An intelligent monitoring method for predicting and warning failures of equipment in a hydropower plant

Abstract

This application proposes an intelligent monitoring method for fault prediction and early warning of hydropower plant equipment, relating to the field of hydropower plant fault monitoring technology. The method includes: collecting operating parameters of hydropower plant equipment through sensors; extracting feature quantities reflecting the equipment status from the collected operating parameters, wherein the feature quantities include time-domain features and frequency-domain features; constructing an equipment fault prediction model based on the operating parameters and feature quantities; inputting the real-time extracted feature quantities into the trained equipment fault prediction model to obtain fault prediction results; and, when a fault is predicted, activating a multimodal early warning mechanism and searching for a matching solution in an emergency response solution library according to the predicted fault type. This invention, employing the above solution, achieves fault monitoring and early warning for hydropower plants.

Description

Technical Field

[0001] This application relates to the field of hydropower plant fault monitoring technology, and in particular to intelligent monitoring methods and systems for predicting and warning of equipment faults in hydropower plants. Background Technology

[0002] In the operation and management of hydropower plants, the safe and stable operation of equipment is a key factor in ensuring a continuous power supply. Traditional methods of monitoring hydropower plant equipment mainly rely on manual inspections and relatively simple automated monitoring systems.

[0003] Manual inspections typically involve checking equipment at fixed time intervals, a method with several drawbacks. Firstly, due to limited human attention and energy, oversights are common during extended inspections, making it difficult to maintain high concentration and detect early signs of subtle equipment malfunctions. For example, minor internal vibrations or slow temperature increases are difficult to detect during a short inspection period. Secondly, manual inspections are inefficient, requiring significant manpower and failing to provide real-time continuous monitoring, making it difficult to promptly detect sudden equipment failures. Summary of the Invention

[0004] This application aims to at least partially address one of the technical problems in the related art.

[0005] Therefore, the first objective of this application is to propose an intelligent monitoring method for predicting and warning of equipment failures in hydropower plants. This method solves the technical problems of low efficiency, inability to monitor continuously in real time, and difficulty in capturing sudden failures in existing methods. It can provide real-time fault warnings and perform automatic emergency handling when a failure occurs, thereby ensuring the economic benefits of hydropower plants and the stability of power supply.

[0006] The second objective of this application is to propose an intelligent monitoring system for predicting and warning of equipment failures in hydropower plants.

[0007] To achieve the above objectives, the first aspect of this application proposes an intelligent monitoring method for predicting and warning of equipment failures in hydropower plants, comprising: collecting operating parameters of hydropower plant equipment through sensors; extracting feature quantities reflecting the equipment status from the collected operating parameters, wherein the feature quantities include time-domain features and frequency-domain features; constructing an equipment failure prediction model based on the operating parameters and feature quantities; inputting the real-time extracted feature quantities into the trained equipment failure prediction model to obtain failure prediction results; and when a failure is predicted, activating a multimodal early warning mechanism and searching for a matching solution in an emergency response solution library according to the predicted failure type.

[0008] To achieve the above objectives, a second aspect of the present invention proposes an intelligent monitoring system for predicting and warning of equipment failures in hydropower plants, comprising: a data acquisition module for acquiring operating parameters of hydropower plant equipment through sensors; a feature extraction module for extracting features reflecting the equipment status from the acquired operating parameters, wherein the features include time-domain features and frequency-domain features; a model building module for constructing an equipment failure prediction model based on the operating parameters and features; a failure prediction module for inputting the real-time extracted features into the trained equipment failure prediction model to obtain a failure prediction result; and a failure handling module for activating a multimodal early warning mechanism when a failure is predicted, and searching for a matching solution in an emergency handling solution library according to the predicted failure type.

[0009] The intelligent monitoring method and system for predicting and warning of equipment failures in hydropower plants according to the embodiments of this application provide real-time fault warnings through equipment fault prediction models and perform automatic emergency handling when a fault occurs. This enables the equipment to take measures quickly and automatically when fault symptoms appear, preventing the fault from worsening, reducing the need for manual intervention, reducing reliance on the professional skills of operators, significantly improving the fault tolerance of the equipment, reducing equipment downtime and maintenance costs, and improving the economic benefits of hydropower plants and the reliability of power supply.

[0010] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0011] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0012] Figure 1 This is a flowchart illustrating an intelligent monitoring method for predicting and warning of equipment failures in a hydropower plant, as provided in Embodiment 1 of this application. Figure 2 This is a schematic diagram of the structure of an intelligent monitoring system for predicting and warning equipment failures in a hydropower plant, provided as an embodiment of this application. Detailed Implementation

[0013] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0014] The following describes, with reference to the accompanying drawings, an intelligent monitoring method and system for predicting and warning of equipment failures in hydropower plants, according to embodiments of this application.

[0015] Figure 1 This is a flowchart illustrating an intelligent monitoring method for predicting and warning of equipment failures in a hydropower plant, as provided in Embodiment 1 of this application.

[0016] like Figure 1 As shown, the intelligent monitoring method for equipment fault prediction and early warning in this hydropower plant includes the following steps: Step 101: Collect operating parameters of hydropower plant equipment using sensors; In this embodiment, multiple monitoring sensors are deployed in the hydropower plant to collect the operating parameters of the equipment in real time and store them according to time series. Specifically, the equipment in a hydropower plant includes turbines, generators, main transformers, and pumps / valvees, and the corresponding operating parameters and sensor types are as follows: Water turbine: Vibration acceleration and shaft vibration are collected by vibration sensors, volute pressure and inlet pressure are collected by pressure sensors, bearing temperature is collected by temperature sensors, rotational speed is collected by rotational speed, and guide vane opening is collected by displacement sensors. Generator: Temperature sensors collect stator temperature and winding temperature, current sensors collect rotor current and motor current, vibration sensors collect shaft vibration, voltage sensors collect output voltage, frequency sensors collect output frequency, and insulation resistance testers collect winding insulation resistance. Main transformer: oil temperature and winding temperature are collected by temperature sensors, the concentration of gases such as H2 and CH4 in the oil is collected by an online oil chromatograph, and the insulation resistance of the bushing is collected by an insulation resistance tester. Water pumps / valves: Inlet and outlet pressures are collected by pressure sensors, outlet flow rates are collected by flow sensors, motor currents are collected by current sensors, valve disc vibrations and impeller vibrations are collected by vibration sensors, and seal leakage is collected by leakage detectors. In this embodiment, while storing the operating parameters, the collected operating parameters are preprocessed. Specifically, for the detected parameters, outlier detection can adopt the 3σ criterion, that is, for a certain parameter data sequence, if a data point exceeds the range of mean ± 3 times the standard deviation, it is regarded as an outlier and corrected or removed; noise elimination can adopt the Kalman filtering algorithm to filter the noisy data and improve the accuracy of the data; data normalization can adopt the linear normalization method to map the data to the [0,1] interval.

[0017] Step 102: Extract feature quantities reflecting the equipment status from the collected operating parameters, wherein the feature quantities include time domain features and frequency domain features; In this embodiment, the time-domain features are: mean, peak value, root mean square, waveform factor, and peak factor. Frequency domain characteristics: After performing a Fast Fourier Transform (FFT) on the time-domain signal, calculate the spectral center, band energy, and characteristic frequency amplitude.

[0018] In this embodiment, the features extracted from different devices are: vibration and current signals of the turbine / generator / pump, and the oil chromatographic concentration change rate of the main transformer; Step 103: Based on operating parameters and characteristic quantities, construct a device fault prediction model; In this embodiment, machine learning algorithms such as support vector machines, neural networks, or random forests are used to construct a device fault prediction model. For example, when using a support vector machine to construct the model, a certain number of normal samples and various fault samples are selected to train the model, finding the optimal classification hyperplane so that samples of different categories can be separated by the maximum margin, thereby achieving accurate prediction of device faults. Specifically, the device fault prediction model includes: Input layer: Fuse feature vectors from multiple devices and add device type encoding (One-Hot vector). Feature processing layer: Dimensionality reduction is achieved through principal component analysis (PCA) to eliminate differences in parameter dimensions between devices; Training data: Steady-state data under normal operating conditions of each device are selected as positive samples, and data under various fault conditions are selected as negative samples. The labels include device type and fault type. Classification layer: SVM handles nonlinear classification through radial basis function (RBF), DNN extracts cross-device correlation features through multilayer perceptron, and achieves multi-device fault prediction by finding the optimal classification hyperplane or backpropagation optimization; Step 104: Input the real-time extracted features into the trained equipment fault prediction model to obtain the fault prediction result; Step 105: When a device failure is predicted, activate the multimodal early warning mechanism and search for a matching solution in the emergency response solution library based on the predicted failure type.

[0019] In this embodiment, the multimodal early warning mechanism includes: Sound Alarm Design: Different sound signals are set according to the severity and type of the fault. For severe faults, such as damage to critical components that may cause immediate equipment shutdown, a high-frequency (e.g., above 2000Hz) and continuous sharp alarm sound is emitted, with the volume set between 80 and 100 decibels. For general faults, such as a slight decrease in equipment performance that can still be maintained, a low-frequency (e.g., 500-1000Hz) intermittent buzzing sound is emitted, with the volume between 60 and 80 decibels. The sound signals are played through speakers installed in the monitoring room and at the equipment site to ensure that operators can hear them from different locations.

[0020] Tactile Warning Design: Equip operators with wearable tactile feedback devices, such as smart bracelets or vibration-enabled work clothes. When a fault occurs, a warning signal is sent to the tactile device via Bluetooth or other wireless communication technologies. Different vibration modes are set for different types of faults. For example, for mechanical component faults, a short-interval, high-intensity vibration mode is used, such as vibrating 5 times per second with a large amplitude each time; for electrical faults, a long-interval, gentler vibration mode is used, such as vibrating once every 3 seconds with a smaller amplitude. In this way, operators can perceive fault information through touch even when their vision or hearing is occupied by other tasks.

[0021] Video early warning design: Utilizing high-definition cameras at the equipment site, when a fault is predicted, the system automatically switches to the video feed of the relevant equipment area and highlights the potentially faulty area using a prominent color (such as a flashing red frame). Simultaneously, text information is overlaid on the video feed, briefly explaining the fault type and its potential impact. The video signal is transmitted via network to a display screen in the monitoring room, allowing operators to intuitively view the actual status of the equipment.

[0022] In this embodiment, an emergency response plan database is established. When a fault symptom is detected, a matching plan is searched in the preset emergency response plan database based on historical fault data and the currently predicted fault type. Specifically: Water turbine: Bearing wear (vibration peak factor > 3.5): Automatically reduce load by 10%-20%, trigger the backup lubrication system, and generate a 72-hour maintenance work order; Blade cracks (amplitude greater than twice the normal value at 2 times the frequency): Start the standby unit and shut down the unit to inspect the blades; dynamo: Overheating of windings (stator temperature > 130℃ and rate of increase > 5℃ / min): Automatically increase cooling fan speed (Δn = 50 rpm / ℃ × (T - 120℃)), and request a 30% load reduction; Rotor imbalance (shaft vibration RMS > 8 mm / s): triggers low-frequency continuous vibration tactile warning, and automatically stops when vibration intensifies; Main transformer: Partial discharge (H2 concentration in oil > 100ppm): Locate the discharge location via video, activate the nitrogen fire extinguishing system for pre-cooling, and conduct maintenance and inspection within 2 hours; Winding short circuit (oil temperature > 95℃ and sudden increase in CH4): Immediately trip the circuit breaker and start the standby transformer; Water pumps / valves: Impeller blockage (flow rate < 80% of rated value): Switch to standby pump and send backwash command (30 seconds reverse water flow); Seal leakage (leakage rate > 5L / min): Close the isolation valve, activate the standby valve, and replace the seal; For equipment with self-healing capabilities (such as parameter adjustment or backup switching), an intelligent self-healing mechanism is activated. For example, in some equipment, when the main equipment fails, the status of the backup equipment is automatically detected and the backup equipment is activated to take over the work; or the operating parameters of the equipment, such as voltage, current, and frequency, are automatically adjusted to restore it to the normal operating range. The adjustment formula can be set according to the specific characteristics and operating requirements of the equipment.

[0023] For example, if it is predicted that a water pump may malfunction due to excessive pressure, the pump speed will be automatically adjusted or the pressure relief valve will be opened to reduce the pressure. The calculation formula is as follows:

[0024] in, The adjusted rotational speed. The original rotational speed, For target pressure, For the current pressure; If a power generation device experiences a localized overheating fault, the speed of the cooling fan can be automatically increased. The increase in speed can be linearly adjusted based on the temperature difference, using the following formula:

[0025] in The amount of increase in fan speed, This is the proportionality coefficient. The current temperature. This represents the temperature threshold.

[0026] During the design and development of equipment, manufacturers typically conduct detailed analysis and testing of the equipment's heat dissipation requirements and fan performance. They then determine fan speed adjustment strategies for different temperature conditions based on factors such as the equipment's power, heat dissipation structure, and operating environment, providing relevant empirical data or design parameters. This embodiment uses information provided by the manufacturer to determine the scaling factor. For example, the manufacturer might specify in the equipment's technical manual that for this model, the fan speed should increase by 50 rpm for every 1°C increase in temperature. Based on this, the following calculations can be made: =50 (Assuming that the increase in rotational speed is linearly related to the temperature difference).

[0027] For example, the voltage adjustment formula is:

[0028] in The adjusted voltage. The original voltage, This is the voltage adjustment coefficient. This represents the voltage deviation. It should be noted that... The value should refer to the proportional coefficient in the cooling fan adjustment formula mentioned above. How to obtain it.

[0029] The intelligent monitoring method for fault prediction and early warning of hydropower plant equipment in this application embodiment provides real-time fault early warning through equipment fault prediction model and performs automatic emergency handling when a fault occurs. This enables the equipment to take measures quickly and automatically when fault symptoms appear, preventing the fault from worsening, reducing the need for manual intervention, reducing reliance on the professional skills of operators, significantly improving the fault tolerance of the equipment, reducing equipment downtime and maintenance costs, and improving the economic benefits of hydropower plants and the reliability of power supply.

[0030] To achieve the above embodiments, this application also proposes an intelligent monitoring system 2 for predicting and warning of equipment failures in hydropower plants.

[0031] Figure 2 This is a schematic diagram of the structure of an intelligent monitoring system for predicting and warning equipment failures in a hydropower plant, provided as an embodiment of this application.

[0032] like Figure 2 As shown, the intelligent monitoring system for equipment fault prediction and early warning at this hydropower plant includes: The data acquisition module is used to collect operating parameters of hydropower plant equipment through sensors; The feature extraction module is used to extract features reflecting the device status from the collected operating parameters. These features include time-domain features and frequency-domain features. The model building module is used to build equipment fault prediction models based on operating parameters and feature quantities; The fault prediction module is used to input the real-time extracted features into the trained equipment fault prediction model to obtain the fault prediction result. The fault handling module is used to activate a multimodal early warning mechanism when a fault is predicted to occur in the equipment, and to search for a matching solution in the emergency handling solution library based on the predicted fault type.

[0033] Furthermore, in this embodiment of the application, the hydropower plant equipment includes a turbine, a generator, a main transformer, and pumps / valvees. The operating parameters of the hydropower plant equipment are collected via sensors, including: For water turbines, vibration sensors are used to collect shaft vibration acceleration, pressure sensors are used to collect volute pressure and inlet pressure, temperature sensors are used to collect bearing temperature, speed sensors are used to collect speed, and displacement sensors are used to collect guide vane opening. For generators, stator temperature and winding temperature are collected by temperature sensors, rotor current and motor current are collected by current sensors, shaft vibration acceleration is collected by vibration sensors, output voltage is collected by voltage sensors, output frequency is collected by frequency sensors, and winding insulation resistance is collected by insulation resistance testers. For the main transformer, oil temperature and winding temperature are collected by temperature sensors, gas concentration in oil is collected by an online oil chromatograph, and bushing insulation resistance is collected by an insulation resistance tester. For water pumps / valves, inlet and outlet pressures are collected by pressure sensors, outlet flow rates are collected by flow sensors, motor currents are collected by current sensors, valve disc vibrations and impeller vibrations are collected by vibration sensors, and seal leakage is collected by leakage detectors.

[0034] It should be noted that the explanation of the above-mentioned intelligent monitoring method for predicting and warning of equipment failures in hydropower plants also applies to the intelligent monitoring system for predicting and warning of equipment failures in hydropower plants in this embodiment, and will not be repeated here.

[0035] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0036] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" 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.

[0037] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0038] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0039] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0040] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0041] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0042] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. An intelligent monitoring method for predicting and warning of equipment failures in hydropower plants, characterized in that, include: The operating parameters of hydropower plant equipment are collected through sensors; The system extracts features reflecting the device status from the collected operating parameters, wherein the features include time-domain features and frequency-domain features. Based on operating parameters and characteristic quantities, construct a device fault prediction model; The features extracted in real time are input into the trained equipment fault prediction model to obtain the fault prediction results; When a device malfunction is predicted, a multimodal early warning mechanism is activated, and a matching solution is searched in the emergency response solution library based on the predicted malfunction type.

2. The method as described in claim 1, characterized in that, The hydropower plant equipment includes turbines, generators, main transformers, and pumps / valvees. Sensors collect operating parameters of the hydropower plant equipment, including: For water turbines, vibration sensors are used to collect shaft vibration acceleration, pressure sensors are used to collect volute pressure and inlet pressure, temperature sensors are used to collect bearing temperature, speed sensors are used to collect speed, and displacement sensors are used to collect guide vane opening. For generators, stator temperature and winding temperature are collected by temperature sensors, rotor current and motor current are collected by current sensors, shaft vibration acceleration is collected by vibration sensors, output voltage is collected by voltage sensors, output frequency is collected by frequency sensors, and winding insulation resistance is collected by insulation resistance testers. For the main transformer, oil temperature and winding temperature are collected by temperature sensors, gas concentration in oil is collected by an online oil chromatograph, and bushing insulation resistance is collected by an insulation resistance tester. For water pumps / valves, inlet and outlet pressures are collected by pressure sensors, outlet flow rates are collected by flow sensors, motor currents are collected by current sensors, valve disc vibrations and impeller vibrations are collected by vibration sensors, and seal leakage is collected by leakage detectors.

3. The method as described in claim 1, characterized in that, Before extracting feature quantities reflecting the equipment status from the collected operating parameters, the process also includes: The operating parameters are preprocessed, including outlier detection, noise elimination, and data normalization. Outlier detection is performed by using the 3σ criterion to detect and correct outliers. Noise elimination is performed by using the Kalman filter algorithm to eliminate noise. Data normalization is performed by mapping the data to the [0,1] interval using a linear normalization method.

4. The method as described in claim 2, characterized in that, The characteristic quantities of water turbines, generators, and water pumps / valve are related to vibration and current signals, while the characteristic quantities of main transformers are related to the rate of change of oil chromatographic concentration.

5. The method as described in claim 1, characterized in that, The time-domain features include mean, peak value, root mean square, waveform factor, and peak factor. The frequency-domain features include spectral center, band energy, and characteristic frequency amplitude. The spectral center is calculated based on the spectral distribution after performing a fast Fourier transform on the time-domain signal. The band energy is obtained by setting a band of interest and calculating the energy percentage within that band. The characteristic frequency amplitude is the amplitude corresponding to a specific frequency point.

6. The method as described in claim 1, characterized in that, Based on operating parameters and characteristic quantities, a device fault prediction model is constructed, including: A support vector machine (SVM) is used to construct a device fault prediction model. The constructed model includes: The input layer is used to fuse feature vectors from multiple devices and combine them with device type-encoded one-hot vectors. The feature processing layer is used to reduce dimensionality through principal component analysis and eliminate differences in the dimensions of parameters between devices; The classification layer is used to process nonlinear classification by SVM through radial basis function (RBF) kernel, and to extract cross-device correlation features by DNN through multilayer perceptron. Multi-device fault prediction is achieved by finding the optimal classification hyperplane. Steady-state data under normal operating conditions of each device are selected as positive samples, and data under various fault conditions are selected as negative samples. Equipment type and fault type are used as labels to construct training data and train the constructed model.

7. The method as described in claim 1, characterized in that, The multimodal early warning mechanism includes sound alarms, tactile alarms, and video alarms.

8. The method as described in claim 2, characterized in that, The fault types of the water turbine include bearing wear and blade cracks; the fault types of the generator include winding overheating and rotor imbalance; the fault types of the main transformer include partial discharge and winding short circuit; the fault types of the water pump / valve include impeller blockage and seal leakage; and the emergency handling plan includes: For the aforementioned turbine, when bearing wear occurs and the vibration peak factor exceeds a preset threshold, the load is automatically reduced, the backup lubrication system is triggered, and a real-time maintenance work order is generated. When blade cracks occur and the rotational frequency amplitude exceeds a preset threshold, the backup unit is started, and the turbine is shut down for blade maintenance. For the generator, when the winding overheats, the stator temperature and the rate of temperature rise both exceed the corresponding threshold, the cooling fan speed is automatically increased and a load reduction is requested. When rotor imbalance occurs, a low-frequency continuous vibration tactile warning is triggered, and the generator automatically shuts down when the vibration intensifies. For the main transformer, when partial discharge occurs, the H2 concentration in the oil is greater than a preset threshold. The discharge location is located by video, the nitrogen fire extinguishing system is activated for cooling, and real-time operation and maintenance monitoring is performed. When a winding short circuit occurs, the oil temperature is greater than a preset threshold and the instantaneous CH4 concentration growth rate is greater than the threshold. The circuit breaker is immediately tripped and the standby transformer is activated. For the aforementioned water pump / valve, when impeller blockage occurs and the flow rate to rated value ratio is less than the threshold, switch to standby pump and send backwash command; when seal leakage occurs and leakage rate is greater than the threshold, close isolation valve, start standby valve, and replace seals. The emergency response plan also includes: For equipment with self-healing capabilities, when a fault occurs, the equipment parameters are automatically adjusted, or the status of the backup equipment is automatically detected and switched to.

9. An intelligent monitoring system for predicting and warning of equipment failures in a hydropower plant, characterized in that, include: The data acquisition module is used to collect operating parameters of hydropower plant equipment through sensors; The feature extraction module is used to extract features reflecting the device status from the collected operating parameters, wherein the features include time-domain features and frequency-domain features; The model building module is used to build equipment fault prediction models based on operating parameters and feature quantities; The fault prediction module is used to input the real-time extracted features into the trained equipment fault prediction model to obtain the fault prediction result. The fault handling module is used to activate a multimodal early warning mechanism when a fault is predicted to occur in the equipment, and to search for a matching solution in the emergency handling solution library based on the predicted fault type.

10. The system as described in claim 9, characterized in that, The hydropower plant equipment includes turbines, generators, main transformers, and pumps / valvees. Sensors collect operating parameters of the hydropower plant equipment, including: For water turbines, vibration sensors are used to collect shaft vibration acceleration, pressure sensors are used to collect volute pressure and inlet pressure, temperature sensors are used to collect bearing temperature, speed sensors are used to collect speed, and displacement sensors are used to collect guide vane opening. For generators, stator temperature and winding temperature are collected by temperature sensors, rotor current and motor current are collected by current sensors, shaft vibration acceleration is collected by vibration sensors, output voltage is collected by voltage sensors, output frequency is collected by frequency sensors, and winding insulation resistance is collected by insulation resistance testers. For the main transformer, oil temperature and winding temperature are collected by temperature sensors, gas concentration in oil is collected by an online oil chromatograph, and bushing insulation resistance is collected by an insulation resistance tester. For water pumps / valves, inlet and outlet pressures are collected by pressure sensors, outlet flow rates are collected by flow sensors, motor currents are collected by current sensors, valve disc vibrations and impeller vibrations are collected by vibration sensors, and seal leakage is collected by leakage detectors.

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