Self-priming pump control system

Abstract

The invention relates to the technical field of self-priming pump control systems, in particular to a self-priming pump control system which comprises an intelligent control module, a structure optimization module, an energy management module, a fault prediction module and a self-priming enhancement module. The fault prediction module carries out early warning on equipment faults in advance through vibration spectrum analysis and voiceprint recognition technologies, the maintenance cost is reduced by more than 50%, the comprehensive energy consumption of the system is reduced by 25%-35% through integration of an optical energy storage power supply system and an AI dynamic frequency modulation technology, the dependence on traditional energy is reduced through a photovoltaic power supply system, multi-sensor detection is carried out, and the overall rapid processing effect is improved. And the intelligent control module dynamically optimizes operation parameters based on an AI algorithm, so that unmanned operation is realized.

Description

Technical Field

[0001] The invention relates to the technical field of self-priming pump control systems, and in particular to a self-priming pump control system. Background Art

[0002] A self-priming pump is a centrifugal pump with self-priming ability. It can automatically absorb water through its own structure without installing a bottom valve in the pipeline. The control of the self-priming pump requires a combination of automation technology, mechanical adjustment and electrical parameter optimization. The core lies in balancing pressure, flow and efficiency.

[0003] At present, the water level in the liquid storage tank is monitored by a liquid level sensor, and the pipeline pressure is adjusted by a pressure sensor. Traditional variable frequency speed regulation relies on a single sensor, and the dynamic response is delayed, resulting in energy waste. In addition, excessive impeller clearance or aging of seals can easily lead to self-priming failure, requiring frequent shutdowns for maintenance. It lacks real-time fault prediction and adaptive adjustment capabilities and relies on manual intervention.

[0004] Therefore, traditional variable frequency speed regulation relies on a single sensor, with a dynamic response lag, which leads to energy waste. Excessive impeller clearance or aging of seals can easily lead to self-priming failure, requiring frequent shutdowns for maintenance. It lacks real-time fault prediction and adaptive adjustment capabilities. A self-priming pump control system can be designed. Summary of the invention

[0005] In order to overcome the problems that traditional variable frequency speed regulation relies on a single sensor, has a delayed dynamic response, leads to energy waste, and large impeller clearance or aging of seals easily lead to self-priming failure, requires frequent shutdowns for maintenance, and lacks real-time fault prediction and adaptive adjustment capabilities.

[0006] The technical solution of the present invention is: a self-priming pump control system, including an intelligent control module, a structural optimization module, an energy management module, a fault prediction module and a self-priming enhancement module; an intelligent control module for receiving sensor data and sending instructions to other modules, a structural optimization module for the intelligent control module to link and feedback the sealing status and impeller load status, an energy management module for the intelligent control module to coordinate and connect to provide stable power for the entire system, a fault prediction module for sending maintenance instructions to the intelligent control module and linking the self-priming enhancement module to adjust the operating status, and a self-priming enhancement module for receiving instructions from the intelligent control module to ensure the stability of self-priming.

[0007] Preferably, the intelligent control module includes an edge computing unit, a multi-channel sensor, a vibration detection unit, a voiceprint recognition module and a dual-mode pressure regulating valve. The multi-channel sensor is divided into a liquid level sensor, a pressure sensor and a temperature sensor, which are used to process sensor data in real time and generate control instructions, and dynamically adjust the inverter output frequency. The inverter output frequency range is 40% to 100% of the rated value, and the edge computing unit has a built-in AI algorithm.

[0008] Preferably, the structure optimization module includes a wear-resistant impeller, a magnetic coupling drive device, and a modular pump casing. The clearance between the impeller and the pump casing is dynamically adjusted by an electric push rod to be 0.1 - 0.5 mm. The magnetic coupling drive device adopts a shaft-seal-free design, and the transmission efficiency is ≥ 92%.

[0009] Preferably, the energy management module includes an integrated photovoltaic-storage power supply system and a bidirectional inverter, which preferentially uses clean energy and provides power support for the system. The conversion efficiency of the photovoltaic system is ≥ 97%, and the charge-discharge cycle times of the energy storage system are > 5000 times.

[0010] Preferably, the fault prediction module includes a vibration sensor array, a voiceprint recognition unit, and a machine learning model, which are used to monitor the device status and generate maintenance warnings. The vibration sensor detects the risk of impeller imbalance, and the warning parameter of the vibration sensor is ≥ 5 μm.

[0011] Preferably, the self-priming enhancement module includes a micro water ring vacuum pump, a variable clearance impeller chamber, and a gas-liquid separation optimizer. Through vacuum assistance and clearance adjustment, the self-priming time is shortened to within 30 seconds. When the vacuum pump starts, a negative pressure of ≥ 0.1 MPa is established.

[0012] Preferably, the self-priming enhancement module further includes a starter for controlling the start and stop of the micro water ring vacuum pump and a gas-liquid separation optimizer. A cyclone separator and a float valve monitoring device are provided inside the gas-liquid separation optimizer. The separation efficiency is > 95%, and the liquid level monitoring error is ≤ ±2 mm.

[0013] Preferably, the prediction indicators of the machine learning model in the fault prediction module include the risk of impeller imbalance, the aging cycle of the seal, and the early warning of motor overheating. The specific parameters are that when the abnormal threshold of the vibration spectrum of the impeller imbalance risk is > 5 μm, the prediction error of the seal aging cycle is ≤ ±72 hours, and the temperature gradient of the motor overheating early warning is > 3 °C / minute, the shutdown protection is triggered.

[0014] The present invention also provides a self-priming pump control system, and its usage steps are as follows:

[0015] S1: The intelligent control system of the self-priming pump perceives and collects multi-dimensional data in real time through an integrated multi-channel sensor array. The liquid level sensor monitors the water level of the liquid storage tank. The pressure sensor detects the pipeline pressure within the range of -0.1 - 0.5 MPa. The temperature sensor measures the temperature within the range of -20 - 150 °C to track the temperature of the pump body and the motor. At the same time, the vibration sensor monitors the vibration spectrum of the impeller and the bearing within the frequency response range of 5 Hz - 10 kHz and a sampling rate of 1 kHz. The voiceprint recognition module captures abnormal noises such as cavitation and idling through a microphone array with a signal-to-noise ratio of 70 dB;

[0016] S2: After receiving the sensor data, the edge computing unit performs real-time processing and analysis using the built-in AI algorithm. The AI algorithm dynamically optimizes the output frequency of the frequency converter to 5 - 50 Hz according to the liquid level, pressure, and temperature data, with an adjustment step of 0.1 Hz, to match the real-time working conditions and reduce energy consumption. At the same time, the AI algorithm also predicts equipment failures through vibration spectrum and voiceprint features and generates maintenance warnings. The control instructions are sent to each execution module through the OPC UA protocol to achieve precise control;

[0017] S3: After receiving the control instructions, each execution module responds quickly. The self-priming enhancement module starts the micro water ring vacuum pump with a parameter range of ultimate vacuum -0.095 MPa and pumping speed 12 m3 / h. When the gas content in the suction pipe is detected to be > 30%, it automatically triggers secondary suction, shortening the self-priming time to within 30 seconds. The structure optimization module dynamically adjusts the gap between the impeller and the pump casing through a high-precision electric push rod to adapt to the viscosity of different media and improve the self-priming efficiency and head;

[0018] S4: The energy management module integrates the photovoltaic system into the energy storage power supply system and the bidirectional inverter to provide stable power support for the system. The photovoltaic power supply unit uses monocrystalline silicon solar panels with a photoelectric conversion efficiency of ≥ 23%, giving priority to using clean energy. The energy storage system is a lithium iron phosphate battery pack with an energy density of ≥ 160 Wh / kg, which supplements energy in low light conditions and reduces the dependence on diesel engines by 30% - 50%. The bidirectional inverter, with an efficiency of ≥ 98.5%, realizes power feedback;

[0019] S5: The fault prediction module analyzes the vibration spectrum and voiceprint features based on a machine learning model to predict equipment failures in advance. When risks such as impeller imbalance (vibration spectrum abnormal threshold > 5 μm), seal aging (prediction error ≤ ± 72 hours), or motor overheating (temperature gradient > 3 °C / minute) are predicted, the fault prediction module sends a maintenance warning signal to the intelligent control module. After receiving the warning signal, the intelligent control module triggers corresponding maintenance instructions and pushes maintenance suggestions to the operation and maintenance personnel through the cloud monitoring platform to achieve remote maintenance and management.

[0020] Advantages of the present invention: By combining the intelligent control module, structure optimization module, energy management module, fault prediction module, and self-priming enhancement module, the overall rapid processing effect is enhanced. The fault prediction module uses vibration spectrum analysis and voiceprint recognition technologies to predict equipment failures in advance, reducing the maintenance cost by more than 50%. Through the integration of the photovoltaic energy storage power supply system and AI dynamic frequency modulation technology, the overall system energy consumption is reduced by 25% - 35%. The photovoltaic power supply system reduces the dependence on traditional energy sources. Multi-sensor detection improves the overall monitoring effect. The intelligent control module dynamically optimizes the operating parameters based on the AI algorithm, achieving unmanned operation. Description of the Drawings

[0021] Figure 1 What is shown is a working flow chart of a self-priming pump control system of the present invention. DETAILED DESCRIPTION

[0022] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0023] See also Figure 1 The present invention provides an embodiment: a self-priming pump control system, including an intelligent control module, a structural optimization module, an energy management module, a fault prediction module and a self-priming enhancement module; an intelligent control module for receiving sensor data and sending instructions to other modules, a structural optimization module for the intelligent control module to link and feedback the sealing state and the impeller load state, an energy management module for the intelligent control module to coordinate and connect to provide stable power for the whole system, a fault prediction module for sending maintenance instructions to the intelligent control module and linking the self-priming enhancement module to adjust the operating state, and a self-priming enhancement module for receiving instructions from the intelligent control module to ensure the stability of self-priming.

[0024] Preferably, the intelligent control module includes an edge computing unit, a multi-channel sensor, a vibration detection unit, a voiceprint recognition module and a dual-mode pressure regulating valve. The multi-channel sensor is divided into a liquid level sensor, a pressure sensor and a temperature sensor, which are used to process sensor data in real time and generate control instructions, and dynamically adjust the inverter output frequency. The inverter output frequency range is 40% to 100% of the rated value, and the edge computing unit has a built-in AI algorithm.

[0025] Preferably, the structural optimization module includes a wear-resistant impeller, a magnetic coupling transmission device and a modular pump casing. The gap between the impeller and the pump casing is dynamically adjusted by 0.1 to 0.5 mm through an electric push rod. The magnetic coupling transmission device adopts a shaft seal-free design, and the transmission efficiency is ≥92%.

[0026] Preferably, the energy management module includes an integrated photovoltaic-energy storage power supply system and a bidirectional inverter, which gives priority to the use of clean energy and provides power support for the system. The photovoltaic system conversion efficiency is ≥97%, and the energy storage system charge and discharge cycle number is >5000 times.

[0027] Preferably, the fault prediction module vibration sensor array, voiceprint recognition unit and machine learning model are used to monitor the equipment status and generate maintenance warnings. The vibration sensor detects the risk of impeller imbalance, and the vibration sensor warning parameter is ≥5μm.

[0028] Preferably, the self-priming enhancement module includes a micro water ring vacuum pump, a variable clearance impeller chamber and a gas-liquid separation optimizer. The self-priming time is shortened to within 30 seconds through vacuum assistance and clearance adjustment, and a negative pressure of ≥ 0.1 MPa is established when the vacuum pump is started.

[0029] Preferably, the self-priming enhancement module further includes a starter for controlling the start and stop of the micro water ring vacuum pump and a gas-liquid separation optimizer. The gas-liquid separation optimizer is internally provided with a cyclone separator and a float valve monitoring device, with a separation efficiency > 95% and a liquid level monitoring error ≤ ±2 mm.

[0030] Preferably, the prediction indicators of the machine learning model of the fault prediction module include the risk of impeller imbalance, the aging cycle of the seal, and the early warning of motor overheating. The specific parameters are that when the abnormal threshold of the vibration spectrum of the impeller imbalance risk > 5 μm, the prediction error of the seal aging cycle ≤ ±72 hours, and the temperature gradient of the motor overheating warning > 3 °C / minute, the shutdown protection is triggered.

[0031] The present invention also provides a self-priming pump control system, and its usage steps are as follows:

[0032] S1: The intelligent control system of the self-priming pump uses the integrated multi-channel sensor array to sense and collect multi-dimensional data in real time. The liquid level sensor monitors the water level of the liquid storage tank, the pressure sensor detects the pipeline pressure in the range of -0.1 to 0.5 MPa, the temperature sensor measures the temperature in the range of -20 to 150 °C to track the temperature of the pump body and the motor. At the same time, the vibration sensor monitors the vibration spectrum of the impeller and the bearing in the frequency response range of 5 Hz to 10 kHz and a sampling rate of 1 kHz, and the acoustic fingerprint recognition module captures abnormal noises such as cavitation and idling with a microphone array with a signal-to-noise ratio of 70 dB;

[0033] S2: After receiving the sensor data, the edge computing unit uses the built-in AI algorithm for real-time processing and analysis. The AI algorithm dynamically optimizes the output frequency of the frequency converter to 5 - 50 Hz according to the liquid level, pressure, and temperature data, with an adjustment step of 0.1 Hz, to match the real-time working condition requirements and reduce energy consumption. At the same time, the AI algorithm also predicts equipment failures through the vibration spectrum and acoustic fingerprint features and generates maintenance warnings. The control instructions are sent to each execution module through the OPC UA protocol to achieve precise control;

[0034] S3: After receiving the control instructions, each execution module responds quickly. The self-priming enhancement module starts the micro water ring vacuum pump with parameters of an ultimate vacuum degree of -0.095 MPa and a pumping speed of 12 m3 / h, and automatically triggers secondary suction when the gas content in the suction pipe > 30%, shortening the self-priming time to within 30 seconds. The structure optimization module dynamically adjusts the gap between the impeller and the pump shell through a high-precision electric push rod to adapt to the viscosity of different media and improve the self-priming efficiency and lift;

[0035] S4: The energy management module, through integrating photovoltaic power generation, energy storage power supply system and bidirectional inverter, provides stable power support for the system. The photovoltaic power supply unit uses monocrystalline silicon solar panels with a photoelectric conversion efficiency of ≥23%. Clean energy is preferentially used. The energy storage system is a lithium iron phosphate battery pack with an energy density of ≥160 Wh / kg, which supplements energy in low light conditions and reduces the dependence on diesel engines by 30% - 50%. The bidirectional inverter has an efficiency of ≥98.5% to achieve power feedback;

[0036] S5: The fault prediction module analyzes the vibration spectrum and acoustic fingerprint features based on a machine learning model to early warn of equipment faults. When risks such as impeller imbalance (abnormal vibration spectrum threshold > 5μm), seal aging (prediction error ≤ ±72 hours), or motor overheating (temperature gradient > 3°C / minute) are predicted, the fault prediction module will send a maintenance warning signal to the intelligent control module. After receiving the warning signal, the intelligent control module will trigger corresponding maintenance instructions and push maintenance suggestions to the operation and maintenance personnel through the cloud monitoring platform to achieve remote maintenance and management.

[0037] The embodiments of the present invention have been described in detail above in conjunction with the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the gist of the present invention.

Claims

1. A self-priming pump control system, comprising an intelligent control module; characterized in that: It also includes a structural optimization module, an energy management module, a fault prediction module and a self-priming enhancement module; an intelligent control module for receiving sensor data and sending instructions to other modules, a structural optimization module for linking the intelligent control modules to feedback the sealing status and impeller load status, an energy management module for collaboratively connecting the intelligent control modules to provide stable power for the entire system, a fault prediction module for sending maintenance instructions to the intelligent control modules and linking the self-priming enhancement module to adjust the operating status, and a self-priming enhancement module for receiving instructions from the intelligent control module to ensure the stability of self-priming.

2. A self-priming pump control system according to claim 1, characterized in that: The intelligent control module includes an edge computing unit, a multi-channel sensor, a vibration detection unit, a voiceprint recognition module and a dual-mode pressure regulating valve. The multi-channel sensor is divided into a liquid level sensor, a pressure sensor and a temperature sensor, which are used to process sensor data in real time and generate control instructions, and dynamically adjust the inverter output frequency. The inverter output frequency range is 40% to 100% of the rated value. The edge computing unit has a built-in AI algorithm.

3. A self-priming pump control system according to claim 1, characterized in that: The structural optimization module includes a wear-resistant impeller, a magnetic coupling transmission device and a modular pump casing. The gap between the impeller and the pump casing is dynamically adjusted by 0.1 to 0.5 mm through an electric push rod. The magnetic coupling transmission device adopts a shaft seal-free design, and the transmission efficiency is ≥92%.

4. A self-priming pump control system according to claim 1, characterized in that: Energy management module package - with integrated photovoltaic-energy storage power supply system and bidirectional inverter, giving priority to the use of clean energy and providing power support for the system, the photovoltaic system conversion efficiency is ≥97%, and the energy storage system charge and discharge cycle number is >5000 times.

5. A self-priming pump control system according to claim 1, characterized in that: The fault prediction module vibration sensor array, voiceprint recognition unit and machine learning model are used to monitor the equipment status and generate maintenance warnings. The vibration sensor detects the risk of impeller imbalance, and the vibration sensor warning parameter is ≥5μm.

6. A self-priming pump control system according to claim 1, characterized in that: The self-priming enhancement module includes a micro water ring vacuum pump, a variable clearance impeller chamber and a gas-liquid separation optimizer. It shortens the self-priming time to within 30 seconds through vacuum assistance and clearance adjustment, and establishes a negative pressure of ≥ 0.1MPa when the vacuum pump is started.

7. A self-priming pump control system according to claim 1, characterized in that: The self-priming enhancement module includes a starter and stop device for controlling the micro water ring vacuum pump and a gas-liquid separation optimizer. The gas-liquid separation optimizer is internally provided with a cyclone separator and a float valve monitoring device. The separation efficiency is greater than 95%, and the liquid level monitoring error is ≤±2mm.

8. A self-priming pump control system according to claim 5, characterized in that: The machine learning model prediction indicators of the fault prediction module include impeller imbalance risk, seal aging cycle and motor overheating warning. The specific parameters are impeller imbalance risk vibration spectrum abnormal threshold >5μm, seal aging cycle prediction error ≤±72 hours and motor overheating warning temperature gradient >3℃ / minute to trigger shutdown protection.

9. A self-priming pump control system according to claim 1, characterized in that: The steps for use are as follows: S1: The self-priming pump intelligent control system senses and collects multi-dimensional data in real time through an integrated multi-channel sensor array. The liquid level sensor monitors the water level in the liquid storage tank, the pressure sensor parameter range is -0.1~0.5MPa range to detect the pipeline pressure, the temperature sensor parameter range is -20~150℃ to measure the temperature and track the temperature of the pump body and motor. At the same time, the vibration sensor parameter range is 5Hz~10kHz frequency response range, 1kHz sampling rate to monitor the vibration spectrum of the impeller and bearing, the voiceprint recognition module parameter range is 70dB signal-to-noise ratio microphone array to capture abnormal noise such as cavitation and idling; S2: After receiving the sensor data, the edge computing unit uses the built-in AI algorithm for real-time processing and analysis. The AI ​​algorithm dynamically optimizes the inverter output frequency to 5-50Hz based on the liquid level, pressure and temperature data, with an adjustment step of 0.1Hz to match the real-time working conditions and reduce energy consumption. At the same time, the AI ​​algorithm also predicts equipment failures through vibration spectrum and soundprint characteristics and generates maintenance warnings. Control instructions are sent to each execution module through the OPC UA protocol to achieve precise control; S3: After receiving the control command, each execution module responds quickly. The self-priming enhancement module starts the micro water ring vacuum pump with a parameter range of -0.095MPa ultimate vacuum and a pumping speed of 12m3 / h. When the gas content in the suction pipe is detected to be greater than 30%, secondary suction is automatically triggered, shortening the self-priming time to within 30 seconds. The structural optimization module dynamically adjusts the gap between the impeller and the pump casing through a high-precision electric push rod to adapt to the viscosity of different media and improve self-priming efficiency and head. S4: The energy management module integrates photovoltaics, energy storage and power supply systems, and bidirectional inverters to provide stable power support for the system. Photovoltaic power supply is provided by monocrystalline silicon panels with a photoelectric conversion efficiency of ≥23%. Clean energy is used first. The energy storage system is a lithium iron phosphate battery pack with an energy density of ≥160Wh / kg. It can replenish energy in low light conditions and reduce diesel engine dependence by 30% to 50%. The bidirectional inverter has an efficiency of ≥98.5% to achieve power feedback. S5: The fault prediction module analyzes the vibration spectrum and soundprint features based on the machine learning model to warn of equipment failures in advance. When risks such as impeller imbalance, vibration spectrum abnormality threshold > 5μm, seal aging, prediction error ≤ ±72 hours, motor overheating, temperature gradient > 3℃ / minute are predicted, the fault prediction module will send a maintenance warning signal to the intelligent control module. After receiving the warning signal, the intelligent control module will trigger the corresponding maintenance instructions and push maintenance suggestions to the operation and maintenance personnel through the cloud monitoring platform to achieve remote maintenance and management.

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