A fault detection method and system for a wireless powered flap door

Abstract

This application discloses a fault detection method and system for a wirelessly powered flap gate, relating to the field of photovoltaic equipment manufacturing technology. The method utilizes the characteristic that the opening and closing of the flap gate is a repetitive and regular mechanical reciprocating motion. Due to water pressure, each flap gate at different depths will actively or passively change its opening and closing time; that is, the deeper the water, the higher the water pressure, and the longer the opening and closing time of the flap gate. At the same time, due to the wear phenomenon that slowly accumulates over time during the reciprocating process, this application utilizes the characteristic that linear regression coefficients can reflect the correlation and importance of independent and dependent variables. By analyzing the correlation between the opening and closing actions of each flap gate at different water depths and the surrounding environment, abnormal coefficients are identified, thereby identifying faults. This application is not only closely related to the working environment of flap gates, but also fills the gap in fault detection of stratified water intake equipment.

Description

Technical Field

[0001] This application relates to the field of photovoltaic equipment manufacturing technology, and in particular to a fault detection method and system for a wirelessly powered tongue-and-valve door. Background Technology

[0002] Stratified water intake is a technique for extracting water from reservoirs, lakes, or other bodies of water at different depths. Its main purpose is to selectively obtain water from specific depths based on water quality, temperature, or ecological needs. Since water bodies often exhibit vertical stratification (such as temperature stratification, dissolved oxygen stratification, and differences in nutrient distribution), stratified water intake uses adjustable intakes (such as multi-layer gates or float-type water intake devices) to extract water from the target water layer, avoiding the extraction of unsuitable water layers.

[0003] Due to its high cost, technical complexity, and specificity, stratified water intake technology has not been widely adopted and is currently only seen in large-scale water conservancy projects and a small number of drinking water reservoirs.

[0004] In summary, the lack of widespread adoption of stratified water intake technology has resulted in stratified water intake equipment fault detection remaining in a blank or experimental stage. Currently, equipment fault detection typically relies on manual inspections and meter reading analysis, which require human intervention. Furthermore, since stratified water intake equipment is in an underwater environment for extended periods, manual inspections are challenging, and meter reading analysis often only becomes available when obvious equipment malfunctions occur, leading to unsatisfactory results and accuracy in stratified water intake. Summary of the Invention

[0005] The main objective of this application is to provide a fault detection method and system for a wirelessly powered tongue-and-valve gate, so as to solve the problem that the fault detection effect and accuracy of the existing stratified water intake technology are not ideal.

[0006] To achieve the above objectives, this application provides the following technical solution: A fault detection method for a wirelessly powered flap gate, wherein the wirelessly powered flap gate is applied to a tiered water intake working gate, the tiered water intake working gate includes at least one set of flap gates with different height differences, all flap gates are engaged in the gate slot of the dam body, and are used to rotate according to different water intake needs to achieve their respective opening or closing purposes, each flap gate has a wireless power supply function, and the fault detection method includes: Step S1: Under underwater operating conditions, obtain the opening rotation time required for each flap valve to go from fully closed to fully open, and the closing rotation time required to go from fully open to fully closed. Step S2: Based on a preset natural time duration, obtain the water temperature, water flow rate, and water purity of each layer of the water body at the depth of the tongue valve in the water area where the stratified water intake gate is located; Step S3: Obtain the number of times each lingual valve is used within each preset natural time period; Step S4: Based on the same preset natural duration, analyze the first relationship between the opening and rotation duration and the water temperature, the water flow rate, the water purity, the water depth, and the number of uses. Step S5: Based on the same preset natural duration, analyze the second relationship between the shutdown rotation duration and the water temperature, the water flow rate, the water purity, the water depth, and the number of uses. Step S6: Integrate the first coefficients of all first mutual relations into a first matrix, and integrate the second coefficients of all second mutual relations into a second matrix; Step S7: Obtain the matrix similarity between the first matrix and the second matrix, and when the matrix similarity is less than a first preset ratio, obtain the element similarity at the same element positions of the two matrices; Step S8: When the element similarity at one of the same element positions approaches 1, it is determined that the corresponding tongue flap door is in a non-working state, that is, the wireless power supply function of the corresponding tongue flap door is not enabled. Step S9: When the element similarity at one of the same element positions is less than the second preset ratio, it is determined that the corresponding tongue flap door has a mechanical failure.

[0007] As a further improvement to this application, in step S8, when the element similarity at one of the same element positions approaches 1, it is determined that the corresponding tongue flap gate is in a non-working state, that is, the wireless power supply function of the corresponding tongue flap gate is not enabled. Afterwards, the process includes: Step S10: Define the tongue valve gate where the wireless power supply function is not enabled as an abnormal tongue valve gate; Step S20: Obtain the voltage and current values ​​of all abnormal tongue flap gates; Step S30: If the voltage and current values ​​of the current abnormal valve gate are both zero, then the current abnormal valve gate is determined to be in a circuit open state. Step S40: If the voltage value of the current abnormal valve gate is zero and the current value is infinite, then the current abnormal valve gate is determined to be in a short-circuit state.

[0008] As a further improvement to this application, in step S40, if the voltage value of the current abnormal flap gate is zero and the current value is infinite, then it is determined that the current abnormal flap gate is in a short-circuit state. Afterwards, the process includes: Step S100: Obtain the location information and device number of each abnormal tongue flap. Step S200: If the current abnormal tongue valve is in a circuit open state, then generate a device open circuit signal; Step S300: Pack the device disconnection signal, the location information corresponding to the current abnormal tongue flap, and the device number into a first abnormal data packet; Step S400: Send the first abnormal data packet to the external monitoring terminal; Step S500: If the current abnormal tongue valve is in a short-circuit state, then generate a device short-circuit signal; Step S600: Pack the device short-circuit signal, the location information corresponding to the current abnormal tongue flap, and the device number into a second abnormal data packet; Step S700: Send the second abnormal data packet to the external monitoring terminal.

[0009] As a further improvement to this application, step S9, when the element similarity at one of the same element positions is less than a second preset ratio, determines that the corresponding tongue flap has a mechanical failure, and then includes: Step S1000: Define the tongue valve that causes mechanical failure as an abnormal tongue valve. Step S2000: Obtain vibration data of the current abnormal tongue flap during the opening or closing process; Step S3000: Abnormal vibration data are filtered out from all vibration data using a classification algorithm; Step S4000: Obtain the abnormal tongue valve door that appears most frequently in the abnormal vibration data and mark it as the faulty tongue valve door.

[0010] As a further improvement to this application, step S30 involves filtering out abnormal vibration data from all vibration data using a classification algorithm, including: Step S30001: Obtain the signal components of each vibration data point through empirical mode decomposition. Step S30002: Classify all signal components of the same lingual valve using the classification algorithm to obtain at least two signal features, each signal feature including at least one signal component; Step S30003: Based on all tongue flaps, delete all identical signal features and mark the remaining signal features as abnormal features; Step S30004: Mark the vibration data corresponding to each of the abnormal features as abnormal vibration data.

[0011] As a further improvement to this application, step S4, based on the same preset natural duration, analyzes the first interrelationship between the opening rotation duration and the water temperature, the water flow rate, the water purity, the water depth, and the number of uses, including: Step S41: Define the start-up and rotation duration of all preset natural durations as known dependent variables, and define all water temperatures, all water flow rates, all water purity, all water depths, and all usage times as known independent variables. Step S42: Define a linear regression equation for the known dependent variable and known independent variable with the same preset natural duration using multiple linear regression. Step S43: Integrate all the linear regression equations with preset natural durations into a system of linear regression equations; Step S44: Solve for all unknown linear regression coefficients of the linear regression equation system using the least squares method; Step S45: Substitute all the known linear regression coefficients obtained from the solution into the linear regression equation system to obtain the first mutual relationship.

[0012] As a further improvement to this application, step S5, based on the same preset natural duration, analyzes the second interrelationship between the shutdown rotation duration and the water temperature, the water flow rate, the water purity, the water depth, and the number of uses, including: Step S51: The rotation-on duration in step S41 is replaced with the rotation-off duration as the execution subject. Step S52: Repeat steps S41 to S45 with the closed rotation duration as the execution subject to obtain the second mutual relationship.

[0013] To achieve the above objectives, this application also provides the following technical solutions: A fault detection system for a wirelessly powered flap door, wherein the fault detection system is applied to the fault detection method described above, and the fault detection system includes: The flap valve opening and closing time acquisition module is used to acquire, under underwater operating conditions, the opening rotation time required for each flap valve to go from fully closed to fully open each time, and the closing rotation time required to go from fully open to fully closed. The tongue-valve gate environmental parameter acquisition module is used to acquire the water temperature, water flow velocity, and water purity of each layer of the water body at the depth of the tongue-valve gate in the water area where the layered water intake gate is located, based on a preset natural time. The module for obtaining the number of times each lingual valve is used is used to obtain the number of times each lingual valve is used within each preset natural time period. The tongue flap opening process analysis module is used to analyze the first relationship between the opening rotation time and the water temperature, the water flow rate, the water purity, the water depth, and the number of times it is used, based on the same preset natural time. The tongue flap closure process analysis module is used to analyze the second relationship between the closure rotation time and the water temperature, the water flow rate, the water purity, the water depth, and the number of uses, based on the same preset natural time. The cross-relationship matrix integration module is used to integrate the first coefficients of all first cross-relationships into a first matrix and the second coefficients of all second cross-relationships into a second matrix. The matrix and element similarity acquisition module is used to acquire the matrix similarity between the first matrix and the second matrix, and when the matrix similarity is less than a first preset ratio, to acquire the element similarity at the same element positions of the two matrices; The wireless power supply function judgment module for the tongue flap gate is used to determine that the corresponding tongue flap gate is not working when the element similarity at one of the same element positions approaches 1, that is, the wireless power supply function of the corresponding tongue flap gate is not enabled. The tongue flap gate mechanical fault judgment module is used to determine that the corresponding tongue flap gate has a mechanical fault when the element similarity at one of the same element positions is less than a second preset ratio.

[0014] To achieve the above objectives, this application also provides the following technical solutions: An electronic device includes a processor and a memory coupled to the processor, the memory storing program instructions executable by the processor; when the processor executes the program instructions stored in the memory, it implements the fault detection method described above.

[0015] To achieve the above objectives, this application also provides the following technical solutions: A storage medium storing program instructions that, when executed by a processor, enable the fault detection method described above.

[0016] Beneficial effects: This application, under underwater operating conditions, obtains the opening rotation time required for each flap gate to go from fully closed to fully open, and the closing rotation time required to go from fully open to fully closed. Based on a preset natural time, it obtains the water temperature, flow velocity, and purity at each depth of the flap gate in the water area where the stratified water intake gate is located. It also obtains the number of times each flap gate is used within each preset natural time period. Based on the same preset natural time period, it analyzes the first correlation between the opening rotation time and water temperature, flow velocity, water purity, water depth, and number of uses. Based on the same preset natural time period, it analyzes the first correlation between the closing rotation time and water temperature. The system identifies the following relationships: water flow velocity, water purity, water depth, and number of uses; integrates the first coefficients of all first relationships into a first matrix and the second coefficients of all second relationships into a second matrix; obtains the matrix similarity between the first and second matrices, and when the matrix similarity is less than a first preset ratio, obtains the element similarity at the same element positions in the two matrices; when the element similarity at one of the same element positions approaches 1, it is determined that the corresponding flap door is in a non-working state, that is, the wireless power supply function of the corresponding flap door is not enabled; when the element similarity at one of the same element positions is less than a second preset ratio, it is determined that the corresponding flap door has a mechanical failure. This application utilizes the characteristic that the opening and closing of the flap gate is a repetitive and regular mechanical reciprocating motion. Because water pressure causes each flap gate at different depths to actively or passively change its opening and closing time—that is, the deeper the water and the higher the water pressure, the longer the opening and closing time of the flap gate—(under high pressure, the gate body needs to overcome greater hydrostatic pressure, and the water pressure acts directly on the gate panel, causing the hydraulic system or motor to output more power to start. Furthermore, when closing the gate in a high-pressure area, the impact force of the water flow on the gate body is stronger, requiring deceleration to avoid instantaneous pressure damage to the sealing structure). Simultaneously, because the flap gate experiences slow, cumulative wear over time during the reciprocating process, this application utilizes the characteristic that linear regression coefficients can reflect the correlation and importance of independent and dependent variables. By analyzing the correlation between the opening and closing actions of each flap gate at different water depths and the surrounding environment, abnormal coefficients are identified, thereby identifying faults. This application is not only closely related to the working environment of the flap gate but also fills the gap in fault detection for stratified water intake equipment. Attached Figure Description

[0017] Figure 1 A schematic diagram of the installation of a flap door according to an embodiment of the fault detection method for a wirelessly powered flap door of this application; Figure 2 A top view of a tongue-shaped door, representing an embodiment of the fault detection method for a wirelessly powered tongue-shaped door according to this application; Figure 3 A schematic diagram of the tongue valve structure of an embodiment of the fault detection method of the wirelessly powered tongue valve of this application; Figure 4 A flowchart illustrating the steps of an embodiment of the fault detection method for a wirelessly powered tongue-and-valve door according to this application; Figure 5 A schematic diagram of functional modules for an embodiment of the fault detection system for a wirelessly powered tongue-and-valve gate according to this application; Figure 6 This is a schematic diagram of the structure of an embodiment of the electronic device of this application; Figure 7 This is a schematic diagram of the structure of one embodiment of the storage medium of this application. Detailed Implementation

[0018] 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.

[0019] The terms "first," "second," and "third" in this application are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this application are only used to explain the relative positional relationships and movements between components in a specific orientation (as shown in the figures). If the specific orientation changes, the directional indications also change accordingly. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0020] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0021] This embodiment provides an example of a fault detection method for a wirelessly powered flap gate. In this embodiment, the wirelessly powered flap gate is applied to a tiered water intake working gate. The tiered water intake working gate includes at least one set of flap gates with different height differences. All flap gates are snapped into the gate slots of the dam body and are used to rotate according to different water intake needs to achieve their respective opening or closing purposes. Each flap gate has a wireless power supply function.

[0022] Preferably, see Figure 1 , Figure 2 , Figure 3 Because the flap gate is powered wirelessly to prevent excessive cables from affecting underwater installation or maintenance, a modular design is adopted for wireless power supply. Each modular component equipped with the flap gate includes a stacked beam frame and a drive device installed on both sides of the stacked beam frame to drive the rotation of the rotating shaft within the inner frame of the stacked beam frame. The flap gate is installed on the rotating shaft and is driven to rotate by the rotating shaft. The receiving coil for wireless power supply is installed in the stacked beam frame; the transmitting coil for wireless power supply is installed in the gate slot; the gate slot is made of concrete poured during the initial construction.

[0023] Specifically, such as Figure 4 As shown, the fault detection method includes the following steps: Step S1: Under underwater operating conditions, obtain the opening rotation time required for each flap valve to go from fully closed to fully open, and the closing rotation time required to go from fully open to fully closed.

[0024] Preferably, in practical applications, the time required for opening and closing the flap gate varies depending on the water depth. Deep flap gates need to slow down to avoid instantaneous pressure damage to the sealing structure in order to avoid hydrostatic pressure under high pressure.

[0025] Step S2: Based on a preset natural duration, obtain the water temperature, water flow velocity, and water purity of each layer of the water body at the depth of the tongue valve in the water area where the stratified water intake gate is located.

[0026] Preferably, the water temperature can be measured by installing a temperature measuring device on a concrete wall at the water depth corresponding to each valve, allowing the water temperature at different depths to be directly obtained. Similarly, a flow meter can be installed to measure the water flow velocity. The water purity can be measured by measuring the water conductivity. The lower the conductivity, the fewer impurities in the water and the higher the water purity. A conductivity meter or a TDS meter with the same installation method as the aforementioned measuring devices can be used for direct measurement. The TDS meter can also measure the total dissolved solids and can also be used as an evaluation standard for water purity.

[0027] Step S3: Obtain the number of times each lingual valve is used within each preset natural duration.

[0028] Preferably, the preset natural duration can be set to one natural time to prevent excessively long time intervals from causing large changes in measurement parameters.

[0029] Step S4: Based on the same preset natural duration, analyze the first relationship between the rotation duration and water temperature, water flow rate, water purity, water depth, and number of uses.

[0030] Step S5: Based on the same preset natural duration, analyze the second relationship between the shutdown rotation duration and water temperature, water flow rate, water purity, water depth, and number of uses.

[0031] Preferably, both the first and second cross-relationships can be analyzed by lasso linear regression.

[0032] Step S6: Integrate the first coefficients of all first mutual relations into a first matrix, and integrate the second coefficients of all second mutual relations into a second matrix.

[0033] Preferably, the first coefficient and the second coefficient are both linear regression coefficients of their respective linear regression relationships. Extracting their respective regression coefficients and placing them according to the positions of the equation system forms a matrix.

[0034] Step S7: Obtain the matrix similarity between the first matrix and the second matrix, and when the matrix similarity is less than a first preset ratio, obtain the element similarity at the same element positions in the two matrices.

[0035] Preferably, the matrix similarity calculation method can be one of Euclidean distance, cosine similarity, Pearson correlation coefficient, or Frobenius norm. Since the two matrices in this embodiment have the same dimension, Euclidean distance can be used to directly calculate the square root of the sum of squares of the differences between corresponding elements, and the result is the matrix similarity. The similarity of elements at the same position can be obtained by obtaining the average value of all elements at the same position in all matrices, obtaining the Euclidean distance between each element and the average value based on the current matrix, adding 1 to the Euclidean distance of the current element, and then taking the reciprocal to obtain the data similarity of the current element.

[0036] Step S8: When the element similarity at one of the same element positions approaches 1, it is determined that the corresponding tongue flap door is in a non-working state, that is, the wireless power supply function of the corresponding tongue flap door is not enabled.

[0037] Preferably, when the element similarity at one of the same element positions approaches 1, it can be understood that the tongue valve at that position has no signs of movement and is in a stagnant state throughout the process. There is no movement process to interfere with the coefficients, so it will approach 1. In order to prevent the "approaching 1" from being unclear, it can be set to an accurate number that approaches 1, such as an element similarity of more than 0.9 or 0.95.

[0038] Step S9: When the element similarity at one of the same element positions is less than the second preset ratio, it is determined that the corresponding tongue flap door has a mechanical failure.

[0039] Preferably, the second preset ratio can be understood as the sensitivity of the fault determination, and can generally be set to 50%.

[0040] Further, in step S8, when the element similarity at one of the same element positions approaches 1, it is determined that the corresponding tongue flap gate is in a non-working state, that is, the wireless power supply function of the corresponding tongue flap gate is not enabled. After that, the following steps are also included: Step S10: Define the tongue valve gate where the wireless power supply function is not enabled as an abnormal tongue valve gate.

[0041] Step S20: Obtain the voltage and current values ​​of all abnormal tongue flap gates.

[0042] Preferably, each layer of the flap door is modularly installed, with no physical power transmission lines exposed to the outside. Voltage and current values ​​can be measured on-site or after the flap door is removed from the door slot.

[0043] Step S30: If the voltage and current values ​​of the current abnormal valve gate are both zero, then the current abnormal valve gate is determined to be in a circuit open state.

[0044] Step S40: If the voltage value of the current abnormal valve gate is zero and the current value is infinite, then the current abnormal valve gate is determined to be in a short-circuit state.

[0045] Preferably, a short circuit is considered to occur when the voltage of a general electricity meter is zero, or when the current is full or reaches a high value.

[0046] Preferably, a lack of power supply caused by a mechanical failure of the wireless power supply function itself is considered an open circuit, such as a coil falling off or a break in the remote power supply line.

[0047] Further, in step S40, if the voltage value of the current abnormal valve gate is zero and the current value is infinite, then it is determined that the current abnormal valve gate is in a short-circuit state. Afterwards, the following steps are also included: Step S100: Obtain the location information and device number of each abnormal tongue flap.

[0048] Step S200: If the current abnormal tongue valve is in a circuit open state, then generate a device open circuit signal.

[0049] Step S300: Pack the device disconnection signal, the location information corresponding to the current abnormal tongue flap door, and the device number into a first abnormal data packet.

[0050] Step S400: Send the first abnormal data packet to the external monitoring terminal.

[0051] Step S500: If the current abnormal tongue valve is in a short-circuit state, a device short-circuit signal is generated.

[0052] Step S600: Pack the device short-circuit signal, the location information corresponding to the current abnormal tongue flap, and the device number into a second abnormal data packet.

[0053] Step S700: Send the second abnormal data packet to the external monitoring terminal.

[0054] Further, in step S9, when the element similarity at one of the same element positions is less than a second preset ratio, it is determined that the corresponding tongue flap has a mechanical fault. Afterwards, the following steps are also included: Step S1000: Define the tongue valve that causes mechanical failure as an abnormal tongue valve.

[0055] Step S2000: Obtain vibration data of the current abnormal tongue flap during the opening or closing process.

[0056] Step S3000: Abnormal vibration data are filtered out from all vibration data using a classification algorithm.

[0057] Preferably, abnormal vibration data can be filtered using Bayesian classification.

[0058] Step S4000: Obtain the abnormal tongue valve door that appears most frequently in the abnormal vibration data and mark it as the faulty tongue valve door.

[0059] Further, in step S30, abnormal vibration data are filtered out from all vibration data using a classification algorithm, including: Step S30001: Obtain the signal components of each vibration data point through empirical mode decomposition.

[0060] Preferably, the Empirical Mode Decomposition (EMD) algorithm is based on the concepts of instantaneous frequency and intrinsic mode functions (IMFs). EMD can decompose complex signals into several IMF components, each IMF representing a local feature of the signal. It decomposes signals based on the time-scale characteristics of the data itself, without requiring any pre-defined basis functions, thus exhibiting adaptability. The advantage of EMD lies in that it does not use any predefined functions as a basis, but rather adaptively generates intrinsic mode functions based on the signal being analyzed. It can be used to analyze nonlinear and non-stationary signal sequences, possessing a high signal-to-noise ratio and good time-frequency focusing.

[0061] Step S30002: Classify all signal components of the same lingual valve gate using a classification algorithm to obtain at least two signal features, each signal feature including at least one signal component.

[0062] Preferably, this embodiment employs Bayesian classification. Bayesian classification is a non-regular classification method. It trains a subset of already classified samples to learn and inductively derive a classification function (prediction of discrete variables is called classification, and classification of continuous variables is called regression). The trained classifier is then used to classify unclassified data. Among different classification algorithms, Naive Bayes is a simple one, and its performance is better than neural network classification and decision tree classification algorithms, especially when the amount of data to be classified is very large. Bayesian classification methods have high accuracy compared to other classification algorithms. The design intention of this embodiment, which prefers Naive Bayes over neural network classification, is to achieve higher accuracy.

[0063] Preferably, the signal characteristics are mainly divided into three time-domain characteristics: short-time energy, zero-crossing rate, and empirical permutation entropy; and six frequency-domain characteristics: spectral centroid, spectral extension, spectral entropy, spectral flux, spectral roll-off point, and Mel frequency cepstral coefficients.

[0064] Preferably, this embodiment can select one or two of the above features. If too many features are selected, it is easy to cause a sudden increase in the amount of calculation, which may lead to computer lag or unresponsiveness in actual application.

[0065] Preferably, this embodiment can use one or two of the following: short-time energy, zero-crossing rate, spectral centroid, and spectral flux, to reduce the difficulty of detection.

[0066] Step S30003: Based on all tongue flap gates, delete all identical signal features and mark the remaining signal features as abnormal features.

[0067] Step S30004: Mark the vibration data corresponding to each of the abnormal features as abnormal vibration data.

[0068] Further, step S4, based on the same preset natural duration, analyzes the first relationship between the rotation duration and water temperature, water flow rate, water purity, water depth, and number of uses, specifically including the following steps: Step S41: Define the start-up rotation duration of all preset natural durations as known dependent variables, and define all water temperatures, all water flow rates, all water purity, all water depths, and all usage counts as known independent variables.

[0069] Step S42: Define a linear regression equation for the known dependent variable and known independent variable with the same preset natural duration using multiple linear regression.

[0070] Step S43: Integrate all the linear regression equations with preset natural durations into a system of linear regression equations.

[0071] Step S44: Solve for all unknown linear regression coefficients of the linear regression equation system using the least squares method.

[0072] Step S45: Substitute all the known linear regression coefficients obtained from the solution into the linear regression equation system to obtain the first cross-relationship.

[0073] Further, step S5, based on the same preset natural duration, analyzes the second relationship between the shutdown rotation duration and water temperature, water flow rate, water purity, water depth, and number of uses, specifically including the following steps: Step S51 replaces the rotation duration in step S41 with the rotation duration being turned off.

[0074] Step S52: Repeat steps S41 to S45 with the rotation duration as the execution subject to obtain the second mutual relationship.

[0075] This embodiment obtains the opening rotation time required for each flap gate to go from fully closed to fully open, and the closing rotation time required to go from fully open to fully closed, under underwater operating conditions. It also obtains the water temperature, flow velocity, and purity at each depth of the flap gate within the stratified water intake area based on a preset natural time. Furthermore, it obtains the number of times each flap gate is used within each preset natural time. Finally, it analyzes the first correlation between the opening rotation time and water temperature, flow velocity, water purity, water depth, and number of uses based on the same preset natural time. Finally, it analyzes the correlation between the closing rotation time and water temperature based on the same preset natural time. The system identifies the following relationships: water flow velocity, water purity, water depth, and number of uses; integrates the first coefficients of all first relationships into a first matrix and the second coefficients of all second relationships into a second matrix; obtains the matrix similarity between the first and second matrices, and when the matrix similarity is less than a first preset ratio, obtains the element similarity at the same element positions in the two matrices; when the element similarity at one of the same element positions approaches 1, it is determined that the corresponding flap door is in a non-working state, that is, the wireless power supply function of the corresponding flap door is not enabled; when the element similarity at one of the same element positions is less than a second preset ratio, it is determined that the corresponding flap door has a mechanical failure. This embodiment utilizes the characteristic that the opening and closing of the flap gate is a repetitive and regular mechanical reciprocating motion. Because water pressure causes each flap gate at different depths to actively or passively change its opening and closing time—that is, the deeper the water and the higher the water pressure, the longer the opening and closing time of the flap gate—(under high pressure, the gate body needs to overcome greater hydrostatic pressure; the water pressure acts directly on the gate panel, causing the hydraulic system or motor to output more power to start; and when closing the gate in a high-pressure area, the impact force of the water flow on the gate body is stronger, requiring deceleration to avoid instantaneous pressure damage to the sealing structure). Simultaneously, because the flap gate experiences slow, cumulative wear over time during the reciprocating process, this embodiment utilizes the characteristic that linear regression coefficients can reflect the correlation and importance of independent and dependent variables. By analyzing the correlation between the opening and closing actions of each flap gate at different water depths and the surrounding environment, abnormal coefficients are identified, thereby identifying faults. This embodiment is not only closely related to the working environment of the flap gate but also fills the gap in fault detection for stratified water intake equipment.

[0076] like Figure 5 As shown, this embodiment provides an example of a fault detection system for a wirelessly powered tongue flap door. In this embodiment, the fault detection system is applied to the fault detection method described in the above embodiment.

[0077] Specifically, the fault detection system includes, in sequence, a valve opening and closing time acquisition module 1, a valve environmental parameter acquisition module 2, a valve usage count acquisition module 3, a valve opening process analysis module 4, a valve closing process analysis module 5, a cross-relationship matrix integration module 6, a matrix and element similarity acquisition module 7, a valve wireless power supply function judgment module 8, and a valve mechanical fault judgment module 9.

[0078] The system includes: a flap gate opening / closing time acquisition module 1, which acquires the opening rotation time required for each flap gate to go from fully closed to fully open, and the closing rotation time required to go from fully open to fully closed, under underwater operating conditions; a flap gate environmental parameter acquisition module 2, which acquires the water temperature, flow velocity, and purity of each flap gate at each depth in the water area where the stratified water intake gate is located, based on a preset natural time; a flap gate usage count acquisition module 3, which acquires the number of times each flap gate is used within each preset natural time; a flap gate opening process analysis module 4, which analyzes the first correlation between the opening rotation time and the water temperature, flow velocity, water purity, water depth, and number of uses, based on the same preset natural time; and a flap gate closing process analysis module 5, which analyzes the closing rotation time, based on the same preset natural time. The system includes: a first mutual relationship with water temperature, water flow rate, water purity, water depth, and number of uses; a second mutual relationship matrix integration module 6, which integrates the first coefficients of all first mutual relationships into a first matrix and the second coefficients of all second mutual relationships into a second matrix; a matrix and element similarity acquisition module 7, which acquires the matrix similarity between the first and second matrices and, when the matrix similarity is less than a first preset ratio, acquires the element similarity at the same element positions in the two matrices; a tongue-valve door wireless power supply function judgment module 8, which determines that the corresponding tongue-valve door is in a non-working state, i.e., the wireless power supply function of the corresponding tongue-valve door is not enabled, when the element similarity at one of the same element positions approaches 1; and a tongue-valve door mechanical fault judgment module 9, which determines that the corresponding tongue-valve door has a mechanical fault when the element similarity at one of the same element positions is less than a second preset ratio.

[0079] Furthermore, the fault detection system also includes, in sequence, an abnormal tongue valve gate definition module based on the wireless power supply function not being enabled, an abnormal tongue valve gate voltage and current acquisition module, a circuit open circuit state definition module, and a circuit short circuit state definition module, which are electrically connected in sequence; the abnormal tongue valve gate definition module based on the wireless power supply function not being enabled is electrically connected to the tongue valve gate wireless power supply function judgment module 8.

[0080] Among them, the abnormal flap gate definition module based on the wireless power supply function not being enabled is used to define the flap gate with the wireless power supply function not being enabled as an abnormal flap gate; the abnormal flap gate voltage and current acquisition module is used to acquire the voltage and current values ​​of all abnormal flap gates; the circuit open state definition module is used to determine that the current abnormal flap gate is in a circuit open state if both the voltage and current values ​​of the current abnormal flap gate are zero; the circuit short circuit state definition module is used to determine that the current abnormal flap gate is in a circuit short circuit state if the voltage value of the current abnormal flap gate is zero and the current value is infinite.

[0081] Furthermore, the fault detection system also includes, in sequence, an abnormal flap door position number acquisition module, an equipment open circuit signal generation module, an open circuit abnormal data packet packaging module, an open circuit abnormal data packet sending module, an equipment short circuit signal generation module, a short circuit abnormal data packet packaging module, and a short circuit abnormal data packet sending module; the abnormal flap door position number acquisition module is electrically connected to the circuit short circuit state definition module.

[0082] The system includes the following modules: An abnormal flap gate location number acquisition module, which acquires the location information and device number of each abnormal flap gate; a device open circuit signal generation module, which generates a device open circuit signal if the current abnormal flap gate is in an open circuit state; an open circuit abnormal data packet packaging module, which packages the device open circuit signal, the location information corresponding to the current abnormal flap gate, and the device number into a first abnormal data packet; an open circuit abnormal data packet sending module, which sends the first abnormal data packet to an external monitoring terminal; a device short circuit signal generation module, which generates a device short circuit signal if the current abnormal flap gate is in a short circuit state; a short circuit abnormal data packet packaging module, which packages the device short circuit signal, the location information corresponding to the current abnormal flap gate, and the device number into a second abnormal data packet; and a short circuit abnormal data packet sending module, which sends the second abnormal data packet to an external monitoring terminal.

[0083] Furthermore, the fault detection system also includes a mechanical fault-based abnormal tongue valve definition module, a vibration data acquisition module, an abnormal vibration data filtering module, and a faulty tongue valve marking module, which are connected in sequence and electrically. The mechanical fault-based abnormal tongue valve definition module is electrically connected to the tongue valve mechanical fault judgment module 9.

[0084] Among them, the abnormal tongue valve definition module based on mechanical fault is used to define the tongue valve that causes mechanical fault as an abnormal tongue valve; the vibration data acquisition module is used to acquire the vibration data of the current abnormal tongue valve during the opening or closing process; the abnormal vibration data filtering module is used to filter out the abnormal vibration data in all vibration data through a classification algorithm; and the faulty tongue valve marking module is used to acquire the abnormal tongue valve with the most occurrences of abnormal vibration data and mark it as a faulty tongue valve.

[0085] Furthermore, the abnormal vibration data filtering module specifically includes a first abnormal vibration data filtering unit, a second abnormal vibration data filtering unit, a third abnormal vibration data filtering unit, and a fourth abnormal vibration data filtering unit that are electrically connected in sequence; the first abnormal vibration data filtering unit is electrically connected to the vibration data acquisition module, and the fourth abnormal vibration data filtering unit is electrically connected to the faulty tongue flap marking module.

[0086] The first abnormal vibration data filtering unit is used to obtain the signal components of each vibration data through empirical mode decomposition; the second abnormal vibration data filtering unit is used to classify all signal components of the same tongue valve through a classification algorithm to obtain at least two signal features, each signal feature including at least one signal component; the third abnormal vibration data filtering unit is used to delete all identical signal features based on all tongue valves and mark the retained signal features as abnormal features; the fourth abnormal vibration data filtering unit is used to mark the vibration data corresponding to each abnormal feature as abnormal vibration data.

[0087] Furthermore, the tongue valve opening process analysis module 4 specifically includes a first tongue valve opening process analysis unit, a second tongue valve opening process analysis unit, a third tongue valve opening process analysis unit, a fourth tongue valve opening process analysis unit, and a fifth tongue valve opening process analysis unit; the first tongue valve opening process analysis unit is electrically connected to the tongue valve usage count acquisition module 3, and the fifth tongue valve opening process analysis unit is electrically connected to the tongue valve closing process analysis module 5.

[0088] The system comprises five modules: the first module for analyzing the opening process of the flap gate, which defines the opening and rotation duration of all preset natural durations as known dependent variables, and all water temperatures, flow rates, purity levels, depths, and usage frequency as known independent variables; the second module for analyzing the opening process of the flap gate, which defines the known dependent and independent variables of the same preset natural duration as a linear regression equation using multiple linear regression; the third module for analyzing the opening process of the flap gate, which integrates all the linear regression equations of the preset natural durations into a system of linear regression equations; the fourth module for analyzing the opening process of the flap gate, which solves for all unknown linear regression coefficients in the system of linear regression equations using the least squares method; and the fifth module for analyzing the opening process of the flap gate, which substitutes all the known linear regression coefficients obtained into the system of linear regression equations to obtain the first mutual relationship.

[0089] Furthermore, the tongue valve closing process analysis module 5 specifically includes a first tongue valve closing process analysis unit and a second tongue valve closing process analysis unit that are electrically connected in sequence; the first tongue valve closing process analysis unit is electrically connected to the fifth tongue valve opening process analysis unit, and the second tongue valve closing process analysis unit is electrically connected to the mutual relationship matrix integration module 6.

[0090] The first tongue valve closing process analysis unit is used to replace the opening rotation duration in the first tongue valve opening process analysis unit with the closing rotation duration as the execution subject; the second tongue valve closing process analysis unit is used to repeatedly execute the first tongue valve opening process analysis unit to the fifth tongue valve opening process analysis unit with the closing rotation duration as the execution subject to obtain the second mutual relationship.

[0091] This embodiment obtains the opening rotation time required for each flap gate to go from fully closed to fully open, and the closing rotation time required to go from fully open to fully closed, under underwater operating conditions. It also obtains the water temperature, flow velocity, and purity at each depth of the flap gate within the stratified water intake area based on a preset natural time. Furthermore, it obtains the number of times each flap gate is used within each preset natural time. Finally, it analyzes the first correlation between the opening rotation time and water temperature, flow velocity, water purity, water depth, and number of uses based on the same preset natural time. Finally, it analyzes the correlation between the closing rotation time and water temperature based on the same preset natural time. The system identifies the following relationships: water flow velocity, water purity, water depth, and number of uses; integrates the first coefficients of all first relationships into a first matrix and the second coefficients of all second relationships into a second matrix; obtains the matrix similarity between the first and second matrices, and when the matrix similarity is less than a first preset ratio, obtains the element similarity at the same element positions in the two matrices; when the element similarity at one of the same element positions approaches 1, it is determined that the corresponding flap door is in a non-working state, that is, the wireless power supply function of the corresponding flap door is not enabled; when the element similarity at one of the same element positions is less than a second preset ratio, it is determined that the corresponding flap door has a mechanical failure. This embodiment utilizes the characteristic that the opening and closing of the flap gate is a repetitive and regular mechanical reciprocating motion. Because water pressure causes each flap gate at different depths to actively or passively change its opening and closing time—that is, the deeper the water and the higher the water pressure, the longer the opening and closing time of the flap gate—(under high pressure, the gate body needs to overcome greater hydrostatic pressure; the water pressure acts directly on the gate panel, causing the hydraulic system or motor to output more power to start; and when closing the gate in a high-pressure area, the impact force of the water flow on the gate body is stronger, requiring deceleration to avoid instantaneous pressure damage to the sealing structure). Simultaneously, because the flap gate experiences slow, cumulative wear over time during the reciprocating process, this embodiment utilizes the characteristic that linear regression coefficients can reflect the correlation and importance of independent and dependent variables. By analyzing the correlation between the opening and closing actions of each flap gate at different water depths and the surrounding environment, abnormal coefficients are identified, thereby identifying faults. This embodiment is not only closely related to the working environment of the flap gate but also fills the gap in fault detection for stratified water intake equipment.

[0092] like Figure 6As shown, this embodiment provides an embodiment of an electronic device. In this embodiment, the electronic device 10 includes a processor 101 and a memory 102 coupled to the processor 101.

[0093] The memory 102 stores program instructions for implementing the fault detection method of the wirelessly powered tongue flap door in any of the above embodiments.

[0094] The processor 101 is used to execute program instructions stored in the memory 102 to perform fault detection of the wirelessly powered tongue flap door.

[0095] The processor 101 can also be referred to as a CPU (Central Processing Unit). The processor 101 may be an integrated circuit chip with data processing capabilities. The processor 101 can also be a general-purpose processor, a digital data processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor can be a microprocessor or any conventional processor.

[0096] Furthermore, Figure 7 This is a schematic diagram of the structure of a storage medium according to an embodiment of this application. The storage medium 11 of this embodiment stores program instructions 111 capable of implementing all the above methods. These program instructions 111 can be stored in the storage medium in the form of a software product, including 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.

[0097] In the several embodiments provided in this application, it should be understood that the disclosed systems, methods, and approaches can be implemented in other ways. For example, the system 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 mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0098] 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.

[0099] The specific embodiments of this application 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 this application 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 fault detection method for a wirelessly powered flap gate, wherein the wirelessly powered flap gate is applied to a tiered water intake working gate, the tiered water intake working gate comprising at least one set of flap gates with different height differences, all flap gates being engaged in door slots within a dam body and used to rotate according to different water intake needs to achieve their respective opening or closing purposes, each flap gate having a wireless power supply function, characterized in that... The fault detection method includes: Step S1: Under underwater operating conditions, obtain the opening rotation time required for each flap valve to go from fully closed to fully open, and the closing rotation time required to go from fully open to fully closed. Step S2: Based on a preset natural time duration, obtain the water temperature, water flow rate, and water purity of each layer of the water body at the depth of the tongue valve in the water area where the stratified water intake gate is located; Step S3: Obtain the number of times each lingual valve is used within each preset natural time period; Step S4: Based on the same preset natural duration, analyze the first relationship between the opening and rotation duration and the water temperature, the water flow rate, the water purity, the water depth, and the number of uses. Step S5: Based on the same preset natural duration, analyze the second relationship between the shutdown rotation duration and the water temperature, the water flow rate, the water purity, the water depth, and the number of uses. Step S6: Integrate the first coefficients of all first mutual relations into a first matrix, and integrate the second coefficients of all second mutual relations into a second matrix; Step S7: Obtain the matrix similarity between the first matrix and the second matrix, and when the matrix similarity is less than a first preset ratio, obtain the element similarity at the same element positions of the two matrices; Step S8: When the element similarity at one of the same element positions approaches 1, it is determined that the corresponding tongue flap door is in a non-working state, that is, the wireless power supply function of the corresponding tongue flap door is not enabled. Step S9: When the element similarity at one of the same element positions is less than the second preset ratio, it is determined that the corresponding tongue flap door has a mechanical failure.

2. The fault detection method according to claim 1, characterized in that, Step S8: When the element similarity at one location of the same element approaches 1, it is determined that the corresponding flap gate is in a non-working state, that is, the wireless power supply function of the corresponding flap gate is not enabled. Afterwards, the following steps are taken: Step S10: Define the tongue valve gate where the wireless power supply function is not enabled as an abnormal tongue valve gate; Step S20: Obtain the voltage and current values ​​of all abnormal tongue flap gates; Step S30: If the voltage and current values ​​of the current abnormal valve gate are both zero, then the current abnormal valve gate is determined to be in a circuit open state. Step S40: If the voltage value of the current abnormal valve gate is zero and the current value is infinite, then the current abnormal valve gate is determined to be in a short-circuit state.

3. The fault detection method according to claim 2, characterized in that, Step S40: If the voltage value of the current abnormal valve gate is zero and the current value is infinite, then the current abnormal valve gate is determined to be in a short-circuit state. Afterwards, the following steps are taken: Step S100: Obtain the location information and device number of each abnormal tongue flap. Step S200: If the current abnormal tongue valve is in a circuit open state, then generate a device open circuit signal; Step S300: Pack the device disconnection signal, the location information corresponding to the current abnormal tongue flap, and the device number into a first abnormal data packet; Step S400: Send the first abnormal data packet to the external monitoring terminal; Step S500: If the current abnormal tongue valve is in a short-circuit state, then generate a device short-circuit signal; Step S600: Pack the device short-circuit signal, the location information corresponding to the current abnormal tongue flap, and the device number into a second abnormal data packet; Step S700: Send the second abnormal data packet to the external monitoring terminal.

4. The fault detection method according to claim 1, characterized in that, Step S9: When the element similarity at one of the same element positions is less than a second preset ratio, it is determined that the corresponding tongue flap has a mechanical fault. Afterwards, the process includes: Step S1000: Define the tongue valve that causes mechanical failure as an abnormal tongue valve. Step S2000: Obtain vibration data of the current abnormal tongue flap during the opening or closing process; Step S3000: Abnormal vibration data are filtered out from all vibration data using a classification algorithm; Step S4000: Obtain the abnormal tongue valve door that appears most frequently in the abnormal vibration data and mark it as the faulty tongue valve door.

5. The fault detection method according to claim 4, characterized in that, Step S30: Abnormal vibration data are filtered out from all vibration data using a classification algorithm, including: Step S30001: Obtain the signal components of each vibration data point through empirical mode decomposition. Step S30002: Classify all signal components of the same lingual valve using the classification algorithm to obtain at least two signal features, each signal feature including at least one signal component; Step S30003: Based on all tongue flaps, delete all identical signal features and mark the remaining signal features as abnormal features; Step S30004: Mark the vibration data corresponding to each of the abnormal features as abnormal vibration data.

6. The fault detection method according to claim 1, characterized in that, Step S4, based on the same preset natural duration, analyze the first relationship between the opening rotation duration and the water temperature, the water flow rate, the water purity, the water depth, and the number of uses, including: Step S41: Define the start-up and rotation duration of all preset natural durations as known dependent variables, and define all water temperatures, all water flow rates, all water purity, all water depths, and all usage times as known independent variables. Step S42: Define a linear regression equation for the known dependent variable and known independent variable with the same preset natural duration using multiple linear regression. Step S43: Integrate all the linear regression equations with preset natural durations into a system of linear regression equations; Step S44: Solve for all unknown linear regression coefficients of the linear regression equation system using the least squares method; Step S45: Substitute all the known linear regression coefficients obtained from the solution into the linear regression equation system to obtain the first mutual relationship.

7. The fault detection method according to claim 6, characterized in that, Step S5, based on the same preset natural duration, analyze the second interrelationship between the shutdown rotation duration and the water temperature, the water flow rate, the water purity, the water depth, and the number of uses, including: Step S51: The rotation-on duration in step S41 is replaced with the rotation-off duration as the execution subject. Step S52: Repeat steps S41 to S45 with the closed rotation duration as the execution subject to obtain the second mutual relationship.

8. A fault detection system for a wirelessly powered flap door, wherein the fault detection system is applied to the fault detection method as described in any one of claims 1 to 7, characterized in that, The fault detection system includes: The flap valve opening and closing time acquisition module is used to acquire, under underwater operating conditions, the opening rotation time required for each flap valve to go from fully closed to fully open each time, and the closing rotation time required to go from fully open to fully closed. The tongue-valve gate environmental parameter acquisition module is used to acquire the water temperature, water flow velocity, and water purity of each layer of the water body at the depth of the tongue-valve gate in the water area where the layered water intake gate is located, based on a preset natural time. The module for obtaining the number of times each lingual valve is used is used to obtain the number of times each lingual valve is used within each preset natural time period. The tongue flap opening process analysis module is used to analyze the first relationship between the opening rotation time and the water temperature, the water flow rate, the water purity, the water depth, and the number of times it is used, based on the same preset natural time. The tongue flap closure process analysis module is used to analyze the second relationship between the closure rotation time and the water temperature, the water flow rate, the water purity, the water depth, and the number of uses, based on the same preset natural time. The cross-relationship matrix integration module is used to integrate the first coefficients of all first cross-relationships into a first matrix and the second coefficients of all second cross-relationships into a second matrix. The matrix and element similarity acquisition module is used to acquire the matrix similarity between the first matrix and the second matrix, and when the matrix similarity is less than a first preset ratio, to acquire the element similarity at the same element positions of the two matrices; The tongue flap gate wireless power supply function judgment module is used to determine that the corresponding tongue flap gate is not working when the element similarity at one of the same element positions approaches 1, that is, the wireless power supply function of the corresponding tongue flap gate is not enabled. The tongue flap gate mechanical fault judgment module is used to determine that the corresponding tongue flap gate has a mechanical fault when the element similarity at one of the same element positions is less than a second preset ratio.

9. An electronic device, characterized in that, The method includes a processor and a memory coupled to the processor, the memory storing program instructions executable by the processor; when the processor executes the program instructions stored in the memory, it implements the fault detection method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium stores program instructions, which, when executed by a processor, enable the fault detection method as described in any one of claims 1 to 7.

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