Intelligent monitoring and fault self-diagnosis method for dam gate opening and closing state

Abstract

This application discloses an intelligent monitoring and fault self-diagnosis method for the opening and closing status of dam gates, including the following steps: S1, data acquisition; S2, vibration data analysis; S3, plotting frequency amplitude gene time series diagrams; S4, evaluating the opening and closing status of the gates; S5, fault diagnosis. This application collects vibration and pressure data of the dam gates using a spectral analyzer and pressure sensors, achieving multi-sensor data fusion analysis. In subsequent processing, the vibration and pressure data are combined to analyze the opening and closing status of the dam gates. Based on a mechanical fatigue strength-pressure coupling model, dynamic response analysis and fault diagnosis of the dam gates are achieved. The method can collect vibration and pressure data of the dam gates in real time, and perform comprehensive analysis through mathematical modeling techniques to evaluate the health status of the mechanical system, thereby achieving fault early warning and diagnosis, and providing a scientific basis for the safe operation of dam gates.

Description

Technical Field

[0001] This application relates to the field of dam gate technology, and in particular to a method for intelligent monitoring and fault self-diagnosis of dam gate opening and closing status. Background Technology

[0002] Dam gates are a crucial component of hydraulic engineering projects, responsible for regulating the opening and closing of water flow to ensure downstream flood control and water supply security. During long-term operation, dam gates are exposed to various environmental factors, including the impact of external water flow, changes in internal pressure, and mechanical vibration. These factors can lead to abnormal vibration, accelerated mechanical fatigue, or even severe damage to the dam gates. Traditional fault diagnosis methods primarily rely on data from single sensors, such as vibration or pressure sensors, which struggle to comprehensively capture the equipment's health status and are susceptible to noise interference in complex industrial environments, resulting in decreased diagnostic accuracy.

[0003] Traditional fault diagnosis methods primarily rely on data from single sensors, such as vibration or pressure sensors. While these methods can capture some information about the mechanical state, sensor signals are often subject to noise interference in complex industrial environments, leading to a decrease in the accuracy of fault diagnosis. Furthermore, traditional methods struggle to comprehensively reflect the dynamic response and health status of mechanical systems, especially under conditions of coupled external pressure and vibration, where fault diagnosis becomes less than ideal.

[0004] Furthermore, during the long-term operation of dam gates, vibration sensing data is often interfered with due to the complex industrial environment and various environmental factors, leading to a decrease in the accuracy of fault prediction. Traditional fault diagnosis methods mainly rely on time-domain vibration sensing data, which is insufficient to comprehensively capture the health status of the equipment.

[0005] To better achieve intelligent monitoring and fault self-diagnosis of dam gates, a multi-sensor data fusion-based intelligent monitoring and fault diagnosis method is proposed, combining spectral analysis, pressure sensor technology, and mechanical dynamics modeling techniques. This method can collect vibration and pressure data from dam gates in real time, and perform comprehensive analysis through mathematical modeling to assess the health status of the mechanical system, thereby enabling fault early warning and diagnosis and providing a scientific basis for the safe operation of dam gates. Summary of the Invention

[0006] The main purpose of this application is to provide an intelligent monitoring and fault self-diagnosis method for the opening and closing status of dam gates, in order to solve the problem that existing technologies rely on data from a single sensor. In complex industrial environments, sensor signals are often subject to noise interference, which leads to a decrease in the accuracy of fault diagnosis. Traditional fault diagnosis methods mainly rely on vibration sensing data in the time domain, which makes it difficult to comprehensively capture the health status of the equipment.

[0007] To achieve the above objectives, this application provides the following technical solution: The intelligent monitoring and fault self-diagnosis method for the opening and closing status of dam gates includes the following steps: S1. Data Acquisition: Vibration data of the dam gate is collected by a spectral analyzer, and impact force data of the water flow outside the dam gate is collected by a pressure sensor. After preprocessing, standardized vibration and pressure data are output. S2. Vibration data analysis: Perform frequency domain transformation on the standardized vibration data to obtain the frequency domain spectrum of the standardized vibration data. Obtain the frequency data of the vibration data through the horizontal axis nodes of the frequency domain spectrum, and obtain the amplitude data of the vibration data through the vertical axis nodes of the frequency domain spectrum. S3. Draw a frequency-amplitude gene time series diagram: Perform heterogeneous processing on the frequency data set and amplitude data set to obtain heterogeneous maps of the frequency data set and amplitude data set, extract gene data based on the heterogeneous maps, construct a frequency-amplitude gene time series diagram from the gene data, and combine with pressure data to obtain the reference vibration frequency and reference vibration amplitude, and then calculate the mechanical fatigue strength based on the reference vibration frequency and reference vibration amplitude. S4. Evaluate the gate opening and closing status: Couple mechanical fatigue strength and pressure data for calculation to monitor the gate opening and closing status and obtain gate opening and closing data; S5. Fault Diagnosis: Evaluate the fault level of the dam gates based on the gate opening and closing data, and provide maintenance recommendations based on the fault level.

[0008] Furthermore, the specific steps of S1 are as follows: First, the spectrometer is placed inside the dam gate and above the dam gate, so that the spectrometer can be tilted directly towards the dam gate, ensuring that the spectrometer can accurately capture the vibration data of the dam gate, and that the water flow will not impact the spectrometer. Pressure sensors are installed on the outside of the dam gate to collect pressure data by measuring the impact force of the water flow. The collected vibration and pressure data are then preprocessed.

[0009] Furthermore, the spectral analyzer in S1 collects vibration data as follows: A photoelectric conversion system is configured on the spectrometer to convert the mechanical stress of mechanical vibration into optical signals; The spectral analyzer collects the vibration signals of the dam gate in real time through a photoelectric conversion system; The spectrometer performs frequency domain decomposition on the collected vibration signal to extract the frequency and amplitude of the vibration signal.

[0010] Furthermore, the specific steps for extracting nodes from the frequency domain spectrum in S2 are as follows: Extract all horizontal and vertical axis nodes; Then all the horizontal axis nodes are combined into a frequency data set; And synthesize the amplitude data set of all vertical axis nodes.

[0011] Furthermore, the generation of the dissimilarity map in S3 is as follows: The frequency and amplitude datasets are differentiated by setting thresholds. This involves binary classification of the frequency and amplitude datasets, extracting the frequency and amplitude datasets below the threshold, and then regenerating the frequency and amplitude datasets above the threshold into a new dataset. This results in differentiated frequency and amplitude datasets, which are then used to generate a differentiated spectrum.

[0012] Furthermore, the gene data extraction in S3 is as follows: Frequency and amplitude data below the threshold are extracted. The accuracy of the frequency and amplitude data below the threshold is improved by optimizing the data. Specifically, a moving average filter is applied to the frequency and amplitude data below the threshold for smoothing. The smoothed frequency and amplitude data below the threshold are then output. The mean of the smoothed frequency and amplitude data below the threshold is calculated to obtain the gene data, and a frequency-amplitude gene time series graph is constructed.

[0013] Furthermore, the reference vibration frequency and reference vibration amplitude in S3 are calculated as follows: The reference vibration frequency and reference vibration amplitude are calculated by combining gene frequency data and gene amplitude data from the gene data with pressure data, and the gene frequency data is set as... Set gene amplitude data as Set the pressure data as : ; in, Represented as the reference vibration frequency, Represented as the frequency of pressure data; ; in, This is represented as the reference vibration amplitude. This is expressed as the magnitude of the pressure data.

[0014] Furthermore, the mechanical fatigue strength in S3 is calculated as follows: ; in, Expressed as mechanical fatigue strength, Represented as the frequency of vibration, This is expressed as the amplitude of the vibration. Represented as frequency coefficients, Represented as amplitude coefficient, It is represented as the material failure index, set as a real number between 0.5 and 1.

[0015] Furthermore, the gate opening and closing data in S4 are calculated as follows: A mechanical fatigue-pressure coupled model is constructed for dynamic response analysis and fault diagnosis of dam gates. Input parameters include mechanical fatigue strength. Stress data The relationship between pressure and mechanical fatigue strength is analyzed through a mechanical fatigue-pressure coupling model. Based on the mechanical fatigue strength output, the output is 1 for the presence of faults and 0 for the absence of faults. Based on the output of the mechanical fatigue-pressure coupling model, early warning and diagnosis of potential faults can be achieved.

[0016] Furthermore, the fault level in S5 is defined based on the operating status and health condition of the dam gate, and is defined as follows: Level 0, gate opening and closing data The gate is operating normally with no abnormal signs, and the mechanical components are in good health. Level 1, gate opening and closing data The gate exhibits slight vibration or slow wear, requiring regular monitoring but not immediate repair. Level 2, gate opening and closing data The gate exhibits moderate to heavy vibration or significant wear and requires immediate inspection and partial repair. Level 3, gate opening and closing data If the gate experiences severe vibration or major mechanical damage, it must be stopped immediately for comprehensive repair or replacement of parts.

[0017] This application collects vibration and pressure data from dam gates using a spectral analyzer and pressure sensors, achieving multi-sensor data fusion analysis. In subsequent processing, the vibration and pressure data are combined to analyze the opening and closing status of the dam gates. Based on a mechanical fatigue strength-pressure coupling model, dynamic response analysis and fault diagnosis of the dam gates are achieved. Maintenance recommendations are output; values ​​closer to 0 indicate a lower fault rate, while values ​​closer to 1 indicate a higher fault rate. This application demonstrates practicality and innovation. Furthermore, the vibration data collected by the spectral analyzer is analyzed. Frequency and amplitude are extracted through frequency domain transformation, and then heterogeneous processing is performed to stratify the frequency and amplitude data. Gene data is extracted from the frequency and amplitude data below the threshold after stratification, and a frequency-amplitude gene time series diagram is constructed. The reference vibration frequency and reference vibration amplitude are calculated by combining pressure data. Then, simple mechanical vibration is calculated using the reference vibration frequency and reference vibration amplitude and the frequency and amplitude data above the threshold. Then, by combining simple mechanical vibration with water flow impact pressure data, mechanical fatigue strength is obtained through calculation. This enables coupled calculation of mechanical fatigue strength and pressure data, monitoring the gate opening and closing status, obtaining gate opening and closing data, assessing the fault level of the dam gate based on the gate opening and closing data, and providing maintenance recommendations based on the fault level. It can collect vibration and pressure data of the dam gate in real time, and conduct comprehensive analysis through mathematical modeling technology to assess the health status of the mechanical system, thereby realizing fault early warning and diagnosis, and providing a scientific basis for the safe operation of the dam gate. Attached Figure Description

[0018] Figure 1 This is a schematic flowchart illustrating the steps of an embodiment of the intelligent monitoring and fault self-diagnosis method for the opening and closing status of dam gates in this application. Figure 2 This is a schematic diagram illustrating the specific steps of data acquisition in one embodiment of the intelligent monitoring and fault self-diagnosis method for the opening and closing status of dam gates in this application. Figure 3 This is a schematic diagram of the vibration data acquisition process using a spectral analyzer, representing an embodiment of the intelligent monitoring and fault self-diagnosis method for the opening and closing status of dam gates in this application. Figure 4 This is a schematic diagram illustrating the specific steps of node extraction from the frequency domain spectrum in an embodiment of the intelligent monitoring and fault self-diagnosis method for the opening and closing status of dam gates in this application. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0020] like Figure 1-4 As shown, this embodiment provides an example of a method for intelligent monitoring and fault self-diagnosis of dam gate opening and closing status. In this embodiment: S1. Data Acquisition: Vibration data of the dam gate is collected by a spectral analyzer, and impact force data of the water flow outside the dam gate is collected by a pressure sensor. After preprocessing, standardized vibration and pressure data are output. It should be noted that the vibration data of the dam gate is collected by a spectral analyzer, which facilitates the acquisition of the frequency and amplitude of the vibration data, and the pressure data of the water flow impact is collected by a pressure sensor, which facilitates the analysis and processing of the opening and closing status of the dam gate. The specific steps are as follows: First, the spectrometer is placed inside the dam gate and above the dam gate, so that the spectrometer can be tilted directly towards the dam gate, ensuring that the spectrometer can accurately capture the vibration data of the dam gate, and that the water flow will not impact the spectrometer. Pressure sensors are installed on the outside of the dam gate to collect pressure data by measuring the impact force of the water flow. It should be noted that by setting the different positions of the spectrometer and pressure sensor, it is easy to achieve effective data acquisition from the dam gate, without causing damage to the spectrometer from the water flow or impacting the water flow from the pressure sensor, thus facilitating the acquisition of accurate data. Then, the collected vibration and pressure data are preprocessed. Preliminary cleaning: Removes outliers or abrupt changes from vibration and pressure data to ensure signal stability; outputs preprocessed vibration and pressure data. High-pass filtering: A high-pass filter is used to analyze and process vibration and pressure data after removing high-frequency noise, remove low-frequency baseline drift in the signal, and retain high-frequency vibration and pressure data; it is set in the high-frequency part of the signal (such as above 100Hz) and adjusted according to the nature of the vibration and pressure data, and outputs vibration and pressure data after removing baseline drift. Moving average filtering: Smooths vibration and pressure data after removing baseline drift, removing local high-frequency noise; sets the number of samples to 5-20, and adjusts it according to the stability of vibration and pressure data; outputs noise-reduced and smoothed vibration and pressure data; Amplitude normalization: Normalizes the amplitude of the noise-reduced and smoothed vibration and pressure data to between 0 and 1, facilitating subsequent analysis and comparison; outputs the normalized vibration and pressure data.

[0021] It should be noted that the preprocessing of vibration and pressure data, through low-pass filtering, baseline removal, noise reduction, and amplitude normalization, ensures the quality and stability of vibration and pressure data, significantly improves the quality of vibration and pressure data, reduces noise interference, and ensures more accurate and reliable subsequent fault diagnosis and analysis.

[0022] In this embodiment, preferably, the spectral analyzer acquires vibration data as follows: A photoelectric conversion system is configured on the spectrometer to convert the mechanical stress of mechanical vibration into optical signals; The spectral analyzer collects the vibration signals of the dam gate in real time through a photoelectric conversion system; The spectrometer performs frequency domain decomposition on the collected vibration signal to extract the frequency and amplitude of the vibration signal; It should be noted that during the operation of dam gates, vibration data affects the health status of the gates. Traditional vibration detection methods mainly rely on vibration sensing data in the time domain. Although these methods can capture the basic characteristics of vibration, in complex environments, vibration data is often affected by noise, leading to a decrease in the accuracy of fault diagnosis. Therefore, the introduction of spectral analysis technology can extract more information from frequency domain features, thereby improving the accuracy and reliability of fault diagnosis.

[0023] S2. Vibration data analysis: Perform frequency domain transformation on the standardized vibration data to obtain the frequency domain spectrum of the standardized vibration data. Obtain the frequency data of the vibration data through the horizontal axis nodes of the frequency domain spectrum, and obtain the amplitude data of the vibration data through the vertical axis nodes of the frequency domain spectrum. It should be noted that by converting the standardized vibration data to the frequency domain and obtaining the frequency domain spectrum, it is convenient to extract frequency and amplitude data from the standardized vibration data. The horizontal axis of the frequency domain spectrum represents the frequency data, and the frequency of the vibration signal can be directly obtained by reading the horizontal axis label of the frequency domain spectrum. The vertical axis of the frequency domain spectrum represents the amplitude data, which reflects the vibration intensity and is determined by reading the vertical axis of the frequency domain spectrum or by calculating the maximum value of the standardized vibration data. In this embodiment, the preferred steps for extracting nodes from the frequency domain graph are as follows: Extract all horizontal and vertical axis nodes; Then all the horizontal axis nodes are combined into a frequency data set; And synthesize the amplitude data set of all vertical axis nodes; It should be noted that by aggregating the collected horizontal and vertical axis nodes, the integrity and completeness of the data are maintained, facilitating subsequent analysis and processing.

[0024] S3. Draw a frequency-amplitude gene time series diagram: Perform heterogeneous processing on the frequency data set and amplitude data set to obtain heterogeneous maps of the frequency data set and amplitude data set, extract gene data based on the heterogeneous maps, construct a frequency-amplitude gene time series diagram from the gene data, and combine with pressure data to obtain the reference vibration frequency and reference vibration amplitude, and then calculate the mechanical fatigue strength based on the reference vibration frequency and reference vibration amplitude. It should be noted that by analyzing and processing the frequency and amplitude data sets, it is easy to obtain the reference vibration frequency and reference vibration amplitude, which facilitates the calculation of mechanical fatigue strength. Furthermore, by combining pressure data, the reference vibration frequency and reference vibration amplitude can be effectively calculated, thereby improving the accuracy of mechanical fatigue strength calculation.

[0025] In this embodiment, preferably, the alienation map is generated as follows: The frequency and amplitude datasets are differentiated by setting thresholds. This involves binary classification of the frequency and amplitude datasets, extracting the frequency and amplitude datasets below the threshold, and then regenerating the frequency and amplitude datasets above the threshold into a set, resulting in differentiated frequency and amplitude datasets. A differentiated spectrum is then generated from the differentiated frequency and amplitude datasets. It should be noted that by collecting a large amount of historical data and setting thresholds, the frequency and amplitude datasets can be heterogeneously processed, allowing them to be divided into two parts, or the frequency and amplitude datasets can be averaged. By calculating the average of the frequency and amplitude, the frequency and amplitude datasets can be stratified to obtain heterogeneous frequency and amplitude datasets. Then, the frequency and amplitude data above the threshold are used to regenerate the spectrum to obtain the heterogeneous spectrum. In this embodiment, the preferred method for extracting gene data is as follows: Frequency and amplitude data below the threshold are extracted. The accuracy of the frequency and amplitude data below the threshold is improved by optimizing the data. Specifically, a moving average filter is applied to the frequency and amplitude data below the threshold for smoothing. The smoothed frequency and amplitude data below the threshold are then output. The mean of the smoothed frequency and amplitude data below the threshold is calculated to obtain the gene data, and a frequency-amplitude gene time series graph is constructed. It should be noted that by optimizing and smoothing the frequency and amplitude data below the threshold, the uniformity and accuracy of the frequency and amplitude data below the threshold are maintained. Furthermore, by performing mean calculation, the gene data can be adjusted in a timely manner according to specific parameter data. In addition, the gene data settings are combined with pressure data to calculate the reference vibration frequency and reference vibration amplitude.

[0026] In this embodiment, preferably, the reference vibration frequency and reference vibration amplitude are calculated as follows: The reference vibration frequency and reference vibration amplitude are calculated by combining gene frequency data and gene amplitude data from the gene data with pressure data, and the gene frequency data is set as... Set gene amplitude data as Set the pressure data as : ; in, Represented as the reference vibration frequency, This is represented by the frequency of the pressure data, and the frequency of the pressure data is calculated as follows: ; in, Represented as periodic Pressure data at the end of the timer Represented as periodic The pressure data at the start of the timing period This is represented as the periodic time value of the pressure data; ; in, This is represented as the reference vibration amplitude. This is represented as the amplitude of the pressure data, and the amplitude of the pressure data is calculated as follows: ; in, This represents the maximum value of the pressure data. Represented as the minimum value of the pressure data; It should be noted that the above calculation formula can be used to calculate the reference vibration frequency and reference vibration amplitude based on the gene frequency data, gene amplitude data and pressure data. This facilitates the subsequent calculation of mechanical fatigue strength based on the reference vibration frequency and reference vibration amplitude, thereby obtaining the fault state of the large white gate.

[0027] In this embodiment, the mechanical fatigue strength is preferably calculated as follows: ; in, Expressed as mechanical fatigue strength, Represented as the frequency of vibration, This is expressed as the amplitude of the vibration. Represented as frequency coefficients, Represented as amplitude coefficient, This is expressed as the material failure index, set as a real number between 0.5 and 1; Vibration frequency The calculation is as follows: ; in, Represented as a set of alienation frequency data above the threshold; The amplitude of the vibration is calculated as follows: ; in, This represents the set of data representing the degree of alienation above the threshold.

[0028] It should be noted that by calculating based on the data sets of heterogeneous frequencies and heterogeneous amplitudes above the threshold, the self-vibration data of the dam gate can be obtained. Furthermore, by performing individual calculations and analyses on each data set to obtain the mechanical fatigue strength, the fault information of the dam gate at each moment can be calculated, facilitating timely assessment and effectively improving the safety of the dam gate during its use.

[0029] S4. Evaluate the gate opening and closing status: Couple mechanical fatigue strength and pressure data for calculation to monitor the gate opening and closing status and obtain gate opening and closing data; It should be noted that by combining mechanical fatigue data with pressure data, we can analyze the opening and closing data of dam gates under the impact of water flow, which facilitates the analysis of dam gate failure information. In this embodiment, the gate opening and closing data are preferably calculated as follows: A mechanical fatigue-pressure coupled model is constructed for dynamic response analysis and fault diagnosis of dam gates. Input parameters include mechanical fatigue strength. Stress data The relationship between pressure and mechanical fatigue strength is analyzed through a mechanical fatigue-pressure coupling model. Based on the mechanical fatigue strength output, the output is 1 for the presence of faults and 0 for the absence of faults. Based on the output of the mechanical fatigue-pressure coupling model, early warning and diagnosis of potential faults can be achieved.

[0030] The specific calculation formula is as follows: ; in, This represents gate opening and closing data, and This represents the total calculated mechanical fatigue strength. Represented as the mechanical fatigue strength sequence in the calculation, and The weight value is represented as the same as the weight value of the pressure data used in the calculation node for mechanical fatigue strength, and is set to 0.3.

[0031] It should be noted that, The output value is between 0 and 1. The closer the output value is to 0, the lower the gate's fault condition. The closer the output value is to 1, the higher the gate's fault condition, and the more maintenance is required.

[0032] S5. Fault Diagnosis: Evaluate the fault level of the dam gates based on the gate opening and closing data, and provide maintenance recommendations based on the fault level. It should be noted that classifying the gate opening and closing data into different levels allows monitoring personnel to directly and visually observe the results, thus improving the clarity of the monitoring.

[0033] In this embodiment, preferably, the following fault levels are defined based on the operating status and health condition of the dam gates: Level 0, gate opening and closing data The gate is operating normally with no abnormal signs, and the mechanical components are in good health. Level 1, gate opening and closing data The gate exhibits slight vibration or slow wear, requiring regular monitoring but not immediate repair. Level 2, gate opening and closing data The gate exhibits moderate to heavy vibration or significant wear and requires immediate inspection and partial repair. Level 3, gate opening and closing data If the gate is experiencing severe vibration or major mechanical damage, it must be stopped immediately for comprehensive repair or replacement of parts. It should be noted that the fault level of the dam gate can be assessed based on the opening and closing status data of the dam gate, combined with the mechanical fatigue strength-pressure coupling parameters, and corresponding maintenance suggestions can be generated. The output maintenance suggestion value is between 0 and 1. The closer the value is to 0, the lower the fault level of the gate, and the closer the value is to 1, the higher the fault level of the gate, requiring maintenance. This can provide scientific decision support for the intelligent monitoring and maintenance of dam gates, ensuring their long-term stable operation.

[0034] The storage medium of this application includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, or terminal devices such as computers, servers, mobile phones, and tablets.

[0035] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0036] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units. The above are merely embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

[0037] The specific embodiments of the invention have been described in detail above, but they are only examples, and this application is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications or substitutions to the invention are also within the scope of this application. Therefore, all equivalent changes, modifications, and improvements made without departing from the spirit and principles of this application should be covered within the scope of this application.

Claims

1. A method for intelligent monitoring and self-diagnosis of faults in the opening and closing status of dam gates, characterized in that, It includes the following steps: S1. Data Acquisition: Vibration data of the dam gate is acquired through a spectral analyzer, and impact force data of the water flow outside the dam gate is acquired through a pressure sensor. After preprocessing, standardized vibration and pressure data are output. S2. Vibration data analysis: Perform frequency domain transformation on the standardized vibration data to obtain the frequency domain spectrum of the standardized vibration data. Obtain the frequency data of the vibration data through the horizontal axis nodes of the frequency domain spectrum, and obtain the amplitude data of the vibration data through the vertical axis nodes of the frequency domain spectrum. S3. Draw a frequency-amplitude gene time series diagram: Perform heterogeneous processing on the frequency data set and amplitude data set to obtain heterogeneous maps of the frequency data set and amplitude data set, extract gene data based on the heterogeneous maps, construct a frequency-amplitude gene time series diagram from the gene data, and combine with pressure data to obtain the reference vibration frequency and reference vibration amplitude, and then calculate the mechanical fatigue strength based on the reference vibration frequency and reference vibration amplitude. The alienation map in S3 is generated as follows: The frequency and amplitude datasets are differentiated by setting thresholds. This involves binary classification of the frequency and amplitude datasets, extracting the frequency and amplitude datasets below the threshold, and then regenerating the frequency and amplitude datasets above the threshold into a set, resulting in differentiated frequency and amplitude datasets. A differentiated spectrum is then generated from the differentiated frequency and amplitude datasets. The gene data extracted from S3 are as follows: Frequency and amplitude data below the threshold are extracted. The accuracy of the frequency and amplitude data below the threshold is improved by optimizing the data. Specifically, a moving average filter is applied to the frequency and amplitude data below the threshold for smoothing. The smoothed frequency and amplitude data below the threshold are then output. The mean of the smoothed frequency and amplitude data below the threshold is calculated to obtain the gene data, and a frequency-amplitude gene time series graph is constructed. The reference vibration frequency and reference vibration amplitude in S3 are calculated as follows: The reference vibration frequency and reference vibration amplitude are calculated by analyzing and combining gene frequency data and gene amplitude data from the gene data with pressure data, and the gene frequency data is set as... Set gene amplitude data as Set the pressure data as : ； in, Represented as the reference vibration frequency, Represented as the frequency of pressure data; ； in, This is represented as the reference vibration amplitude. This is expressed as the magnitude of the pressure data; The mechanical fatigue strength in S3 is calculated as follows: ； in, Expressed as mechanical fatigue strength, Represented as the frequency of vibration, Expressed as the amplitude of vibration, Represented as frequency coefficients, Represented as amplitude coefficient, This is expressed as the material failure index, set as a real number between 0.5 and 1; S4. Evaluate the gate opening and closing status: Couple mechanical fatigue strength and pressure data for calculation to monitor the gate opening and closing status and obtain gate opening and closing data; The gate opening and closing data in S4 are calculated as follows: A mechanical fatigue-pressure coupled model is constructed for dynamic response analysis and fault diagnosis of dam gates. Input parameters include mechanical fatigue strength. Pressure data The relationship between pressure and mechanical fatigue strength is analyzed using a mechanical fatigue-pressure coupling model. Based on the mechanical fatigue strength output, the system classifies the presence of faults as 1 and the absence of faults as 0. Based on the output of the mechanical fatigue-pressure coupling model, early warning and diagnosis of potential faults can be achieved. The specific calculation formula is as follows: ； in, This represents gate opening and closing data, and This represents the total calculated mechanical fatigue strength. Represented as the mechanical fatigue strength sequence in the calculation, and The weight value is represented as the same as the weight value of the pressure data used in the calculation node for mechanical fatigue strength, and is set to 0.

3. S5. Fault Diagnosis: Evaluate the fault level of the dam gates based on the gate opening and closing data, and provide maintenance recommendations based on the fault level.

2. The intelligent monitoring and fault self-diagnosis method for the opening and closing status of dam gates according to claim 1, characterized in that, The specific steps of S1 are as follows: First, the spectrometer is placed inside the dam gate and above the dam gate, so that the spectrometer can be tilted directly towards the dam gate, ensuring that the spectrometer can accurately capture the vibration data of the dam gate, and that the water flow will not impact the spectrometer. Pressure sensors are installed on the outside of the dam gate to collect pressure data by measuring the impact force of the water flow. The collected vibration and pressure data are then preprocessed.

3. The intelligent monitoring and fault self-diagnosis method for the opening and closing status of dam gates according to claim 2, characterized in that, The vibration data collected by the spectrometer in S1 is as follows: A photoelectric conversion system is configured on the spectrometer to convert the mechanical stress of mechanical vibration into optical signals; The spectral analyzer collects the vibration signals of the dam gate in real time through a photoelectric conversion system; The spectrometer performs frequency domain decomposition on the collected vibration signal to extract the frequency and amplitude of the vibration signal.

4. The intelligent monitoring and fault self-diagnosis method for the opening and closing status of dam gates according to claim 1, characterized in that, The specific steps for extracting nodes from the frequency domain spectrum in S2 are as follows: Extract all horizontal and vertical axis nodes; Then all the horizontal axis nodes are combined into a frequency data set; And synthesize the amplitude data set of all vertical axis nodes.

5. The intelligent monitoring and fault self-diagnosis method for the opening and closing status of dam gates according to claim 1, characterized in that, The fault level in S5 is defined based on the operating status and health condition of the dam gate, and is defined as follows: Level 0, gate opening and closing data The gate is operating normally with no abnormal signs, and the mechanical components are in good health. Level 1, gate opening and closing data The gate exhibits slight vibration or slow wear, requiring regular monitoring but not immediate repair. Level 2, gate opening and closing data The gate exhibits moderate to heavy vibration or significant wear and requires immediate inspection and partial repair. Level 3, gate opening and closing data If the gate experiences severe vibration or major mechanical damage, it must be stopped immediately for comprehensive repair or replacement of parts.

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