfer equipment in a power station, the method comprising:

[0005] Determine the turbine status monitoring results based on key indicator parameters, preset identification algorithms and abnormal judgment rule base;

[0006] Determine the gearbox condition monitoring results based on the gearbox vibration image and heterogeneous computing optimization method;

[0007] Determine the rotating auxiliary machine condition monitoring results based on the rotating auxiliary machine vibration data and fault assessment database;

[0008] The status monitoring results of the key transfer equipment of the power station are obtained according to the status monitoring results of the steam turbine, the status monitoring results of the gear box and the status monitoring results of the rotating auxiliary machine.

[0009] In one embodiment, the step of determining the turbine status monitoring result according to the key indicator parameters, the preset identification algorithm and the abnormality judgment rule base includes:

[0010] Perform feature recognition on key indicator parameters according to a preset identification algorithm to obtain state feature parameters;

[0011] The turbine state monitoring result is determined according to the state characteristic parameters and the abnormality judgment rule base.

[0012] In one embodiment, after the step of determining the turbine state monitoring result according to the state characteristic parameter and the abnormality judgment rule base, the method further includes:

[0013] Determine the parameter growth rate corresponding to each parameter according to the key indicator parameters;

[0014] Compare the parameter growth rate corresponding to each parameter with the growth rate threshold to obtain a growth rate comparison result;

[0015] The turbine trend warning information is generated according to the increase rate comparison result.

[0016] In one embodiment, the step of determining the gearbox state monitoring result according to the gearbox vibration image and the heterogeneous computing optimization method includes:

[0017] Preprocessing the gearbox vibration image to obtain a preprocessed vibration image;

[0018] Determining vibration characteristic information according to the preprocessed vibration image and a preset image processing algorithm;

[0019] Performing spectrum analysis on the vibration characteristic information to obtain frequency spectrum information and key characteristic information;

[0020] The gearbox condition monitoring result is determined according to the frequency spectrum information, the key feature information and the heterogeneous computing optimization method.

[0021] In one embodiment, the step of performing spectrum analysis on the vibration characteristic information to obtain frequency spectrum information and key characteristic information includes:

[0022] Obtaining target coordinate data according to the vibration characteristic information, the three-dimensional reconstruction algorithm and the device coordinate system;

[0023] Obtain vibration monitoring data according to the target coordinate data, the signal processing algorithm and the displacement correction algorithm;

[0024] Performing spectrum analysis on the vibration monitoring data to obtain frequency spectrum information and key feature information.

[0025] In one embodiment, the step of determining the rotating auxiliary machine state monitoring result according to the rotating auxiliary machine vibration data and the fault assessment database comprises:

[0026] Perform fault feature recognition on the vibration data of the rotating auxiliary machine to obtain vibration fault features;

[0027] Determine a fault assessment result according to the vibration fault characteristics and a fault assessment database;

[0028] The rotating auxiliary machine state monitoring result is determined according to the fault assessment result.

[0029] In one embodiment, after the step of determining the rotating auxiliary machine state monitoring result according to the fault assessment result, the method further includes:

[0030] Establish a rotating auxiliary machine monitoring model based on historical state parameters and rotating auxiliary machine failure mechanisms;

[0031] Obtaining a fault warning result according to the rotating auxiliary machine monitoring model and the rotating auxiliary machine vibration data;

[0032] The rotating auxiliary machine fault warning information is generated according to the fault warning result.

[0033] In addition, to achieve the above-mentioned purpose, the present application also proposes a state monitoring device for key transfer equipment of a power station, the state monitoring device for key transfer equipment of a power station comprising:

[0034] Monitoring module, used to determine the monitoring results of the steam turbine status according to key indicator parameters, preset identification algorithms and abnormal judgment rule base;

[0035] The monitoring module is further used to determine the gearbox state monitoring result according to the gearbox vibration image and the heterogeneous computing optimization method;

[0036] The monitoring module is further used to determine the monitoring result of the state of the rotating auxiliary machine according to the vibration data of the rotating auxiliary machine and the fault assessment database;

[0037] The processing module is used to obtain the status monitoring result of the key transfer equipment of the power station according to the status monitoring result of the steam turbine, the status monitoring result of the gear box and the status monitoring result of the rotating auxiliary machine.

[0038] In addition, to achieve the above-mentioned objectives, the present application also proposes a status monitoring device for key transition equipment of a power plant, the device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the status monitoring method for key transition equipment of a power plant as described above.

[0039] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the status monitoring method of key transfer equipment of the power plant as described above are implemented.

[0040] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the condition monitoring method of key power station transfer equipment as described above.

[0041] This application determines the turbine status monitoring results according to key indicator parameters, preset identification algorithms, and abnormal judgment rule bases; determines the gearbox status monitoring results according to the gearbox vibration image and heterogeneous computing optimization method; determines the rotating auxiliary machine status monitoring results according to the rotating auxiliary machine vibration data and fault assessment database; and obtains the status monitoring results of the key transfer equipment of the power station according to the turbine status monitoring results, the gearbox status monitoring results, and the rotating auxiliary machine status monitoring results. By monitoring the health status of the steam turbine, gas turbine auxiliary gearbox, and key rotating auxiliary machines, the safety, reliability, and adaptability of the key transfer equipment of the unit are improved, and online monitoring and early warning of the key transfer equipment of the power station are realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0043] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0044] Figure 1 A flow chart of the first embodiment of the method for monitoring the state of key transfer equipment in a power station provided in the present application;

[0045] Figure 2 A schematic diagram of the health status monitoring and trend warning effect of a steam turbine provided in the first embodiment of the method for status monitoring of key transfer equipment of a power station of the present application;

[0046] Figure 3 A schematic diagram of a steam turbine health status monitoring and trend warning workflow provided in Embodiment 1 of the method for status monitoring of key power station transfer equipment of the present application;

[0047] Figure 4 A flow chart of Embodiment 2 of the method for monitoring the state of key transfer equipment of a power station provided in this application;

[0048] Figure 5 A schematic diagram of the installation position of a rolling bearing vibration sensor provided in Embodiment 2 of the method for monitoring the condition of key transfer equipment of a power station of the present application;

[0049] Figure 6 A schematic diagram of the position and installation of a sliding bearing vibration sensor provided in Embodiment 2 of the method for monitoring the condition of key transfer equipment in a power station of the present application;

[0050] Figure 7A schematic diagram of the arrangement of condensate pump measuring points provided in Embodiment 2 of the method for monitoring the state of key transfer equipment of a power station in this application;

[0051] Figure 8 A schematic diagram of an online process flow of maintenance decision-making provided in Embodiment 2 of the method for condition monitoring of key transfer equipment of a power station of the present application;

[0052] Fig. 9 This is a schematic diagram of the module structure of a state monitoring device for key transfer equipment of a power station according to an embodiment of the present application;

[0053] Fig.10 Schematic diagram of the equipment structure of the hardware operating environment involved in the state monitoring method of key power station transfer equipment in the embodiment of the present application.

[0054] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0055] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0056] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0057] The main solutions of the embodiments of the present application are: determining the turbine status monitoring results according to key indicator parameters, preset identification algorithms and abnormal judgment rule bases; determining the gearbox status monitoring results according to the gearbox vibration image and heterogeneous computing optimization method; determining the rotating auxiliary machine status monitoring results according to the rotating auxiliary machine vibration data and the fault assessment database; obtaining the status monitoring results of the key transfer equipment of the power station according to the turbine status monitoring results, the gearbox status monitoring results and the rotating auxiliary machine status monitoring results.

[0058] In recent years, researchers have begun to combine visual measurement technology with the field of vibration monitoring to explore the application and development of visual vibration measurement and monitoring systems. At present, some research teams have begun to explore the application of visual vibration measurement and monitoring systems in the engineering field. For example, in the field of civil engineering, machine vision sensing technology is used to perform multi-point synchronous measurement of building structural health and large-scale structural monitoring. However, at present, visual vibration measurement and monitoring systems are still blank in the field of safety monitoring of thermal power units, and related applications are still in the exploratory stage. There are a series of challenges and obstacles. Therefore, how to achieve online monitoring and early warning of key transfer equipment in power plants has become an urgent problem to be solved.

[0059] This application determines the turbine status monitoring results according to key indicator parameters, preset identification algorithms, and abnormal judgment rule bases; determines the gearbox status monitoring results according to the gearbox vibration image and heterogeneous computing optimization method; determines the rotating auxiliary machine status monitoring results according to the rotating auxiliary machine vibration data and fault assessment database; and obtains the status monitoring results of the key transfer equipment of the power station according to the turbine status monitoring results, the gearbox status monitoring results, and the rotating auxiliary machine status monitoring results. By monitoring the health status of the steam turbine, gas turbine auxiliary gearbox, and key rotating auxiliary machines, the safety, reliability, and adaptability of the key transfer equipment of the unit are improved, and online monitoring and early warning of the key transfer equipment of the power station are realized.

[0060] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or a state monitoring device of key transfer equipment of a power station that can realize the above functions, etc. The following takes the state monitoring device of key transfer equipment of a power station as an example of the execution subject to illustrate this embodiment and the following embodiments.

[0061] Based on this, the embodiment of the present application provides a method for monitoring the status of key transfer equipment in a power station, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the method for monitoring the status of key transfer equipment in a power station according to the present application.

[0062] In this embodiment, the state monitoring method of the key transfer equipment of the power station includes steps S10 to S40:

[0063] Step S10, determining the turbine status monitoring result according to the key indicator parameters, the preset identification algorithm and the abnormality judgment rule base;

[0064] It should be noted that this embodiment carries out research work in three aspects: steam turbine health status monitoring and trend warning, large-field-of-view high-precision health status monitoring and warning of gas turbine auxiliary gearbox, and key rotating auxiliary equipment health status monitoring and warning, and maintenance decision tracking and optimization. It aims to solve the technical difficulties of online monitoring and early warning of key transfer equipment in power plants, support the development of maintenance decision optimization, and effectively improve the safety, reliability and adaptability of key transfer equipment of the unit.

[0065] It can be understood that the key indicator parameters refer to the key indicator parameters of the safety of the turbine, which mainly include vibration parameters and other process parameters. The preset identification algorithm refers to the pre-set intelligent identification algorithm. The abnormal judgment rule base refers to the pre-set rule database for judging whether the data is abnormal. The turbine status monitoring result refers to the result of classifying the current operating status of the turbine according to the monitoring data.

[0066] During the specific implementation, a steam turbine shaft system safety assessment and risk monitoring system was established, a shaft system stability assessment system was formulated, and the cylinder, rotor, and bearings were monitored and tracked. Furthermore, based on the analysis of failure mechanisms such as main shaft fracture, main shaft bending, and bearing damage, relevant judgment rules were formulated and a rule base was generated to achieve health status monitoring of the steam turbine.

[0067] In a feasible implementation, step S10 may include steps A11 to A12:

[0068] Step A11, performing feature recognition on key indicator parameters according to a preset identification algorithm to obtain state feature parameters;

[0069] It should be noted that the state characteristic parameters refer to characteristic parameters related to the device state.

[0070] In the specific implementation, the key safety indicator parameters of the steam turbine are characterized by extraction through a preset intelligent identification algorithm, and then the characteristic parameters related to the equipment status are obtained.

[0071] Step A12, determining the turbine state monitoring result according to the state characteristic parameters and the abnormality judgment rule base.

[0072] It is understandable that the weak signs of changes in the extracted equipment status characteristics are identified in advance and effectively, and according to the established abnormal judgment rule base, equipment status monitoring based on massive data is realized, and the contribution of equipment failure-related parameters is further studied to provide support for the early detection of equipment failures, that is, to determine the turbine status monitoring results.

[0073] In a feasible implementation manner, step A12 may include steps B121 to B123:

[0074] Step B121, determining the parameter growth rate corresponding to each parameter according to the key indicator parameter;

[0075] It can be understood that the parameter growth rate refers to the speed at which the monitoring parameter increases per unit time.

[0076] In a specific implementation, the detected key safety indicator parameters of the steam turbine are calculated to obtain the speed at which each parameter increases in unit time, that is, the parameter growth rate.

[0077] Step B122, comparing the parameter growth rate corresponding to each parameter with the growth rate threshold to obtain a growth rate comparison result;

[0078] It can be understood that the growth rate threshold refers to the growth rate critical value used to determine whether there is a potential fault in the steam turbine, and the growth rate comparison result refers to the comparison result between the parameter growth rate corresponding to each parameter and the growth rate threshold.

[0079] In the specific implementation, the speed of increase of the monitoring parameters corresponding to each parameter in unit time is compared with the critical value of the growth rate used to determine whether there is a potential fault in the turbine, and then the comparison result of the parameter growth rate corresponding to each parameter and the growth rate threshold is obtained, that is, the growth rate comparison result.

[0080] Step B123, generating turbine trend warning information according to the increase rate comparison result.

[0081] In specific implementation, turbine trend warning information refers to identifying the trend of parameter changes through long-term monitoring and data analysis of turbine operating parameters, and predicting possible problems in the future on this basis, thereby issuing warning information in advance.

[0082] It should be noted that the trend of monitoring parameters that affect the safe and stable operation of steam turbines, such as tile temperature and vibration, is tracked and analyzed. Multi-source heterogeneous data is integrated to establish a mathematical model. The relationship between feature data and related parameters is analyzed through feature extraction and correlation analysis methods. Trend warning information is pushed in combination with expert experience. At the same time, the massive historical data and newly added real-time status data are fully utilized, and big data analysis and artificial intelligence technology are applied to complete the iterative update of the algorithm knowledge base to achieve trend warning of the health status of steam turbines.

[0083] It should be noted that this embodiment analyzes potential equipment failures and their root causes based on the selected key safety indicators, tracks equipment operation trends, and evaluates alarm information to support correct inspection and maintenance decisions. Figure 2 As shown in the work flow diagram Figure 3 In this embodiment, the key indicators of the healthy state of the steam turbine mainly include: vibration parameters and other process parameters. The specific indicators are shown in Table 1.

[0084] Table 1:

[0085]

[0086] It can be understood that in this embodiment, data information management based on multi-source heterogeneous data acquisition technology is to connect the monitoring data related to the safety and reliability of the steam turbine to the monitoring and early warning system through a one-way isolation device to realize the collection and storage of monitoring data during the start-up and shutdown and operation stages of the unit. The required parameter indicators are introduced from systems such as TSI and will not cause any interference to the original system. Advanced algorithms are used in the collection stage to ensure the time synchronization of the collected data while preventing data loss during network transmission. Data information management can realize the storage of data such as speed increase / deceleration data, historical data, event data, etc., and can provide a variety of data storage methods, such as: equal time interval sampling storage, equal speed interval sampling storage, alarm storage and manual storage. It can be displayed according to different operating states of the steam turbine and user needs. The details are as follows: (a) Equipment basic information management: Keep consistent with the KKS coding of the equipment in the factory, establish an equipment basic information management system, and mainly manage the extracted key parameters in a systematic and hierarchical manner. (b) Data information processing: Data processing includes data cleaning and data analysis. Data cleaning mainly removes data wild values ​​caused by sensor failure or human factors. Data analysis is the use of advanced algorithms and models to extract useful information from raw data. (c) Feature symptom management: Display status monitoring data, and implement the functions of classifying and querying symptom data based on relevant retrieval information. At the same time, feature data can be transformed to provide data support for the early warning module. (d) Abnormal feature management: Collect the time series of abnormal signs, perform pattern analysis on abnormal data, determine the range of abnormal occurrence, and realize the recording and processing of abnormal data.

[0087] It should be noted that the state monitoring and trend warning based on mechanism + mathematics in this embodiment include: (a) state monitoring. State monitoring is mainly achieved by establishing a state monitoring indicator system to realize real-time monitoring of the health status. By tracking the changes in parameters such as shaft vibration, bearing vibration, and bearing temperature, multi-parameter fusion identification and monitoring of the steam turbine is realized. Research on abnormal state identification is carried out, a state monitoring model that integrates multiple parameters is constructed, and an intelligent discrimination algorithm is used to accurately identify the working conditions, extract feature markers from the data, and identify the weak signs of changes in the extracted equipment state characteristics in advance and effectively. This function can also be configured online, and can complete the online real-time monitoring and management of the key parameters of the unit. It also has easy-to-expand functions, and can select the display mode of the data (such as data trend charts, etc.), and can query the spectrum, axis trajectory, axis position, Bode diagram, polar coordinate diagram, etc. According to the established abnormal judgment rule base, equipment status monitoring based on massive data is realized, and the contribution of equipment fault-related parameters is further studied to provide support for early detection of equipment faults; (b) Trend warning: On the one hand, the setting method of alarm thresholds such as the growth rate is adopted to achieve multi-dimensional warning of steam turbines on the basis of meeting relevant national standards and plant-side operation regulations, breaking through the limitations of traditional fixed value alarms. On the other hand, the operating parameters are monitored and analyzed, and through the performance of the degradation process of key components in the operating data, combined with the laws of the degradation development of key components such as bearings, a health monitoring and early warning model integrating multiple parameters is constructed to make fault early warning judgments. Furthermore, the historical data and real-time monitoring data of the unit are fully utilized, and the big data algorithm is used to construct a dynamic health status trend chart to detect potential faults of the unit early, creating favorable conditions and response time to avoid major accidents such as non-stop, overspeed, large shaft fracture, large shaft bending, and bearing damage.

[0088] Step S20, determining a gearbox state monitoring result according to the gearbox vibration image and the heterogeneous computing optimization method;

[0089] It can be understood that the gearbox vibration image refers to the vibration image of the auxiliary gearbox of the gas turbine, the heterogeneous computing optimization method refers to the heterogeneous computing optimization technology that combines CPU (Central Processing Unit) + GPU (Graphics Processing Unit), and the gearbox state monitoring result refers to the result of classifying the current operating state of the gearbox according to the vibration image.

[0090] In the specific implementation, the vibration image of the target object, that is, the vibration image of the auxiliary gearbox of the gas turbine is captured by a camera, and then the vibration image of the auxiliary gearbox of the gas turbine is processed and calculated based on the heterogeneous computing optimization technology combining CPU+GPU, and finally the result of classifying the current operating status of the gearbox according to the vibration image is determined, that is, the gearbox status monitoring result.

[0091] In a feasible implementation, step S20 may include steps A21 to A24:

[0092] Step A21, preprocessing the gear box vibration image to obtain a preprocessed vibration image;

[0093] It can be understood that the pre-processed vibration image refers to the pre-processed vibration image of the auxiliary gearbox of the combustion engine.

[0094] In the specific implementation, the collected vibration image is processed to improve the image quality and accuracy, including removing the noise that may exist in the image, eliminating the background interference, enhancing the image, highlighting the characteristics of the vibrating target object, and finally obtaining the pre-processed vibration image.

[0095] Step A22, determining vibration characteristic information according to the pre-processed vibration image and a preset image processing algorithm;

[0096] It can be understood that the preset image processing algorithm refers to a pre-set image processing algorithm, and the vibration feature information refers to key feature information of vibration.

[0097] In the specific implementation, the monitoring area features in the image are identified based on the image processing algorithm, so as to accurately extract the key information of the vibration. The efficient image processing algorithms, such as edge detection and contour extraction, are used to ensure that the extracted regional features have high precision and accuracy, that is, the vibration feature information is obtained.

[0098] Step A23, performing spectrum analysis on the vibration characteristic information to obtain frequency spectrum information and key characteristic information;

[0099] It can be understood that frequency spectrum information refers to the amplitude distribution of the vibration signal at different frequencies obtained through spectrum analysis, and key feature information refers to richer and more comprehensive vibration feature information, such as the frequency and amplitude of vibration.

[0100] In the specific implementation, the spectrum analysis technology is used to analyze the vibration signal in the frequency domain to extract the frequency spectrum information of the vibration. The relevant algorithms are used to extract the vibration characteristics, such as the main frequency and amplitude, to provide a basis for subsequent vibration analysis and diagnosis. The efficient spectrum analysis algorithms, such as fast Fourier transform and wavelet transform, are used to ensure the accuracy and reliability of vibration feature extraction.

[0101] In a feasible implementation manner, step A23 may include steps B231 to B233:

[0102] Step B231, obtaining target coordinate data according to the vibration characteristic information, the three-dimensional reconstruction algorithm and the device coordinate system;

[0103] It can be understood that the three-dimensional reconstruction algorithm includes stereo vision matching, triangulation, etc. The equipment coordinate system refers to the coordinate system where the auxiliary gearbox of the combustion turbine is located, and the target coordinate data refers to the vibration data after coordinate conversion.

[0104] In the specific implementation, based on the monitoring area information in the image, the two-dimensional image is converted into a three-dimensional space model through a three-dimensional reconstruction algorithm. Accurate three-dimensional reconstruction algorithms, such as stereo vision matching and triangulation, are used to accurately restore the spatial posture of the monitoring area. The coordinate conversion method is used to convert the three-dimensional coordinates of the monitoring area into coordinates consistent with the device coordinate system to ensure that the vibration monitoring data accurately corresponds to the device position and direction, that is, to obtain the target coordinate data.

[0105] Step B232, obtaining vibration monitoring data according to the target coordinate data, the signal processing algorithm and the displacement correction algorithm;

[0106] It can be understood that the signal processing algorithm refers to an algorithm for suppressing noise on a vibration image, the displacement correction algorithm refers to an algorithm for correcting displacement errors in an image, and the vibration monitoring data refers to the vibration monitoring data after noise suppression and displacement correction.

[0107] In the specific implementation, the noise interference and displacement error that may exist in the captured image are processed. The vibration image is subjected to noise suppression based on the signal processing algorithm, such as median filtering, wavelet denoising, etc., to reduce the influence of noise on vibration feature extraction. The displacement correction algorithm is used to correct the displacement error in the image to ensure the accuracy and stability of the vibration monitoring data and obtain the vibration monitoring data.

[0108] Step B233, performing spectrum analysis on the vibration monitoring data to obtain frequency spectrum information and key feature information.

[0109] It is understandable that the frequency domain analysis of vibration signals is performed using spectrum analysis technology to extract the frequency spectrum information of vibration. The relevant algorithms are used to extract vibration features, such as main frequency, amplitude, etc., to provide a basis for subsequent vibration analysis and diagnosis. Efficient spectrum analysis algorithms, such as fast Fourier transform and wavelet transform, are used to ensure the accuracy and reliability of vibration feature extraction.

[0110] Step A24, determining the gearbox state monitoring result according to the frequency spectrum information, the key feature information and the heterogeneous computing optimization method.

[0111] It is understandable that the vibration data is efficiently processed and analyzed by combining heterogeneous computing optimization technology of CPU+GPU. Data transmission and processing strategies are adopted to ensure that the system can operate stably in real-time monitoring and respond to changes in the vibration state of the equipment in a timely manner. Efficient parallel computing technology, data flow optimization technology, etc. are adopted to ensure that the system has high real-time performance and response speed, and finally determine the gearbox condition monitoring results.

[0112] It should be noted that in this embodiment, vibration sensors are installed at appropriate locations to realize vibration monitoring of local key parts of the gearbox in response to the local monitoring needs of the input and output ends of the gearbox. In response to the monitoring needs of the overall intuitive large field of view area of ​​the gearbox, the technical route is image processing and feature extraction, three-dimensional reconstruction and coordinate transformation, noise suppression and displacement correction, spectrum analysis and vibration feature extraction, heterogeneous computing optimization and real-time guarantee. Based on the above technical route, high-precision real-time vibration monitoring of the gearbox with a large field of view is realized. Specifically: 1) Image processing and feature extraction, the collected vibration image is processed to improve the image quality and accuracy. Including removing the noise that may exist in the image, eliminating background interference, enhancing the image, and highlighting the characteristics of the vibrating target object. Based on the image processing algorithm, the monitoring area features in the image are identified, so as to accurately extract the key information of the vibration. Adopt efficient image processing algorithms, such as edge detection, contour extraction, etc., to ensure that the extracted regional features have high precision and accuracy; 2) Three-dimensional reconstruction and coordinate transformation, based on the monitoring area information in the image, the two-dimensional image is converted into a three-dimensional space model through a three-dimensional reconstruction algorithm. Adopt precise 3D reconstruction algorithms, such as stereo vision matching and triangulation, to accurately restore the spatial posture of the monitoring area. Adopt coordinate transformation method to convert the 3D coordinates of the monitoring area into coordinates consistent with the equipment coordinate system to ensure the accurate correspondence between vibration monitoring data and equipment position and direction; 3) Noise suppression and displacement correction, to process the noise interference and displacement errors that may exist in the captured image. Based on signal processing algorithms, the vibration image is subjected to noise suppression, such as median filtering and wavelet denoising, to reduce the influence of noise on vibration feature extraction. The displacement correction algorithm is used to correct the displacement error in the image to ensure the accuracy and stability of vibration monitoring data; 4) Spectral analysis and vibration feature extraction, the spectrum analysis technology is used to analyze the vibration signal in the frequency domain to extract the frequency spectrum information of the vibration. The relevant algorithms are used to extract vibration features, such as main frequency and amplitude, to provide a basis for subsequent vibration analysis and diagnosis. Adopt efficient spectrum analysis algorithms, such as fast Fourier transform and wavelet transform, to ensure the accuracy and reliability of vibration feature extraction; 5) Heterogeneous computing optimization and real-time guarantee, combined with CPU+GPU heterogeneous computing optimization technology, to efficiently process and analyze vibration data. Data transmission and processing strategies are adopted to ensure that the system can operate stably in real-time monitoring and respond to changes in the vibration state of the equipment in a timely manner. Efficient parallel computing technology and data flow optimization technology are adopted to ensure that the system has high real-time performance and response speed.

[0113] Step S30, determining the state monitoring result of the rotating auxiliary machine according to the rotating auxiliary machine vibration data and the fault assessment database;

[0114] It can be understood that the rotating auxiliary machine vibration data refers to the vibration monitoring data of the key rotating auxiliary machine, and the rotating auxiliary machine state monitoring result refers to the result of classifying the current operating state of the rotating auxiliary machine according to the vibration monitoring.

[0115] In the specific implementation, multi-source monitoring data acquisition technology is adopted, standardized vibration measurement point layout and multi-source data collection plan are formulated, and a key transfer equipment health status monitoring system integrating online data such as vibration and other process parameters is constructed to provide a data basis for key rotating auxiliary equipment health status monitoring and early warning. The intelligent characterization technology of equipment failure is adopted to realize equipment failure tracking and early warning that integrates vibration, process parameters and other data, and to build an online early warning and status assessment system for equipment failure. Based on the plant-side equipment operation and maintenance mode, the equipment early warning assessment conclusions and inspection and maintenance strategies are fully integrated to build a digital equipment inspection and maintenance process, complete the equipment management closed loop, and finally realize the key rotating auxiliary equipment health status monitoring and early warning and maintenance decision tracking optimization.

[0116] Step S40, obtaining the status monitoring result of the key transfer equipment of the power station according to the status monitoring result of the steam turbine, the status monitoring result of the gear box and the status monitoring result of the rotating auxiliary machine.

[0117] In the specific implementation, the results of classifying the current operating status of the turbine according to the monitoring data, the results of classifying the current operating status of the gearbox according to the vibration image, and the results of classifying the current operating status of the rotating auxiliary equipment according to vibration monitoring will be analyzed and summarized to obtain the status monitoring results of the key transfer equipment of the power plant.

[0118] This embodiment determines the turbine status monitoring result according to key indicator parameters, preset identification algorithms, and abnormal judgment rule bases; determines the gearbox status monitoring result according to the gearbox vibration image and heterogeneous computing optimization method; determines the rotating auxiliary machine status monitoring result according to the rotating auxiliary machine vibration data and fault assessment database; and obtains the status monitoring result of the key transfer equipment of the power station according to the turbine status monitoring result, the gearbox status monitoring result, and the rotating auxiliary machine status monitoring result. By monitoring the health status of the steam turbine, the auxiliary gearbox of the gas engine, and the key rotating auxiliary machine, the safety, reliability, and adaptability of the key transfer equipment of the unit are improved, and online monitoring and early warning of the key transfer equipment of the power station are realized.

[0119] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 4 In the method for monitoring the status of key transfer equipment of a power station, step S30 further includes steps S31 to S33:

[0120] Step S31, performing fault feature recognition on the vibration data of the rotating auxiliary machine to obtain vibration fault features;

[0121] It can be understood that the vibration fault characteristics refer to the vibration fault characteristics of the rotating auxiliary machine. By identifying the fault characteristics in the vibration monitoring data of the key rotating auxiliary machine, the vibration fault characteristics of the rotating auxiliary machine, that is, the vibration fault characteristics, are obtained.

[0122] Step S32, determining a fault assessment result according to the vibration fault characteristics and a fault assessment database;

[0123] It can be understood that the fault assessment database refers to a knowledge base of fault assessment rules based on the study of vibration fault mechanisms, and the fault assessment result refers to the assessment result of whether the rotating auxiliary machine has a fault.

[0124] In a specific implementation, a fault assessment rule knowledge base based on the study of vibration fault mechanism is combined with the vibration fault characteristics of the rotating auxiliary machine to perform fault assessment and obtain an assessment result of whether the rotating auxiliary machine has a fault, that is, a fault assessment result.

[0125] Step S33, determining the rotating auxiliary machine state monitoring result according to the fault assessment result.

[0126] It can be understood that the corresponding rotating auxiliary machine status monitoring result is determined by the evaluation result of whether the rotating auxiliary machine has a fault, so as to achieve effective trend warning and status evaluation of equipment failure.

[0127] In a feasible implementation, step S33 may include steps A331 to A333:

[0128] Step A331, establishing a rotating auxiliary machine monitoring model according to historical state parameters and rotating auxiliary machine failure mechanism;

[0129] It can be understood that the historical state parameters refer to the state-related parameters of the rotating auxiliary machine history, the rotating auxiliary machine failure mechanism refers to the various physical and chemical processes that cause the rotating auxiliary machine failure, and the rotating auxiliary machine monitoring model refers to the equipment health monitoring model of the rotating auxiliary machine.

[0130] In the specific implementation, the process parameters related to the status of rotating auxiliary equipment are included in the monitoring system. Based on the failure mechanism analysis, an equipment health monitoring model is constructed through the analysis of plant-side failure cases and expert experience to achieve effective trend warning and status assessment of equipment failures.

[0131] Step A332, obtaining a fault warning result according to the rotating auxiliary machine monitoring model and the rotating auxiliary machine vibration data;

[0132] It can be understood that the fault warning result refers to the fault prediction and alarm result of the rotating auxiliary machine.

[0133] In the specific implementation, the equipment health monitoring model of the rotating auxiliary machine is combined with the vibration data of the rotating auxiliary machine for prediction, and the fault prediction and alarm results of the rotating auxiliary machine, that is, the fault warning results, are determined according to the prediction results of the model.

[0134] Step A333, generating rotating auxiliary machine fault warning information according to the fault warning result.

[0135] In specific implementation, the rotating auxiliary machine fault warning information refers to the early warning information issued by monitoring and analyzing the operating parameters of the rotating auxiliary machine, identifying potential fault signs.

[0136] It should be noted that the online monitoring system in this embodiment is constructed based on the redundancy of the rotating auxiliary equipment of Wuchang Company, the difficulty of fault handling and the severity of the consequences of the faults, combined with the historical data of plant-side faults, to select key rotating auxiliary equipment (temporarily condensing pumps, circulating pumps, and feeding pumps), and conduct a survey of the existing vibration measuring points of the equipment. According to the needs of vibration analysis and diagnosis, the measuring points that do not meet the spectrum analysis are replaced, and the measuring points are supplemented for the key parts that are missing from vibration monitoring. A one-type-one-policy vibration measuring point installation and layout plan is formulated for different types of equipment to ensure comprehensive monitoring of the vibration status of the equipment. Specifically: (a) Installation of vibration sensors. The vibration sensors should be installed on the load-bearing side of the bearing. Rolling bearings should be installed as follows: Figure 5 Install the sliding bearing as shown. Figure 6 Install in the position shown. Try to avoid installing in hollow parts such as bearing covers, and also try to avoid installing on components with weak supporting stiffness, such as fan covers, insulation skins, fins, fin plates, etc. The structural selection of the installation position should be as close to the required bearing position as possible. The transmission path of the vibration signal should be as short as possible. When installing radially, ensure that the extension line of the sensor's test axis passes through the center line of the machine shaft. The ratio of the total mass of the sensor and its installation accessories to the mass of the structure being measured should be as small as possible, and the ratio should not exceed 1 / 10; (b) Measurement point layout plan, select key rotating auxiliary machines, and according to the structural characteristics of the equipment, install vibration sensors that can provide monitoring information such as vibration spectrum and waveform at key points where the equipment is supported. According to the needs of analysis and processing, selectively install key phase sensors. Taking the condensing pump as an example, the measurement point layout plan is as follows Figure 7 shown.

[0137] It should be noted that the construction of the trend warning and status assessment system in this embodiment is specifically: (a) based on vibration online equipment trend warning and status assessment, according to relevant national standards, according to different equipment types, different vibration amplitude warning schemes are formulated to achieve static warning of equipment vibration amplitude, and on this basis, warning research such as equipment vibration amplitude growth rate is carried out to achieve dynamic warning of equipment vibration amplitude. At the same time, a fault assessment rule knowledge base based on vibration fault mechanism research is established, and vibration fault feature extraction, identification and push are carried out for the collected vibration data; (b) based on process parameter online equipment trend warning and status assessment, the process parameters related to the status of rotating auxiliary equipment are included in the monitoring system, based on the fault mechanism analysis, through the analysis of factory-side fault cases combined with expert experience to build an equipment health monitoring model, to achieve effective trend warning and status assessment of equipment faults.

[0138] It can be understood that the construction of the maintenance decision optimization system in this embodiment establishes a status assessment module with a single device as the main body, realizes edge-side deployment, and uses equipment category as a distinguishing feature to integrate monitoring data collection, trend warning, and status assessment into an independent operation module. The module takes into account both on-site monitoring, evaluation, display, and remote data transmission. As needed, the local system can add a display or open a portable digital display device data interface for on-site viewing by inspection personnel. A remote data transmission interface can be opened to centrally display the intelligent warning evaluation conclusions on the remote platform. Figure 8 As shown in the figure, a system for classifying the degree of equipment failure is constructed. Based on the conclusions of the equipment trend warning and status assessment system, the equipment component-level failure information is integrated to push the equipment failure degree and inspection and maintenance suggestions. Taking the current equipment inspection and maintenance management process offline at the factory as the design prototype, an online intelligent tracking and management system for equipment inspection and maintenance is constructed to track the inspection and maintenance work in real time, and finally realize a double closed loop at the technical and management levels.

[0139] This embodiment obtains vibration fault characteristics by performing fault characteristic identification on the vibration data of the rotating auxiliary machine; determines the fault assessment result according to the vibration fault characteristic and the fault assessment database; determines the rotating auxiliary machine status monitoring result according to the fault assessment result, and constructs a digital equipment inspection and maintenance process based on the plant-side equipment operation and maintenance mode, fully integrates the equipment early warning assessment conclusion and the inspection and maintenance strategy, completes the equipment management closed loop, and finally realizes the health status monitoring and early warning of key rotating auxiliary machines and the tracking and optimization of maintenance decisions.

[0140] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the condition monitoring method of key transfer equipment of the power station of the present application. More simple transformations based on this technical concept are all within the protection scope of the present application.

[0141] This application also provides a state monitoring device for key power station equipment, please refer to Fig. 9 The state monitoring device of the key transfer equipment of the power station comprises:

[0142] Monitoring module 10, used to determine the monitoring result of the steam turbine state according to key indicator parameters, preset identification algorithm and abnormal judgment rule base;

[0143] The monitoring module 10 is also used to determine the gearbox state monitoring result according to the gearbox vibration image and the heterogeneous computing optimization method;

[0144] The monitoring module 10 is also used to determine the monitoring result of the rotating auxiliary machine state according to the rotating auxiliary machine vibration data and the fault assessment database;

[0145] The processing module 20 is used to obtain the status monitoring result of the key transfer equipment of the power station according to the status monitoring result of the steam turbine, the status monitoring result of the gear box and the status monitoring result of the rotating auxiliary machine.

[0146] Optionally, the monitoring module 10 is further used for:

[0147] Perform feature recognition on key indicator parameters according to a preset identification algorithm to obtain state feature parameters;

[0148] The turbine state monitoring result is determined according to the state characteristic parameters and the abnormality judgment rule base.

[0149] Optionally, the monitoring module 10 is further used for:

[0150] Determine the parameter growth rate corresponding to each parameter according to the key indicator parameters;

[0151] Compare the parameter growth rate corresponding to each parameter with the growth rate threshold to obtain a growth rate comparison result;

[0152] The turbine trend warning information is generated according to the increase rate comparison result.

[0153] Optionally, the monitoring module 10 is further used for:

[0154] Preprocessing the gearbox vibration image to obtain a preprocessed vibration image;

[0155] Determining vibration characteristic information according to the preprocessed vibration image and a preset image processing algorithm;

[0156] Performing spectrum analysis on the vibration characteristic information to obtain frequency spectrum information and key characteristic information;

[0157] The gearbox condition monitoring result is determined according to the frequency spectrum information, the key feature information and the heterogeneous computing optimization method.

[0158] Optionally, the monitoring module 10 is further used for:

[0159] Obtaining target coordinate data according to the vibration characteristic information, the three-dimensional reconstruction algorithm and the device coordinate system;

[0160] Obtain vibration monitoring data according to the target coordinate data, the signal processing algorithm and the displacement correction algorithm;

[0161] Performing spectrum analysis on the vibration monitoring data to obtain frequency spectrum information and key feature information.

[0162] Optionally, the monitoring module 10 is further used for:

[0163] Perform fault feature recognition on the vibration data of the rotating auxiliary machine to obtain vibration fault features;

[0164] Determine a fault assessment result according to the vibration fault characteristics and a fault assessment database;

[0165] The rotating auxiliary machine state monitoring result is determined according to the fault assessment result.

[0166] Optionally, the monitoring module 10 is further used for:

[0167] Establish a rotating auxiliary machine monitoring model based on historical state parameters and rotating auxiliary machine failure mechanisms;

[0168] Obtaining a fault warning result according to the rotating auxiliary machine monitoring model and the rotating auxiliary machine vibration data;

[0169] The rotating auxiliary machine fault warning information is generated according to the fault warning result.

[0170] The state monitoring device for key transfer equipment of a power station provided by the present application adopts the state monitoring method for key transfer equipment of a power station in the above-mentioned embodiment, and can solve the technical problem of online monitoring and early warning of key transfer equipment of a power station. Compared with the prior art, the beneficial effects of the state monitoring device for key transfer equipment of a power station provided by the present application are the same as the beneficial effects of the state monitoring method for key transfer equipment of a power station provided by the above-mentioned embodiment, and the other technical features in the state monitoring device for key transfer equipment of a power station are the same as the features disclosed in the above-mentioned embodiment method, and will not be described in detail here.

[0171] The present application provides a status monitoring device for key transition equipment of a power plant, and the status monitoring device for key transition equipment of a power plant includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the status monitoring method for key transition equipment of a power plant in the above-mentioned embodiment one.

[0172] Reference below Fig.10 , which shows a schematic diagram of the structure of a state monitoring device for a key power station transition device suitable for implementing the embodiment of the present application. The state monitoring device for the key power station transition device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Fig.10 The status monitoring device for key power station equipment shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0173] like Fig.10 As shown, the state monitoring device of the key transfer equipment of the power station may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 to the random access memory (RAM: Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of the state monitoring device of the key transfer equipment of the power station are also stored. The processing device 1001, the ROM 1002 and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the state monitoring device of the key transfer equipment of the power station to communicate wirelessly or wired with other devices to exchange data. Although the state monitoring device of the key transfer equipment of the power station with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0174] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0175] The state monitoring device for key transfer equipment of a power station provided by the present application adopts the state monitoring method for key transfer equipment of a power station in the above-mentioned embodiment, and can solve the technical problem of online monitoring and early warning of key transfer equipment of a power station. Compared with the prior art, the beneficial effects of the state monitoring device for key transfer equipment of a power station provided by the present application are the same as the beneficial effects of the state monitoring method for key transfer equipment of a power station provided by the above-mentioned embodiment, and the other technical features in the state monitoring device for key transfer equipment of a power station are the same as the features disclosed in the method of the previous embodiment, and will not be described in detail here.

[0176] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0177] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0178] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the method for monitoring the status of key transfer equipment of a power station in the above-mentioned embodiment.

[0179] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0180] The computer-readable storage medium may be included in the status monitoring device of the key transfer equipment of the power station; or may exist independently without being assembled into the status monitoring device of the key transfer equipment of the power station.

[0181] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the status monitoring equipment of the key transfer equipment of the power plant, the status monitoring equipment of the key transfer equipment of the power plant: determines the turbine status monitoring result according to the key indicator parameters, the preset identification algorithm and the abnormal judgment rule library; determines the gearbox status monitoring result according to the gearbox vibration image and the heterogeneous computing optimization method; determines the rotating auxiliary equipment status monitoring result according to the rotating auxiliary equipment vibration data and the fault assessment database; obtains the status monitoring result of the key transfer equipment of the power plant according to the steam turbine status monitoring result, the gearbox status monitoring result and the rotating auxiliary equipment status monitoring result.

[0182] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0183] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0184] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0185] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned state monitoring method for key power station transfer equipment, and can solve the technical problem of online monitoring and early warning of key power station transfer equipment. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the state monitoring method for key power station transfer equipment provided by the above-mentioned embodiment, and will not be repeated here.

[0186] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned method for monitoring the status of key transfer equipment of a power station when executed by a processor.

[0187] The computer program product provided by this application can solve the technical problem of online monitoring and early warning of key power station transfer equipment. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the state monitoring method of key power station transfer equipment provided by the above embodiment, which will not be repeated here.

[0188] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for monitoring the status of key transfer equipment in a power station, characterized in that: The state monitoring method of the key transfer equipment of the power station comprises: Determine the turbine status monitoring results based on key indicator parameters, preset identification algorithms and abnormal judgment rule base; Determine the gearbox condition monitoring results based on the gearbox vibration image and heterogeneous computing optimization method; Determine the rotating auxiliary machine condition monitoring results based on the rotating auxiliary machine vibration data and fault assessment database; The status monitoring results of the key transfer equipment of the power station are obtained according to the status monitoring results of the steam turbine, the status monitoring results of the gear box and the status monitoring results of the rotating auxiliary machine.

2. The method according to claim 1, characterized in that The step of determining the turbine status monitoring result according to the key indicator parameters, the preset identification algorithm and the abnormality judgment rule base includes: Perform feature recognition on key indicator parameters according to a preset identification algorithm to obtain state feature parameters; The turbine state monitoring result is determined according to the state characteristic parameters and the abnormality judgment rule base.

3. The method according to claim 2, characterized in that After the step of determining the turbine state monitoring result according to the state characteristic parameters and the abnormality judgment rule base, the method further includes: Determine the parameter growth rate corresponding to each parameter according to the key indicator parameters; Compare the parameter growth rate corresponding to each parameter with the growth rate threshold to obtain a growth rate comparison result; The turbine trend warning information is generated according to the increase rate comparison result.

4. The method according to claim 1, characterized in that The step of determining the gearbox condition monitoring result according to the gearbox vibration image and the heterogeneous computing optimization method includes: Preprocessing the gearbox vibration image to obtain a preprocessed vibration image; Determining vibration characteristic information according to the preprocessed vibration image and a preset image processing algorithm; Performing spectrum analysis on the vibration characteristic information to obtain frequency spectrum information and key characteristic information; The gearbox condition monitoring result is determined according to the frequency spectrum information, the key feature information and the heterogeneous computing optimization method.

5. The method according to claim 4, characterized in that The step of performing spectrum analysis on the vibration characteristic information to obtain frequency spectrum information and key characteristic information comprises: Obtaining target coordinate data according to the vibration characteristic information, the three-dimensional reconstruction algorithm and the device coordinate system; Obtain vibration monitoring data according to the target coordinate data, the signal processing algorithm and the displacement correction algorithm; Performing spectrum analysis on the vibration monitoring data to obtain frequency spectrum information and key feature information.

6. The method according to claim 1, characterized in that The step of determining the rotating auxiliary machine state monitoring result according to the rotating auxiliary machine vibration data and the fault assessment database comprises: Perform fault feature recognition on the vibration data of the rotating auxiliary machine to obtain vibration fault features; Determine a fault assessment result according to the vibration fault characteristics and a fault assessment database; The rotating auxiliary machine state monitoring result is determined according to the fault assessment result.

7. The method according to claim 6, characterized in that After the step of determining the rotating auxiliary machine state monitoring result according to the fault assessment result, the method further includes: Establish a rotating auxiliary machine monitoring model based on historical state parameters and rotating auxiliary machine failure mechanisms; Obtaining a fault warning result according to the rotating auxiliary machine monitoring model and the rotating auxiliary machine vibration data; The rotating auxiliary machine fault warning information is generated according to the fault warning result.

8. A state monitoring device for key transfer equipment of a power station, characterized in that: The device comprises: Monitoring module, used to determine the monitoring results of the steam turbine status according to key indicator parameters, preset identification algorithms and abnormal judgment rule base; The monitoring module is further used to determine the gearbox state monitoring result according to the gearbox vibration image and the heterogeneous computing optimization method; The monitoring module is further used to determine the monitoring result of the state of the rotating auxiliary machine according to the vibration data of the rotating auxiliary machine and the fault assessment database; The processing module is used to obtain the status monitoring result of the key transfer equipment of the power station according to the status monitoring result of the steam turbine, the status monitoring result of the gear box and the status monitoring result of the rotating auxiliary machine.

9. A condition monitoring device for key transfer equipment of a power station, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for status monitoring of key power station transfer equipment according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the method for status monitoring of key transfer equipment of a power station according to any one of claims 1 to 7 are implemented.

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