Intelligent monitoring system for running state of water turbine

Abstract

The invention discloses an intelligent monitoring system for the running state of a water turbine. The intelligent monitoring system comprises a data acquisition module, a real-time data pushing module, a data storage module, a state evaluation module and a man-machine interaction module. The data acquisition module is used for acquiring vibration, throw, pressure pulsation and key phase information by using various sensors; the real-time data pushing module is used for distinguishing data types and pushing the data through WebSocket and HTTP protocols; the data storage module adopts a Redis and MySQL double-layer framework; the state evaluation module constructs an evaluation index system, determines a weight by using an entropy weight method and an improved analytic hierarchy process, and determines an operation state through deterioration degree analysis and fuzzy comprehensive evaluation; the man-machine interaction module displays monitoring data and evaluation results, provides a real-time monitoring, data query and state evaluation interface, has the advantages of being fast in data updating, convenient to query, visual in display and the like, can comprehensively monitor the operation state of the water turbine, and provides powerful guarantee for safe and efficient operation of a hydropower station.

Description

Technical Field

[0001] The invention relates to the field of intelligent monitoring of the running state of a water turbine, and in particular to an intelligent monitoring system for the running state of a water turbine. Background Art

[0002] As a key equipment of a hydropower station, the operation status of the turbine directly affects the power generation efficiency, safety and stability of the hydropower station. During the operation of the turbine, various faults and abnormal conditions may occur due to factors such as the complexity of the water flow, the wear of the mechanical structure and the changes in the operating environment. For example, the vibration of the turbine may cause loose components, fatigue damage, and even cause the unit to shut down; pressure pulsation may cause unstable operation of the turbine, reduce power generation efficiency, and in severe cases may damage the flow components.

[0003] Traditional turbine monitoring methods mainly rely on manual inspections and regular maintenance, which has many limitations. Manual inspections are difficult to fully and comprehensively understand the operating status of turbines, and are prone to missed inspections and misjudgments. Although regular maintenance can detect problems to a certain extent, the maintenance cycle is long and cannot respond to sudden failures in a timely manner. Moreover, traditional monitoring methods lack the ability to effectively analyze and process large amounts of monitoring data, making it difficult to accurately assess the operating status and health of turbines.

[0004] With the rapid development of computer technology, sensor technology and communication technology, the application of intelligent monitoring system in the field of water turbines has become an inevitable trend. Intelligent monitoring system can collect various parameters of water turbine operation in real time, and through data analysis and processing technology, it can accurately evaluate the operation status of water turbines and predict faults, providing strong guarantee for the safe, stable and efficient operation of hydropower stations. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent monitoring system for the operation status of a water turbine to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: an intelligent monitoring system for the operation status of a hydraulic turbine, comprising:

[0007] Data acquisition module

[0008] Used to collect data related to the operation status of the turbine. The data acquisition module includes multiple sensors, each of which is used to collect different types of signals, as follows:

[0009] DP type seismic low-frequency vibration sensor, used to collect vibration signals of stationary parts of turbines, including vibrations of top cover, upper frame, lower frame and stator base;

[0010] The eddy current displacement sensor is used to collect the swing signal of the rotating parts of the turbine. It is installed at the three guide bearings. Two detection points are set at each guide bearing, marked as the X axis and the Y axis respectively, to collect the radial vibration of the main shaft and the bearing or other fixing devices during the rotation process;

[0011] At least one of a piezoresistive pressure sensor, a piezoelectric pressure sensor and a capacitive pressure sensor, used to collect pressure pulsation signals of flow-through components of the turbine, including pressure pulsations at the volute inlet, the bladeless area, under the top cover, between the runner and the discharge ring, the tailwater cone and the tailwater elbow;

[0012] The key phase sensor is used to obtain information related to the key phase of the turbine rotor. By marking the raised key position or groove on the rotating part, the sensor receives a pulse signal when it rotates to the key phase mark position to monitor the unit speed, the amplitude, frequency and phase angle of the rotor radial vibration;

[0013] Real-time data push module

[0014] Used to push the collected data to the monitoring platform in real time. This module divides the data into real-time data and ordinary data streams. Real-time data is pushed efficiently in real time through the WebSocket protocol, and ordinary data streams are transmitted through the HTTP protocol;

[0015] Data storage module

[0016] A two-layer data storage architecture is used, including the in-memory database Redis and the relational database MySQL. Redis stores monitoring indicator data that needs to be displayed in real time, and MySQL stores historical data that does not require frequent read and write operations.

[0017] Condition Assessment Module

[0018] It includes: an evaluation index system construction unit, which is used to construct a turbine operation status evaluation index system, and the evaluation index system includes:

[0019] Top cover status indicators, including top cover x-axis vibration, top cover y-axis vibration, top cover pressure pulsation, and top cover pressure pulsation static pressure value;

[0020] Lower frame status indicators, including lower frame x-axis vibration and lower frame y-axis vibration;

[0021] Volute status indicators, including the average pressure at the volute inlet and the average pressure at the volute end;

[0022] Water guide mechanism status indicators, including guide vane outlet pressure pulsation static pressure, guide vane outlet pressure pulsation;

[0023] Draft tube status indicators, including the average draft tube outlet pressure;

[0024] The indicator weight determination unit uses the entropy weight method to determine the weight of the bottom-level indicators and the improved hierarchical analysis method to determine the weight of the middle-level indicators;

[0025] The degradation degree analysis unit is used to calculate the degradation degree according to the characteristics of the underlying indicators. The vibration monitoring value belongs to the smaller the better type of indicator, and the pressure value belongs to the intermediate better type of indicator. The degradation degree is calculated using the corresponding formula according to different types of indicators;

[0026] A fuzzy comprehensive evaluation unit is used to perform fuzzy comprehensive evaluation based on the underlying index membership model and the combined weight algorithm model to determine the operating status of the turbine;

[0027] Human-computer interaction module

[0028] Used to display monitoring data and evaluation results and receive user instructions, including:

[0029] The real-time monitoring interface displays the real-time collected data in the form of charts, such as the real-time values ​​and change trends of vibration, swing, and pressure pulsation data. Users can choose to display or hide the corresponding data by clicking the indicator name;

[0030] Data query interface, users can select query time and form, query historical monitoring data, and export data to Excel table;

[0031] The status assessment interface displays the multi-layer indicator system of the assessment system, including the underlying indicator logic diagram. It also displays the cumulative online hours of the system, the total system assessment score, the score trend of the unit's total score within a certain period of time, and the underlying indicator scores and intermediate indicator scores. The scores are displayed in the form of dashboard panels, tables, and bar charts.

[0032] Furthermore, the operating frequency range of the DP-type seismic low-frequency vibration sensor in the data acquisition module is 0.5200 Hz, and the sensitivity is 8 V / mm, which is used to accurately collect the vibration signals of the stationary parts of the turbine in the low-frequency band to monitor the vibration characteristics and changing trends during the operation of the turbine.

[0033] Furthermore, the measuring range of the eddy current displacement sensor is set according to the actual operating parameters of the turbine main shaft, and can accurately measure the changes in the main shaft swing within the range of ±200mm, where 200 is a suitable value determined according to the turbine model and operating conditions, ensuring accurate acquisition of radial displacement information between the main shaft and the bearing or other fixing devices.

[0034] Furthermore, the piezoresistive pressure sensor has a measurement accuracy of ±0.5% of the full range, and can accurately measure the pressure pulsation of the flow-through components of the turbine, providing high-precision data support for analyzing the pressure changes in the flow field inside the turbine.

[0035] Furthermore, the connection establishment process of the WebSocket protocol in the real-time data push module includes: the client sends an HTTP connection request to the server, and the request header contains an "Upgrade: websocket" identifier. After receiving the request, the server constructs a response message and returns it to the client. The client then sends a confirmation connection request to the server to establish a WebSocket connection, thereby realizing full-duplex communication between the client and the server and ensuring efficient and stable transmission of real-time data.

[0036] Furthermore, when the Redis database in the data storage module stores monitoring indicator data, a Hash data structure is adopted, with the monitoring indicator name as the Key value, the indicator quantity value as the Value value, the time when the indicator quantity value is generated is stored in the field value, and the data is marked by a timestamp to facilitate rapid query and management of the data, as well as analysis of data change trends.

[0037] Furthermore, the specific steps of determining the weights of the underlying indicators by the entropy weight method in the state assessment module are as follows:

[0038] Normalize the underlying indicator data. For positive indicators, use the formula:

[0039] For negative indicators, use the formula:

[0040]

[0041] , where X ij is the normalized index value, x ij is the jth index value of the i-th sample, and n is the number of samples;

[0042] Calculate the entropy value of the jth indicator

[0043] Where k = 1 / Inn

[0044] Calculate the redundancy d of the jth index j =1-e j ;

[0045] Calculate the weight of the jth indicator

[0046]

[0047] Where m is the number of underlying indicators.

[0048] Furthermore, when the degradation degree analysis unit calculates the degradation degree of the underlying indicator of the pressure value type, for the indicator value within the optimal interval [a, b], the degradation degree is 0, and for the indicator value less than a, the formula is used Calculate the degradation degree. For index values ​​greater than b, use the formula Calculate the degradation degree, where d i is the degradation degree, x i is the current indicator value, c is the minimum value allowed for the pressure value, and d is the maximum value allowed for the pressure value.

[0049] Furthermore, the data update cycle displayed on the real-time monitoring interface in the human-computer interaction module does not exceed 1 second, ensuring that users can obtain the latest monitoring data in a timely manner, and when the data is abnormal, an alarm prompt can be automatically issued, and the alarm mode includes sound alarm and pop-up prompt.

[0050] Furthermore, the data query interface in the human-computer interaction module supports a fuzzy query function, and users can quickly locate and query the required historical monitoring data by entering keywords, such as time range keywords, data type keywords, etc. The underlying indicator logic diagram displayed in the status evaluation interface in the human-computer interaction module adopts a hierarchical structure. The upper layer is the overall operating status of the turbine, the middle layer is the status of each component, and the lower layer is the specific indicators. By clicking on the upper or middle node, the lower node can be expanded or collapsed, which is convenient for users to view and understand the indicator system structure.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] Through various types of sensors, it is possible to collect data on the vibration of the turbine's stationary parts, the swing of the rotating parts, the pressure pulsation of the flow-through parts, and the rotor key phase information, comprehensively reflecting the turbine's operating status and providing a rich information basis for accurate evaluation.

[0053] The real-time data push module divides data into real-time data and ordinary data streams, and transmits them using the WebSocket protocol and HTTP protocol respectively, ensuring efficient and stable push of real-time data, so that the monitoring platform can obtain the latest data in a timely manner to monitor the operating status of the turbine in real time.

[0054] It adopts a two-layer data storage architecture, with Redis storing real-time display data and MySQL storing historical data. This not only meets the needs of fast reading and writing of real-time data, but also facilitates long-term and effective management and query of historical data, providing strong support for subsequent data analysis and status evaluation.

[0055] The status assessment module has built a complete evaluation index system, which uses the entropy weight method and the improved hierarchical analysis method to determine the index weights. Combined with degradation analysis and fuzzy comprehensive evaluation, it can accurately assess the operating status of the turbine, promptly discover potential fault hazards, and provide a basis for preventive maintenance.

[0056] The human-computer interaction module provides multiple interfaces such as real-time monitoring, data query and status evaluation. It displays data and results in the form of intuitive charts, supports data export, fuzzy query and hierarchical display of indicator systems, facilitates user operation and understanding, and improves the practicality and ease of use of the system.

[0057] Through real-time monitoring and accurate evaluation of the turbine's operating status, it is possible to adjust operating parameters in a timely manner, optimize turbine operating conditions, and improve power generation efficiency; at the same time, potential faults can be discovered and dealt with in advance, reducing the probability of accidents and ensuring the safe and stable operation of the hydropower station. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] See also Figure 1 The present invention provides a technical solution: an intelligent monitoring system for the operation status of a water turbine, which is mainly composed of a data acquisition module, a real-time data push module, a data storage module, a state evaluation module and a human-computer interaction module. The modules work together to achieve comprehensive monitoring, data transmission, storage, evaluation, and result display and interaction of the operation status of the water turbine.

[0061] Data acquisition module

[0062] DP type seismic low-frequency vibration sensor: A sensor with an operating frequency range of 0.5-200Hz and a sensitivity of 8V / mm is selected and installed on stationary parts such as the turbine top cover, upper frame, lower frame and stator base to collect low-frequency vibration signals to monitor their vibration characteristics and changing trends.

[0063] Eddy current displacement sensor: The measurement range is set to ±200mm according to the actual operating parameters of the turbine main shaft. It is installed at the three guide bearings. A detection point is set in the X-axis and Y-axis directions at each guide bearing to accurately measure the radial vibration of the main shaft and the bearings or other fixing devices during rotation.

[0064] Pressure sensor, at least one of piezoresistive, piezoelectric and capacitive types: the piezoresistive pressure sensor has a measurement accuracy of ±0.5% of the full scale and is installed at flow-passing parts such as the volute inlet, bladeless area, under the top cover, between the impeller and the discharge ring, tailwater cone and tailwater elbow to collect pressure pulsation signals and provide high-precision data for analyzing pressure changes in the internal flow field.

[0065] Key phase sensor: By marking raised key positions or grooves on the rotating parts, the sensor receives a pulse signal when it rotates to the key phase mark position, thereby obtaining information related to the key phase of the turbine rotor, which is used to monitor the unit speed, amplitude, frequency and phase angle of the rotor radial vibration.

[0066] Data collection process: Each sensor collects the corresponding signal in real time, converts the analog signal into a digital signal and transmits it to the controller of the data collection module. The controller performs preliminary processing on the collected data, such as filtering and amplification, to ensure the accuracy and reliability of the data.

[0067] Real-time data push module

[0068] The client sends an HTTP connection request to the server, and the request header contains the "Upgrade: websocket" identifier.

[0069] After receiving the request, the server constructs a response message and returns it to the client.

[0070] The client then sends a connection confirmation request to the server to complete the WebSocket connection establishment, achieving full-duplex communication between the client and the server and ensuring efficient and stable transmission of real-time data.

[0071] Data classification and push: The collected data is divided into real-time data and ordinary data streams. Real-time data is pushed to the monitoring platform in real time through the established WebSocket protocol to ensure that the monitoring platform can obtain the latest turbine operation status data in a timely manner; ordinary data streams are transmitted through the HTTP protocol and are used in some data transmission scenarios that do not require high real-time performance.

[0072] Data storage module

[0073] The monitoring indicator data is stored in a Hash data structure, with the monitoring indicator name as the Key value, the indicator value as the Value value, and the time when the indicator value is generated stored in the field value. The data is marked with a timestamp to facilitate quick query and management of the data, as well as analysis of data change trends. For example, for the top cover x-axis vibration data, "top cover x-axis vibration" can be used as the Key, the vibration value as the Value, and the collection time as the field value.

[0074] The Redis database can meet the needs of fast reading and writing of real-time data, ensuring that the monitoring platform can quickly obtain the required information when displaying real-time data.

[0075] MySQL stores historical data: historical data that does not require frequent read and write operations is stored in the relational database MySQL. It is classified and stored according to the data type, collection time and other information to facilitate subsequent query, statistics and analysis of historical data. For example, different data tables can be created according to different sensor types, and each data table contains fields such as collection time and data value.

[0076] Condition Assessment Module

[0077] An evaluation index system is constructed, including top cover status indicators, lower frame status indicators, volute status indicators, water guide mechanism status indicators, tailwater pipe status indicators, etc., to comprehensively reflect the operating status of various turbine components.

[0078] Entropy weight method determines the weight of underlying indicators

[0079] Normalize the underlying indicator data. For positive indicators, use the formula:

[0080] For negative indicators, use the formula:

[0081]

[0082] , where X ij is the normalized index value, x ij is the jth index value of the i-th sample, and n is the number of samples;

[0083] Calculate the entropy value of the jth indicator

[0084] Where k = 1 / Inn

[0085] Calculate the redundancy d of the jth index j =1-e j ;

[0086] Calculate the weight of the jth indicator

[0087]

[0088] Where m is the number of underlying indicators.

[0089] Improved analytic hierarchy process to determine the weights of intermediate-layer indicators: through expert evaluation, hierarchical structure analysis and other methods, a judgment matrix is ​​constructed, the relative weights of intermediate-layer indicators are calculated, and consistency tests are performed to ensure the rationality of weight distribution.

[0090] For indicators such as vibration monitoring, the smaller the better, the degree of degradation is calculated based on the degree of deviation between the actual measured value and the normal operating range. For example, when the vibration value exceeds the normal upper limit, the degree of degradation increases as the vibration value increases.

[0091] For intermediate indicators such as pressure values, the degradation degree is 0 according to the indicator value in the optimal interval [a, b]. For indicator values ​​less than a, the formula is used. Calculate the degradation degree. For index values ​​greater than b, use the formula Calculate the degradation degree, where d i is the degradation degree, x i is the current indicator value, c is the minimum value allowed for the pressure value, and d is the maximum value allowed for the pressure value.

[0092] Fuzzy comprehensive evaluation is performed based on the underlying indicator membership model and the combined weight algorithm model. By establishing the membership function of each indicator, the indicator value is mapped to the membership space, and then combined with the determined indicator weight, the comprehensive evaluation value of the turbine operation status is calculated to determine the turbine operation status, such as normal, slightly abnormal, seriously abnormal, etc.

[0093] Human-computer interaction module

[0094] The values ​​and changing trends of real-time collected data such as vibration, swing, and pressure pulsation are displayed in the form of graphs. The data update cycle does not exceed 1 second, ensuring that users can obtain the latest information in a timely manner.

[0095] Users can choose to display or hide the corresponding data by clicking on the indicator name, so that users can focus on key data. When the data is abnormal, an alarm and pop-up window will automatically sound to remind users to deal with it in time.

[0096] Users can select query time and table, and perform fuzzy query by entering keywords to quickly locate and query the required historical monitoring data.

[0097] Users can export the queried data to Excel tables for further data analysis and processing.

[0098] The multi-layer indicator system of the evaluation system is displayed, and the underlying indicator logic diagram is displayed in a hierarchical structure. The upper layer is the overall operating status of the turbine, the middle layer is the status of each component, and the lower layer is the specific indicator. By clicking the upper or middle layer node, the lower layer node can be expanded or collapsed, which is convenient for users to view and understand the indicator system structure.

[0099] It also displays the system's cumulative online hours, the total system evaluation score, the score trend of the unit's total score within a certain period of time, and the bottom-level indicator scores and intermediate-level indicator scores. The scores are displayed in the form of dashboard panels, tables, and bar charts, intuitively presenting the evaluation results of the turbine's operating status.

[0100] After the system is started, the sensors in the data acquisition module begin to collect data related to the turbine operation status in real time. For example, the DP seismic low-frequency vibration sensor continuously monitors the vibration of the stationary parts, the eddy current displacement sensor accurately measures the swing of the rotating parts, the pressure sensor collects the pressure pulsation of the flow-through parts, and the key phase sensor obtains the rotor key phase information.

[0101] After the collected data is initially processed, the real-time data push module pushes it according to the data type. Real-time data is quickly transmitted to the monitoring platform through the WebSocket protocol to ensure that the monitoring platform can instantly grasp the latest operating status of the turbine; ordinary data streams are transmitted in an orderly manner through the HTTP protocol.

[0102] The data storage module receives the pushed data, and the Redis database immediately stores the monitoring indicator data that needs to be displayed in real time, using its efficient read and write characteristics to ensure that the monitoring platform can quickly obtain and display real-time data; at the same time, the MySQL database stores historical data in categories to facilitate subsequent analysis of the long-term operating status of the turbine.

[0103] The status assessment module obtains data from the data storage module regularly. First, the evaluation index system constructed is used to classify and organize the data. Then, the entropy weight method and the improved hierarchical analysis method are used to determine the weight of each index. Then, the degradation degree is analyzed. Finally, the evaluation result of the turbine operation status is obtained through fuzzy comprehensive evaluation. For example, for the vibration data of the top cover, the weight is first determined according to its position in the evaluation index system, and then its degradation degree is analyzed. Finally, the impact of the top cover operation status on the whole machine is comprehensively evaluated in combination with other indicators.

[0104] The human-computer interaction module obtains data and results from the data storage module and the status assessment module in real time, and displays the latest data in dynamic charts on the real-time monitoring interface, so that operators can see it at a glance; in the data query interface, operators can flexibly query historical data according to needs; in the status assessment interface, the assessment system and results are clearly presented, providing operators with a comprehensive and intuitive basis for decision-making. Operators can adjust the turbine operating parameters in a timely manner based on the displayed information, optimize the operating conditions, and ensure the safe and efficient operation of the turbine. If an abnormality is found, maintenance personnel will be arranged for maintenance in a timely manner. Maintenance personnel can quickly locate the fault point and take targeted maintenance measures based on the detailed data and assessment results provided by the human-computer interaction module.

Claims

1. An intelligent monitoring system for the operation status of a hydraulic turbine, characterized in that: include: Data acquisition module Used to collect data related to the operation status of the turbine. The data acquisition module includes multiple sensors, each of which is used to collect different types of signals, as follows: DP type seismic low-frequency vibration sensor, used to collect vibration signals of stationary parts of turbines, including vibrations of top cover, upper frame, lower frame and stator base; The eddy current displacement sensor is used to collect the swing signal of the rotating parts of the turbine. It is installed at the three guide bearings. Two detection points are set at each guide bearing, marked as the X axis and the Y axis respectively, to collect the radial vibration of the main shaft and the bearing or other fixing devices during the rotation process; At least one of a piezoresistive pressure sensor, a piezoelectric pressure sensor and a capacitive pressure sensor, used to collect pressure pulsation signals of flow-through components of the turbine, including pressure pulsations at the volute inlet, the bladeless area, under the top cover, between the runner and the discharge ring, the tailwater cone and the tailwater elbow; The key phase sensor is used to obtain information related to the key phase of the turbine rotor. By marking the raised key position or groove on the rotating part, the sensor receives a pulse signal when it rotates to the key phase mark position to monitor the unit speed, the amplitude, frequency and phase angle of the rotor radial vibration; Real-time data push module Used to push the collected data to the monitoring platform in real time. This module divides the data into real-time data and ordinary data streams. Real-time data is pushed efficiently in real time through the WebSocket protocol, and ordinary data streams are transmitted through the HTTP protocol; Data storage module A two-layer data storage architecture is used, including the in-memory database Redis and the relational database MySQL. Redis stores monitoring indicator data that needs to be displayed in real time, and MySQL stores historical data that does not require frequent read and write operations. Condition Assessment Module It includes: an evaluation index system construction unit, which is used to construct a turbine operation status evaluation index system, and the evaluation index system includes: Top cover status indicators, including top cover x-axis vibration, top cover y-axis vibration, top cover pressure pulsation, and top cover pressure pulsation static pressure value; Lower frame status indicators, including lower frame x-axis vibration and lower frame y-axis vibration; Volute status indicators, including the average pressure at the volute inlet and the average pressure at the volute end; Water guide mechanism status indicators, including guide vane outlet pressure pulsation static pressure, guide vane outlet pressure pulsation; Draft tube status indicators, including the average tailwater tube outlet pressure; The indicator weight determination unit uses the entropy weight method to determine the weight of the bottom-level indicators and the improved hierarchical analysis method to determine the weight of the middle-level indicators; The degradation degree analysis unit is used to calculate the degradation degree according to the characteristics of the underlying indicators. The vibration monitoring value belongs to the smaller the better type of indicator, and the pressure value belongs to the intermediate better type of indicator. The degradation degree is calculated using the corresponding formula according to different types of indicators; A fuzzy comprehensive evaluation unit is used to perform fuzzy comprehensive evaluation based on the underlying index membership model and the combined weight algorithm model to determine the operating status of the turbine; Human-computer interaction module Used to display monitoring data and evaluation results and receive user instructions, including: The real-time monitoring interface displays the real-time collected data in the form of charts, such as the real-time values ​​and change trends of vibration, swing, and pressure pulsation data. Users can choose to display or hide the corresponding data by clicking the indicator name; Data query interface, users can select query time and form, query historical monitoring data, and export data to Excel table; The status assessment interface displays the multi-layer indicator system of the assessment system, including the underlying indicator logic diagram. It also displays the cumulative online hours of the system, the total system assessment score, the score trend of the unit's total score within a certain period of time, and the underlying indicator scores and intermediate indicator scores. The scores are displayed in the form of dashboard panels, tables, and bar charts.

2. The intelligent monitoring system for the operation status of a hydraulic turbine according to claim 1, characterized in that: The operating frequency range of the DP-type seismic low-frequency vibration sensor in the data acquisition module is 0.5200 Hz, and the sensitivity is 8 V / mm. It is used to accurately collect the vibration signals of the stationary parts of the turbine in the low-frequency band to monitor the vibration characteristics and change trends during the operation of the turbine.

3. The intelligent monitoring system for the operation status of a hydraulic turbine according to claim 1, characterized in that: The measuring range of the eddy current displacement sensor is set according to the actual operating parameters of the turbine main shaft, and can accurately measure the change of the main shaft swing within the range of ±200mm, where 200 is a suitable value determined according to the turbine model and operating conditions, ensuring accurate acquisition of radial displacement information between the main shaft and the bearing or other fixing devices.

4. The intelligent monitoring system for the operation status of a water turbine according to claim 1 is characterized in that: The piezoresistive pressure sensor has a measurement accuracy of ±0.5% of the full range, and can accurately measure the pressure pulsation of the flow-through components of the turbine, providing high-precision data support for analyzing the pressure changes in the flow field inside the turbine.

5. The intelligent monitoring system for the operation status of a water turbine according to claim 1 is characterized in that: The connection establishment process of the WebSocket protocol in the real-time data push module includes: the client sends an HTTP connection request to the server, and the request header contains "Upgrade: websocket" identifier. After receiving the request, the server constructs a response message and returns it to the client. The client then sends a confirmation connection request to the server to establish a WebSocket connection, realizing full-duplex communication between the client and the server and ensuring efficient and stable transmission of real-time data.

6. The intelligent monitoring system for the operation status of a hydraulic turbine according to claim 1, characterized in that: When the Redis database in the data storage module stores monitoring indicator data, a Hash data structure is adopted, with the monitoring indicator name as the Key value, the indicator quantity value as the Value value, the time when the indicator quantity value is generated is stored in the field value, and the data is marked by a timestamp to facilitate rapid query and management of the data, as well as analysis of data change trends.

7. The intelligent monitoring system for the operation status of a water turbine according to claim 1, characterized in that: The specific steps of determining the weights of the underlying indicators using the entropy weight method in the state assessment module are as follows: Normalize the underlying indicator data. For positive indicators, use the formula: For negative indicators, use the formula: , where X ij is the normalized index value, x ij is the jth index value of the i-th sample, and n is the number of samples; Calculate the entropy value of the jth indicator where k = 1 / Inn Calculate the redundancy d of the jth index j =1-e j ; Calculate the weight of the jth indicator Where m is the number of underlying indicators.

8. The intelligent monitoring system for the operation status of a water turbine according to claim 1, characterized in that: When the degradation degree analysis unit calculates the degradation degree of the underlying indicator of the pressure value type, for the indicator value within the optimal interval [a, b], the degradation degree is 0, and for the indicator value less than a, the formula is used. Calculate the degradation degree. For index values ​​greater than b, use the formula Calculate the degradation degree, where d i is the degradation degree, x i is the current indicator value, c is the minimum value allowed for the pressure value, and d is the maximum value allowed for the pressure value.

9. The intelligent monitoring system for the operation status of a water turbine according to claim 1, characterized in that: The data update cycle displayed on the real-time monitoring interface in the human-computer interaction module does not exceed 1 second, ensuring that users can obtain the latest monitoring data in a timely manner, and when the data is abnormal, an alarm prompt can be automatically issued, and the alarm mode includes sound alarm and pop-up prompt.

10. The intelligent monitoring system for the operation status of a water turbine according to claim 1, characterized in that: The data query interface in the human-computer interaction module supports a fuzzy query function. Users can quickly locate and query the required historical monitoring data by entering keywords, such as time range keywords, data type keywords, etc. The underlying indicator logic diagram displayed in the status evaluation interface in the human-computer interaction module adopts a hierarchical structure display. The upper layer is the overall operating status of the turbine, the middle layer is the status of each component, and the lower layer is the specific indicator. By clicking on the upper or middle node, the lower node can be expanded or collapsed, which is convenient for users to view and understand the indicator system structure.

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