Hydraulic engineering machine motor set fault early warning system

Abstract

The invention discloses a hydraulic engineering motor set fault early warning system, and relates to the technical field of hydraulic engineering equipment monitoring, and the hydraulic engineering motor set fault early warning system comprises the following steps: S1, collecting and integrating mechanical state data, including vibration, temperature and oil pollution monitoring; s2, electrical parameter monitoring and complementary analysis are carried out, and mechanical data are associated to identify faults such as short circuit; s3, hydraulic monitoring and cooperative early warning are carried out, pressure flow abnormity is detected, and mechanical and electrical data are linked; s4, multi-source data fusion and fault classification are carried out, and dynamic weight distribution is carried out to improve the diagnosis accuracy; s5, dynamic threshold optimization and cloud decision making are carried out, and the threshold is adjusted according to working conditions to reduce the false alarm rate; s6, closed-loop maintenance and intelligent pushing are carried out, faults are positioned, maintenance suggestions are pushed, and closed-loop verification is carried out; the method has the beneficial effects that accurate fault classification, cross-dimension collaborative early warning and closed-loop maintenance decision making are realized, and the operation reliability and the maintenance efficiency of the unit are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hydraulic engineering equipment monitoring, and particularly relates to a hydraulic engineering electromechanical unit fault early warning system. BACKGROUND

[0002] Traditional fault early warning systems mostly rely on single-dimensional data (such as only vibration or temperature monitoring), lack deep fusion of multi-source data, and thus have high false alarm rates and inaccurate fault positioning. For example: data island problem: mechanical, electrical and hydraulic monitoring data are independently analyzed, cross-dimensional correlation is not fully utilized, and it is difficult to identify complex fault modes (such as lubrication failure accompanied by winding short circuit); static threshold limitation: fixed alarm threshold cannot adapt to dynamic working conditions such as device start-stop and variable load, and is easily triggered by transient interference to cause false alarms; maintenance lag: traditional systems only provide alarms and lack intelligent maintenance decision support, rely on manual experience, and have low response efficiency; sensor limitation: single sensor data is easily disturbed by noise and cannot fully reflect the device state (such as vibration data alone cannot accurately assess the lubrication state).

[0003] In the prior art, there is no comprehensive solution that fuses mechanical, electrical and hydraulic multi-source data and realizes accurate early warning and closed-loop maintenance through dynamic weight distribution and cloud optimization. SUMMARY

[0004] The present application provides a hydraulic engineering electromechanical unit fault early warning system, which fuses mechanical, electrical and hydraulic multi-source data, combines dynamic threshold optimization and intelligent analysis algorithm, realizes accurate fault classification, cross-dimensional collaborative early warning and closed-loop maintenance decision, and significantly improves the operation reliability and maintenance efficiency of the unit.

[0005] Technical solution: A hydraulic engineering electromechanical unit fault early warning system, comprising the following steps: S1. Mechanical state data acquisition and integration: integrate acceleration sensors and vibration analyzers through a vibration monitoring subsystem to real-time collect vibration frequency and amplitude of the unit rotor and bearings; combine infrared sensors and thermocouples deployed by a temperature monitoring subsystem to track temperature changes of the motor winding and gear box parts; at the same time, a portable particle counter and a moisture detector are used by an oil analysis subsystem to regularly detect the metal particle and contaminant content in the lubricating oil, and comprehensively determine the risk of mechanical wear, overheating and lubrication failure; S2. Electrical parameter monitoring and complementary analysis: Real-time monitoring of current, voltage and power factor fluctuations in the power circuit by using the current sensor and voltage sensor integrated in the generator and power cabinet; diagnosing winding insulation degradation and accurately identifying short circuit and open phase electrical faults through the ground resistance detector and partial discharge sensor configured by the insulation monitoring unit; the electrical parameter monitoring results complement the mechanical data of S1, and current abnormalities are associated with bearing vibration intensification or winding temperature rise, providing key inputs for subsequent cross-dimensional analysis; S3. Hydraulic monitoring and collaborative early warning: Real-time detection of abnormal pressure fluctuations by installing corrosion-resistant pressure transmitters in the water turbine inlet pipe and pump outlet; monitoring the flow trend to prevent cavitation and pipeline blockage by deploying turbine flow meters in the water pipeline. The hydraulic monitoring data work together with the mechanical and electrical data, and sudden pressure drop triggers abnormal vibration or current fluctuation. Such associated characteristics are directly input into the cross-dimensional analysis framework of S4; S4. Multi-source data fusion and fault classification: Integrating heterogeneous data streams from S1 mechanical monitoring, S2 electrical monitoring and S3 hydraulic monitoring through SCADA monitoring and data acquisition system, establishing a multi-dimensional data matrix based on timestamp alignment; through dynamic weight allocation strategy, vibration data is given priority as the main weight in bearing fault diagnosis, while lubrication failure is evaluated comprehensively by combining temperature and oil data, generating high-confidence fault classification results; this algorithm dynamically integrates heterogeneous data from S1-S3, solves the limitations of single sensor, and significantly improves the accuracy of lubrication failure diagnosis when electrical parameters and oil data are abnormal at the same time; S5. Dynamic threshold optimization and cloud decision: The cloud early warning platform identifies the equipment operating stage, including start-stop, steady state and variable load conditions, through flow and current time series characteristics, and dynamically matches the preset threshold group; the vibration alarm threshold is automatically relaxed during the start-stop stage to reduce transient interference and significantly reduce false alarm rate. Based on the fault classification results output by S4, the cloud platform dynamically optimizes the threshold strategy combined with the flow trend in historical operation data, ensuring the alarm sensitivity and reliability under different working conditions; S6. Closed-loop maintenance and intelligent push: Based on the real-time monitoring data of S1-S5 and the fault classification results generated by S4, the local alarm unit triggers audible and visual alarms and locates the fault position; the cloud platform generates executable maintenance recommendations by analyzing the threshold strategy optimized by S5 and historical operation data through machine learning, and pushes them to the mobile terminal of the operation and maintenance personnel in real time; maintenance recommendations integrate real-time abnormal indicators and long-term trend characteristics, forming a closed-loop management from fault early warning, threshold optimization to maintenance execution, improving system sustainability.

[0006] Preferably, S1 specifically comprises the following steps: S1-1. Vibration monitoring subsystem deployment: Real-time acquisition of vibration frequency and amplitude data of the rotor and bearing of the unit through integration of acceleration sensors and vibration analyzers; S1-2. Temperature monitoring subsystem configuration: Continuous tracking of temperature changes by deploying infrared sensors on the surface of the motor winding and embedding thermocouples in key nodes of the gearbox; S1-3. Oil analysis subsystem execution: Detection of metal particle concentration in lubricating oil using a portable particle counter and simultaneous quantification of oil contamination content using a moisture detector; S1-4. Risk comprehensive judgment: Establishment of a three-level early warning model for mechanical wear, overheating, and lubrication failure based on vibration spectrum characteristics, temperature gradient curves, and oil pollution indexes; Preferably, the S2 specifically includes the following steps: S2-1. Power circuit parameter monitoring: Real-time capture of three-phase current, voltage effective value, and power factor fluctuations by installing current / voltage sensors in the generator and power distribution cabinet; S2-2. Insulation state diagnosis: Measurement of winding-to-ground insulation resistance by a ground resistance detector and capture of microampere-level discharge pulse signals by a partial discharge sensor; S2-3. Cross-dimensional correlation analysis: Establishment of a time series correlation matrix between current abnormal events and bearing vibration spectrum peak values and winding temperature mutations in S1; Preferably, the S3 specifically includes the following steps: S3-1. Dynamic water pressure monitoring: Installation of a corrosion-resistant pressure transmitter at the flange of the water inlet pipe to detect pressure fluctuation characteristics at a sampling rate of 100 Hz; S3-2. Flow trend analysis: Construction of a flow-time change curve by a turbine flowmeter to identify the characteristic flow steep drop mode of cavitation; S3-3. Collaborative early warning triggering: Activation of a three-level interlock early warning protocol when a pressure drop event occurs synchronously with vibration amplitude exceeding the standard in S1 or current harmonic distortion in S2; Preferably, the S4 specifically includes the following steps: S4-1. Data space-time alignment: Reduction of sampling rate and time scale synchronization of mechanical vibration data with a sampling rate of 5 kHz, electrical parameters with a sampling rate of 50 Hz, and hydraulic data with a sampling rate of 10 Hz by a SCADA monitoring and data acquisition system; S4-2. Dynamic weight distribution: Assignment of a weight coefficient of 0.7 to vibration data in the bearing fault mode using a sliding time window algorithm; for the lubrication failure scenario, comprehensive evaluation is performed through weighted combination of temperature data 0.5 and oil data 0.5; S4-3. Failure mode identification: When the oil metal particle concentration is greater than 800ppm and the current harmonic content is greater than 5%, it is determined that the composite failure mode of lubrication failure accompanied by winding short circuit; Preferably, the S5 specifically comprises the following steps: S5-1. Working condition feature extraction: Based on the flow rate change rate parameter and the current fluctuation standard deviation index, the running stage is divided by threshold condition: start-stop stage: activated when the flow rate change rate exceeds 10 liters per second square; steady state: determined when the current fluctuation standard deviation is lower than 0.05 standard value; S5-2. Threshold dynamic adjustment: In the start-stop stage, the vibration alarm threshold is increased to 150% of the normal value, and the steady state restores the ISO10816-3 standard threshold; S5-3. Cloud strategy optimization: Combined with the historical failure case library, when the flow rate decreases by 5% for 3 hours, the cavitation erosion early warning threshold sensitivity is automatically increased by 30%; Preferably, the S6 specifically comprises the following steps: S6-1. Local alarm positioning: Through ZigBee mesh topology, the fault device ID code and physical coordinates are integrated in the sound and light alarm unit; S6-2. Maintenance decision generation: Using LSTM neural network to predict the remaining useful life, when the oil water content is greater than 500ppm, the "oil change within 72 hours" instruction is pushed; S6-3. Closed-loop verification mechanism: After maintenance, a composite verification test including 0.5-2kHz frequency band vibration baseline and total harmonic distortion rate THD lower than 3% is automatically started, which specifically includes: vibration baseline verification: in the 0.5kHz to 2kHz characteristic frequency band, detect whether the unit vibration amplitude returns to the preset baseline range; current quality verification: measure the power supply loop current harmonic distortion rate, ensure that the THD index is less than 3% of the industrial standard limit.

[0007] Compared with the prior art, the advantages of the present application are: (1) Multi-source heterogeneous data deep fusion: By integrating vibration, temperature, oil, current, voltage, pressure, flow and other multi-dimensional data, a multi-source data matrix aligned in time and space is constructed, combined with dynamic weight distribution strategy (such as bearing failure with vibration data as main weight, lubrication failure with temperature and oil data), which significantly improves the accuracy of fault classification and overcomes the limitations of single sensor; (2) Dynamic threshold optimization and working condition self-adaptation: The cloud platform dynamically adjusts the alarm threshold based on the equipment running stage (start-stop, steady state, variable load), for example, relax the vibration threshold in the start-stop stage to reduce false alarms, and increase the cavitation erosion early warning sensitivity when the flow rate continuously decreases, to ensure the alarm reliability and sensitivity in different working conditions; (3) Cross-dimension coordinated early warning mechanism: Establish a correlation analysis model of mechanical, electrical and hydraulic data, such as pressure drop and vibration exceeding the standard triggering interlocking early warning, time series correlation matrix formed by abnormal current and temperature mutation, accurately identifying complex failure modes (such as lubrication failure accompanied by winding short circuit). DETAILED DESCRIPTION

[0008] EMBODIMENT 1. A water conservancy electromechanical unit fault early warning system, characterized in that it comprises the following steps: S1. Mechanical state data acquisition and integration: Through the integration of acceleration sensors and vibration analyzers in the vibration monitoring subsystem, the vibration frequency and amplitude of the rotor and bearing of the unit are collected in real time; combined with the infrared sensors and thermocouples deployed in the temperature monitoring subsystem, the temperature changes of the motor winding and gear box parts are tracked; at the same time, the oil analysis subsystem periodically detects the metal particles and contaminant content in the lubricating oil through the portable particle counter and moisture detector, and comprehensively determines the risk of mechanical wear, overheating and lubrication failure; S1 specifically comprises the following steps: S1-1. Vibration monitoring subsystem deployment: Through the integration of acceleration sensors and vibration analyzers, the vibration frequency and amplitude data of the rotor and bearing of the unit are collected in real time; S1-2. Temperature monitoring subsystem configuration: Deploy infrared sensors on the surface of the motor winding, and embed thermocouples in the key nodes of the gear box to continuously track temperature changes; S1-3. Oil analysis subsystem execution: Use a portable particle counter to detect the metal particle concentration in the lubricating oil, and simultaneously use a moisture detector to quantify the oil contaminant content; S1-4. Risk comprehensive determination: Based on the vibration spectrum characteristics, temperature gradient curve and oil pollution index, a three-level early warning model for mechanical wear, overheating and lubrication failure is established, which specifically includes the following contents: Three-level early warning model construction standard: S1-4-1. Mechanical wear early warning: Vibration energy threshold: trigger when 1-3 kHz frequency band vibration effective value > 4.5 mm / s; Wear index formula:

[0009] Wherein , ; S1-4-2. Overheating early warning: Temperature gradient threshold: trigger when the temperature difference of the gear box nodes > 15℃ / 10min; Winding temperature compensation formula:

[0010] S1-4-3. Lubrication failure early warning: Composite pollution index: ; When CPI > 1, the oil replacement protocol is started; S2. Electrical parameter monitoring and complementary analysis: Real-time monitoring of current, voltage and power factor fluctuation in the power circuit by integrating current sensor, voltage sensor in the generator and power cabinet; Diagnose winding insulation degradation through ground resistance detector and partial discharge sensor configured by insulation monitoring unit, accurately identify short circuit, open phase electrical fault; The electrical parameter monitoring results and S1 mechanical data form a complement, current abnormality is associated with bearing vibration aggravation or winding temperature rise, providing key input for subsequent cross-dimensional analysis; The S2 specifically comprises the following steps: S2-1. Power circuit parameter monitoring: Install current / voltage sensor in the generator and power cabinet to capture real-time three-phase current, voltage effective value and power factor fluctuation; S2-2. Insulation state diagnosis: Measure the winding insulation resistance to ground by ground resistance detector, and capture microampere level discharge pulse signal with partial discharge sensor; S2-3. Cross-dimensional correlation analysis: Establish time sequence correlation matrix between current abnormality event and S1 bearing vibration frequency spectrum peak value, winding temperature mutation, which specifically includes the following contents: The construction of the time-space correlation matrix includes the following elements S2-3-1. Time alignment window: ±200ms sliding window matching mechanism; Correlation degree calculation:

[0011] Where X is the current parameter, Y is the vibration / temperature parameter: Triggering condition: When the correlation coefficient of current harmonic distortion rate and bearing vibration in 100-400Hz frequency band is >0.75; The time lag of winding temperature change rate and current effective value change rate is <5 seconds; S3. Hydraulic monitoring and collaborative early warning: Install corrosion-resistant pressure transducer at the water inlet pipe of the water turbine and the outlet of the water pump to detect real-time pressure abnormal fluctuation; Deploy turbine flowmeter in the water pipeline to monitor the flow change trend and prevent cavitation, pipeline blockage hydraulic failure; Hydraulic monitoring data work together with mechanical and electrical data, pressure drop triggers vibration anomaly or current fluctuation, such correlation characteristics are directly input into the cross-dimensional analysis framework of S4; The S3 specifically comprises the following steps: S3-1. Water pressure dynamic monitoring: Install corrosion-resistant pressure transducer at the water inlet pipe flange to detect pressure fluctuation characteristics at 100Hz sampling rate; S3-2. Flow trend analysis: Construct flow-time curve by turbine flow meter, identify the characteristic flow drop pattern of cavitation erosion; S3-3. Collaborative early warning trigger: When the pressure drop event occurs synchronously with the vibration amplitude exceeding the threshold of S1 or the current harmonic distortion exceeding the threshold of S2, activate the Level 3 interlock early warning protocol, which includes the following: When the pressure drop event occurs synchronously with mechanical and electrical abnormalities, the system starts the Level 3 interlock early warning protocol: S3-3-1. Primary warning: When the pressure drops by 20% and lasts for 10 seconds: trigger a yellow alarm in the SCADA system, automatically adjust the guide vane opening to compensate for the pressure; S3-3-2. Intermediate warning: When the pressure drops by 30% and the vibration amplitude is greater than 8 mm / s 2 : activate the water pump speed reduction program and simultaneously shut down non-critical electrical loads; S3-3-3. Advanced warning: When the pressure drops by 50% and the current harmonic distortion rate is greater than 15%: execute the emergency shutdown instruction, start the standby unit and push the GIS coordinates to the emergency response system; S4. Multi-source data fusion and fault classification: Integrate heterogeneous data streams from S1 mechanical monitoring, S2 electrical monitoring and S3 hydraulic monitoring through the monitoring and data acquisition system, establish a multi-dimensional data matrix based on timestamp alignment; through dynamic weight distribution strategy, vibration data is given priority as the main weight in bearing fault diagnosis, while lubrication failure is evaluated comprehensively by combining temperature and oil data, generating high-confidence fault classification results; this algorithm dynamically integrates heterogeneous data from S1-S3, solving the limitations of single sensor, significantly improving the accuracy of lubrication failure diagnosis when electrical parameters and oil data are abnormal at the same time; The S4 specifically includes the following steps: S4-1. Data space-time alignment: Through the monitoring and data acquisition system, downsample and synchronize the timestamps of mechanical vibration data with a sampling rate of 5kHz, electrical parameters with a sampling rate of 50Hz and hydraulic data with a sampling rate of 10Hz; S4-2. Dynamic weight distribution: Use sliding time window algorithm to assign vibration data a weight coefficient of 0.7 in bearing fault mode; for lubrication failure scenario, evaluate comprehensively through weighted combination of temperature data 0.5 and oil data 0.5, which includes the following: Weight distribution uses multi-objective optimization algorithm with sliding time window (200ms window): S4-2-1. Bearing fault mode: vibration data main weight 0.7, among which high frequency band (2-5kHz) accounts for 0.4 and power frequency component accounts for 0.3; S4-2-2. Lubrication failure mode: the weighted combination formula of temperature data and oil data is: Comprehensive score = 0.5 x (T_actual - T_base) / ΔT_max + 0.5 x (C_metal / 800 ppm + C_water / 500 ppm) / 2 When the comprehensive score > 0.8, a secondary early warning is triggered, and an electrical parameter correction factor is introduced: if the current harmonic distortion rate > 5%, the weight adjustment is temperature 0.6 / oil 0.4; S4-3. Fault mode identification: when the oil metal particle concentration is greater than 800 ppm and the current harmonic content is greater than 5%, it is determined that the lubrication failure is accompanied by a composite fault mode of winding short circuit; S5. Dynamic threshold optimization and cloud decision: the cloud early warning platform identifies the equipment running stage through flow and current time sequence characteristics, including start-stop, steady state and variable load conditions, and dynamically matches the preset threshold group; the vibration alarm threshold is automatically relaxed in the start-stop stage to reduce transient interference, significantly reducing the false alarm rate; the cloud platform dynamically optimizes the threshold strategy based on the fault classification results output by S4, combined with the flow trend in the historical operation data, to ensure the alarm sensitivity and reliability under different conditions; The S5 specifically includes the following steps: S5-1. Working condition feature extraction: based on the flow rate change rate parameter and the current fluctuation standard deviation index, the running stage is divided by threshold conditions: start-stop stage: when the flow rate change rate exceeds 10 liters per second square, it is activated; steady state: when the current fluctuation standard deviation is less than 0.05 standard value, it is determined; S5-2. Dynamic adjustment of threshold: in the start-stop stage, the vibration alarm threshold is increased to 150% of the normal value, and the steady state restores the ISO10816-3 standard threshold; S5-3. Cloud strategy optimization: combined with the historical fault case library, when the flow rate decreases by 5% for 3 hours, the cavitation erosion early warning threshold sensitivity is automatically increased by 30%; S6. Closed-loop maintenance and intelligent push: based on the real-time monitoring data of S1-S5 and the fault classification results generated by S4, the local alarm unit triggers an audible and visual alarm and locates the fault position; the cloud platform analyzes the threshold strategy optimized by S5 and the historical operation data through machine learning, generates executable maintenance recommendations, and pushes them to the mobile terminal of the operation and maintenance personnel in real time; the maintenance recommendations integrate real-time abnormal indicators and long-term trend characteristics, forming a closed-loop management from fault warning, threshold optimization to maintenance execution, improving the sustainability of the system.

[0012] The S6 specifically includes the following steps: S6-1. Local alarm positioning: through ZigBee mesh topology, the fault device ID code and physical coordinates are integrated in the audible and visual alarm unit; S6-2. Maintenance decision generation: adopt LSTM neural network to predict the remaining useful life, and push the "change oil within 72 hours" instruction when the water content in the oil is greater than 500ppm; Remaining useful life (RUL) prediction model parameters: S6-2-1. Input feature dimension: vibration envelope spectrum entropy value (0-5kHz); water content change gradient in oil (ppm / h); current THD trend slope (% / h); S6-2-2. LSTM network structure: 3 layers of stacked LSTM units (128→64→32); sliding input window: 24 hours of historical data; output layer: Sigmoid activation function maps the remaining life percentage; S6-2-3. Training data requirements: at least 3 complete failure cycles of labeled data; data sampling interval ≤5 minutes; S6-3. Closed-loop verification mechanism: after maintenance is completed, automatically start a composite verification test including 0.5-2kHz frequency band vibration baseline and total harmonic distortion rate THD lower than 3%, which specifically includes: vibration baseline verification: in the 0.5kHz to 2kHz characteristic frequency band, detect whether the unit vibration amplitude returns to the preset baseline range; current quality verification: measure the harmonic distortion rate of the power supply circuit current to ensure that the THD index is less than the industrial standard limit of 3%, which specifically includes the following content: The composite verification test includes a double-channel verification process S6-3-1. Vibration baseline verification: perform a sweep vibration test (step size 50Hz) in the 0.5-2kHz characteristic frequency band; Each frequency point amplitude needs to meet: ≤50μm@≤1kHz (rigid support section); ≤35μm@1-2kHz (bearing resonance zone); S6-3-2. Current quality verification: use an FFT analyzer to capture 0-50 harmonic components; The total harmonic distortion rate THD needs to meet: odd harmonics ∑(3rd+5th+7th)<2%; even harmonics ∑(2nd+4th+6th)<1%; three-phase imbalance degree detected simultaneously <0.5%.

[0013] The above shows and describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above examples, and the above examples and descriptions in the specification are only preferred examples of the present application and are not intended to limit the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A fault early warning system for generator sets in hydraulic engineering projects, characterized in that, Includes the following steps: S1. Mechanical Condition Data Acquisition and Integration: The vibration monitoring subsystem integrates an accelerometer and a vibration analyzer to collect the vibration frequency and amplitude of the unit's rotor and bearings in real time; combined with the infrared sensors and thermocouples deployed in the temperature monitoring subsystem, it tracks the temperature changes of the motor windings and gearbox; at the same time, the oil analysis subsystem uses a portable particle counter and a moisture detector to periodically detect the content of metal particles and contaminants in the lubricating oil, and comprehensively judges the risks of mechanical wear, overheating and lubrication failure. S2. Electrical Parameter Monitoring and Complementary Analysis: Utilizing integrated current and voltage sensors within the generator and transmission cabinet, real-time monitoring of current, voltage, and power factor fluctuations in the power circuit is achieved. Through the grounding resistance detector and partial discharge sensor configured in the insulation monitoring unit, winding insulation degradation is diagnosed, and short-circuit and phase-loss electrical faults are accurately identified. The electrical parameter monitoring results complement the mechanical data from S1; abnormal current is correlated with increased bearing vibration or rising winding temperature, providing crucial input for subsequent cross-dimensional analysis. S3. Hydraulic Monitoring and Collaborative Early Warning: Corrosion-resistant pressure transmitters are installed in the turbine inlet pipe and pump outlet to detect abnormal pressure fluctuations in real time; turbine flow meters are deployed in the water transmission pipeline to monitor flow rate trends and prevent hydraulic failures such as cavitation and pipe blockage. Hydraulic monitoring data works in conjunction with mechanical and electrical data; sudden pressure drops trigger abnormal vibrations or current fluctuations, and these correlated features are directly input into the cross-dimensional analysis framework of S4. S4. Multi-source Data Fusion and Fault Classification: This system integrates heterogeneous data streams from S1 (mechanical monitoring), S2 (electrical monitoring), and S3 (hydraulic monitoring) through a SCADA monitoring and data acquisition system, establishing a timestamp-aligned multi-dimensional data matrix. A dynamic weight allocation strategy prioritizes vibration data as the primary weight in bearing fault diagnosis, while lubrication failure is assessed by combining temperature and oil data to generate high-confidence fault classification results. This algorithm overcomes the limitations of single sensors by dynamically integrating heterogeneous data from S1 to S3, significantly improving the accuracy of lubrication failure diagnosis when both electrical parameters and oil data are abnormal simultaneously. S5. Dynamic Threshold Optimization and Cloud Decision-Making: The cloud-based early warning platform identifies the equipment's operating phase, including start-up, steady-state, and variable load conditions, based on flow and current timing characteristics, and dynamically matches preset threshold groups. During the start-up and shutdown phase, it automatically relaxes the vibration alarm threshold to reduce transient interference and significantly lower the false alarm rate. Based on the fault classification results output by S4, the cloud platform dynamically optimizes the threshold strategy by combining the traffic trends in historical operating data to ensure the sensitivity and reliability of alarms under different operating conditions. S6. Closed-Loop Maintenance and Intelligent Push: Based on real-time monitoring data from S1-S5 and fault classification results generated by S4, the local alarm unit triggers audible and visual alarms and locates the fault location; the cloud platform uses machine learning to analyze the threshold strategy dynamically optimized by S5 and historical operating data to generate executable maintenance suggestions, which are then pushed to the mobile terminals of maintenance personnel in real time; the maintenance suggestions integrate real-time abnormal indicators and long-term trend characteristics to form a closed-loop management system from fault warning and threshold optimization to maintenance execution, thereby improving system sustainability.

2. The fault early warning system for hydraulic engineering generator sets according to claim 1, characterized in that, S1 specifically includes the following steps: S1-1. Vibration monitoring subsystem deployment: By integrating an accelerometer with a vibration analyzer, the vibration frequency and amplitude data of the unit rotor and bearings are collected in real time; S1-2. Temperature monitoring subsystem configuration: Infrared sensors are deployed on the surface of the motor windings, and thermocouples are embedded in key nodes of the gearbox to continuously track temperature changes; S1-3. Oil Analysis Subsystem Execution: A portable particle counter is used to detect the concentration of metal particles in the lubricating oil, and a moisture analyzer is used simultaneously to quantify the content of oil contaminants. S1-4. Comprehensive Risk Assessment: Based on vibration spectrum characteristics, temperature gradient curves, and oil contamination index, a three-level early warning model for mechanical wear, overheating, and lubrication failure is established.

3. The fault early warning system for hydraulic engineering generator sets according to claim 1, characterized in that, S2 specifically includes the following steps: S2-1. Power circuit parameter monitoring: Install current / voltage sensors in the generator and transmission cabinet to capture the three-phase current, voltage RMS values ​​and power factor fluctuations in real time; S2-2. Insulation condition diagnosis: The insulation resistance of the winding to ground is measured by a grounding resistance tester, and microampere-level discharge pulse signals are captured by a partial discharge sensor. S2-3. Cross-dimensional correlation analysis: Establish a time series correlation matrix between abnormal current events and the peak value of bearing vibration spectrum and sudden change in winding temperature in S1.

4. The fault early warning system for hydraulic engineering generator sets according to claim 1, characterized in that, S3 specifically includes the following steps: S3-1. Dynamic monitoring of water pressure: Install a corrosion-resistant pressure transmitter at the flange of the water inlet pipe to detect pressure fluctuation characteristics at a sampling rate of 100Hz; S3-2. Flow Trend Analysis: Construct flow-time variation curves using turbine flow meters to identify cavitation-characteristic flow drop patterns; S3-3. Collaborative Early Warning Trigger: When a sudden pressure drop event occurs simultaneously with the vibration amplitude exceeding the standard in S1 or the current harmonic distortion in S2, the three-level interlocking early warning protocol is activated.

5. A fault early warning system for hydraulic engineering generator sets according to claim 1, characterized in that, S4 specifically includes the following steps: S4-1. Data Spatiotemporal Alignment: Downsampling and time-scale synchronization are performed on mechanical vibration data with a sampling rate of 5kHz, electrical parameters of 50Hz, and hydraulic data of 10Hz through the SCADA monitoring and data acquisition system. S4-2. Dynamic weight allocation: A sliding time window algorithm is adopted to assign a weight coefficient of 0.7 to vibration data in bearing failure mode; for lubrication failure scenario, a comprehensive evaluation is carried out by weighting temperature data (0.5) and oil data (0.5). S4-3. Fault Mode Identification: When the concentration of metal particles in the oil is greater than 800ppm and the current harmonic content is greater than 5%, it is determined to be a composite fault mode of lubrication failure accompanied by winding short circuit.

6. The fault early warning system for hydraulic engineering generator sets according to claim 1, characterized in that, S5 specifically includes the following steps: S5-1. Operating condition feature extraction: Based on the flow rate change parameter and the current fluctuation standard deviation index, the operating phase is divided by threshold conditions: Start-up and shutdown phase: activated when the flow rate change exceeds 10 liters per second squared; steady state phase: determined when the current fluctuation standard deviation is lower than 0.05 per unit value. S5-2. Threshold dynamic adjustment: During the start-up and shutdown phase, the vibration alarm threshold is increased to 150% of the normal value, and during the steady-state phase, it is restored to the ISO10816-3 standard threshold. S5-3. Cloud strategy optimization: Based on the historical fault case library, when the traffic drops by 5% for 3 consecutive hours, the sensitivity of the cavitation warning threshold will be automatically increased by 30%.

7. A fault early warning system for hydraulic engineering generator sets according to claim 1, characterized in that, S6 specifically includes the following steps: S6-1. Local alarm location: Through the ZigBee protocol mesh topology, the fault device ID code and physical coordinates are integrated into the audible and visual alarm unit; S6-2. Maintenance Decision Generation: LSTM neural network is used to predict the remaining service life. When the water content of the oil is greater than 500ppm, a "change oil within 72 hours" instruction is pushed. S6-3. Closed-loop verification mechanism: After maintenance, a composite verification test is automatically started, which includes a vibration baseline in the 0.5-2kHz frequency band and a total harmonic distortion (THD) of less than 3%. Specifically, it includes: Vibration baseline verification: In the characteristic frequency band from 0.5kHz to 2kHz, it is detected whether the vibration amplitude of the unit has recovered to the preset baseline range; Current quality verification: The harmonic distortion of the power supply circuit current is measured to ensure that the THD index is less than the industrial standard limit of 3%.

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