A fan motor fault and energy consumption integrated monitoring circuit based on edge cloud

Abstract

The utility model discloses a fan motor fault and energy consumption integration monitoring circuit based on edge cloud, including main control unit circuit, fault detection module, energy consumption monitoring module, communication module, power module, main control unit circuit has core processor, fault detection module includes current monitoring circuit, vibration monitoring circuit and temperature monitoring circuit, each circuit all has sensor, and each sensor output signal inputs main control unit, energy consumption monitoring module includes voltage sampling circuit, power calculation unit and electric energy cumulative unit, fault detection module, energy consumption monitoring module is connected with main control unit circuit respectively, communication module is connected with main control unit circuit, and power module provides operating power for each module and circuit respectively. The utility model realizes motor state real -time monitoring, early fault early warning and energy consumption accurate statistics, and the motor equipment operation reliability and efficiency have been improved significantly.

Description

Technical Field

[0001] This utility model relates to the field of industrial equipment monitoring technology, specifically to an integrated monitoring circuit for wind turbine motor faults and energy consumption based on edge cloud computing. Background Technology

[0002] In the monitoring of wind turbine motors, their importance is mainly reflected in their impact on production processes and emission compliance. On the one hand, the stability of the wind turbine motor's operating status directly affects the smoothness of the entire production process. On the other hand, the operating status of the wind turbine motor also directly affects whether emissions meet standards.

[0003] However, during long-term operation, wind turbine motors are inevitably susceptible to malfunctions due to various factors. Mechanically, prolonged operation causes wear on components such as bearings and gears, leading to increased vibration and noise, and in severe cases, even equipment seizure. Misalignment of the coupling and rotor imbalance can also cause mechanical failures, affecting motor performance and lifespan. Electrically, motor windings may be damaged due to overheating, moisture, or short circuits, causing the motor to fail to start or operate unstablely. Furthermore, fluctuations in power supply voltage and harmonic interference can also adversely affect the normal operation of the motor.

[0004] Besides malfunctions, the energy consumption of fan motors is also an important parameter in industrial production. Reducing the energy consumption of fan motors and accurately monitoring energy consumption can greatly improve energy utilization efficiency.

[0005] In addition, existing wind turbine motor monitoring systems have limited functionality and low integration, making it impossible to achieve real-time status monitoring, early fault warning, and precise energy consumption monitoring of wind turbine motors. Utility Model Content

[0006] To address the aforementioned issues, this invention provides an integrated monitoring circuit for wind turbine motor faults and energy consumption based on edge-cloud computing. This circuit can perform real-time and accurate fault diagnosis and energy consumption monitoring of wind turbine motors. It can not only promptly detect potential faults in wind turbine motors but also accurately monitor the energy consumption of the motors.

[0007] This utility model provides an integrated monitoring circuit for wind turbine motor faults and energy consumption based on edge-cloud architecture. It includes a main control unit circuit, a fault detection module, an energy consumption monitoring module, a communication module, and a power supply module. The main control unit circuit has a core processor equipped with 64KB Flash memory, 4KB RAM, a 12-bit ADC, and SPI, I2C, and UART communication interfaces. The fault detection module includes a current monitoring circuit, a vibration monitoring circuit, and a temperature monitoring circuit, each with its own sensor. The output signals from each sensor are input to the main control unit. The energy consumption monitoring module includes a voltage sampling circuit, a power calculation unit, and an energy accumulation unit. The fault detection module and the energy consumption monitoring module are connected to the main control unit circuit, as is the communication module. The power supply module provides operating power to each module and circuit.

[0008] Furthermore, the core processor is an STC8H3K64S2-TSSOP20 chip with a working frequency of 40MHz and a task scheduling cycle of 10ms.

[0009] Furthermore, the current monitoring circuit of the fault detection module adopts the Hall current sensor ACS758. The Hall current sensor has a measurement range of ±50A and an accuracy of ±1.5%. The output signal is processed by an RC low-pass filter with a cutoff frequency of 10kHz.

[0010] Furthermore, the vibration monitoring circuit of the fault detection module adopts a triaxial MEMS accelerometer ADXL345. The accelerometer has a measurement range of ±16g, a resolution of 13 bits, and a sampling frequency of 1kHz. It communicates with the main control unit through an I2C interface at a communication rate of 400kHz.

[0011] Furthermore, the temperature monitoring circuit of the fault detection module uses a DS18B20 digital temperature sensor.

[0012] Furthermore, the voltage sampling circuit of the energy consumption monitoring module includes a resistor divider network and an operational amplifier.

[0013] Furthermore, the power module includes an AC-DC conversion unit and a DC-DC conversion unit. The AC-DC conversion unit uses a TOP258PN control chip, and the DC-DC conversion unit uses an LM2576 and an AMS1117-3.3 voltage regulator.

[0014] Furthermore, the AC-DC conversion unit has an input voltage range of 85V-264VAC and an output of +12VDC / 1A. It adopts a flyback topology and has a conversion efficiency of >80%. The DC-DC conversion unit converts +12VDC to +5VDC / 1A and +3.3VDC / 500mA, and each output terminal is equipped with an LC filter circuit.

[0015] Furthermore, the communication module integrates a 4G communication unit, the 4G communication unit adopts an ESP8266 module, and the communication module adopts a data priority scheduling mode.

[0016] Furthermore, the monitoring circuit adopts a four-layer PCB design, with the top layer being the signal layer, the bottom layer being the ground layer, and the middle two layers being the power plane and the signal plane. The critical signal traces adopt a differential routing method with a trace width between 5mil and 20mil.

[0017] This utility model's edge-cloud-based integrated monitoring circuit for wind turbine motor faults and energy consumption achieves real-time status monitoring, early fault warning, and accurate energy consumption calculation for wind turbine motors through a multi-sensor fusion circuit and module.

[0018] This utility model presents an edge-cloud-based integrated monitoring circuit for wind turbine motor faults and energy consumption. It deeply integrates fault detection and energy consumption monitoring functions, achieving comprehensive status perception, fault diagnosis, and energy efficiency statistics for wind turbine motors through multi-sensor data fusion and embedded algorithms. It features integrated multi-parameter monitoring, early fault warning, accurate energy consumption statistics, and wireless interconnection capabilities, significantly improving the operational reliability and energy utilization efficiency of wind turbine motors. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this utility model or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this utility model. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 The block diagram of the vibration signal acquisition circuit provided by this utility model;

[0021] Figure 2 A block diagram of a 4G signal communication circuit provided by this utility model;

[0022] Figure 3 The block diagram of the MCU processing circuit provided by this utility model;

[0023] Figure 4 Block diagram of voltage, current, and analog voltage acquisition circuit provided by this utility model;

[0024] Figure 5 The power supply circuit block diagram provided by this utility model;

[0025] Figure 6 The overall block diagram of the integrated monitoring circuit provided by this utility model. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0027] In the description of this utility model, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the accompanying drawings and are only for the convenience of describing this utility model and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this utility model. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0028] This utility model provides an integrated monitoring circuit for wind turbine motor faults and energy consumption based on edge cloud computing. The monitoring circuit uses the STC8H3K64S2-TSSOP20 high-performance microcontroller as the core processor, with built-in 64KB Flash memory, 4KB RAM and rich peripheral interfaces. The operating frequency can reach 40MHz and it can process multiple sensor data simultaneously.

[0029] The STC8H3K64S2-TSSOP20 chip in the main control unit circuit realizes multi-task parallel processing such as fault feature extraction, energy consumption calculation, and communication protocol conversion through task scheduling algorithm. The main control unit circuit is used to realize multi-task parallel processing.

[0030] Furthermore, in terms of fault detection, the circuit integrates three subsystems: current monitoring, vibration monitoring, and temperature monitoring. Current monitoring uses the ACS758 Hall effect current sensor, with a measurement range of ±50A and an accuracy of ±1.5%, capable of detecting motor starting current, running current, and short-circuit faults. Vibration monitoring uses the ADXL345 triaxial MEMS accelerometer, with a measurement range of ±16g and a 13-bit resolution, capable of detecting mechanical faults such as motor bearing wear and rotor imbalance. Temperature monitoring uses the DS18B20 digital temperature sensor, with a measurement range of -55℃ to +125℃ and an accuracy of ±0.5℃, capable of detecting abnormal motor winding and bearing temperatures.

[0031] Furthermore, in terms of energy consumption monitoring, the circuit collects voltage signals through a resistor voltage divider network, combines them with current sensor data, and uses synchronous sampling technology to perform 1024-point synchronous sampling of voltage and current signals. It calculates the fundamental power, harmonic power and power factor through discrete Fourier transform (DFT), and uses a digital integration algorithm to realize the energy accumulation function.

[0032] Furthermore, the power calculation unit of the energy consumption monitoring module is implemented based on the discrete Fourier transform algorithm, and the energy accumulation unit adopts the digital integration algorithm.

[0033] Furthermore, the communication module integrates 4G communication, enabling remote wireless data transmission. Fault alarm information from the communication module has a higher priority than regular monitoring data; when a serious fault is detected, the system automatically switches to the highest communication priority to ensure real-time transmission of alarm information.

[0034] Furthermore, the power supply module adopts a flyback switching power supply topology with an input voltage range of 85V to 264VAC and an output of +12VDC / 1A. The +5VDC and +3.3VDC voltages are generated by the LM2576 and AMS1117-3.3 regulators, respectively, to power the various modules of the system.

[0035] The monitoring using the above-mentioned edge-cloud based integrated monitoring circuit for wind turbine motor faults and energy consumption includes the following steps:

[0036] 1. Power module workflow:

[0037] (1) The AC input voltage is converted into a pulsating DC voltage after being suppressed by the EMI filters (LF1, C1, C2) to suppress common-mode and differential-mode interference;

[0038] (2) The pulsating DC voltage is smoothed by the main filter capacitor (C3 = 470μF / 400V) and then applied to the DRAIN pin of the TOP258PN control chip;

[0039] (3) The TOP258PN achieves energy conversion through a high-frequency transformer (T1), and the secondary output obtains a stable +12VDC output through a rectifier diode (D2), a filter inductor (L1) and an output capacitor (C5);

[0040] (4) The +12VDC voltage enters the LM2576 and AMS1117-3.3 regulators respectively, generating +5VDC and +3.3VDC voltages to power the subsequent circuits.

[0041] 2. Fault detection module workflow:

[0042] (1) The ACS758 current sensor monitors the motor current in real time and outputs a current signal of ±50A corresponding to 0~5V;

[0043] (2) After the high-frequency noise is filtered out by the RC low-pass filter (R10=10kΩ, C10=0.1μF), the signal enters the ADC0 channel of the main control unit.

[0044] (3) The ADXL345 accelerometer sensor collects motor vibration signals at a sampling rate of 1kHz and transmits the data to the main control unit through the I2C interface (SDA, SCL);

[0045] (4) The DS18B20 temperature sensor collects the motor temperature every second and sends the digital temperature value to the main control unit via a single bus (DQ).

[0046] (5) The main control unit performs fusion analysis on the above multidimensional data and judges the motor operating status by comparing thresholds, spectrum analysis and other methods.

[0047] 3. Energy consumption monitoring module workflow:

[0048] (1) The voltage sampling circuit converts the 380V line voltage into a 0-5V signal and inputs it into the ADC1 channel of the main control unit;

[0049] (2) The main control unit synchronously samples the voltage and current signals at a sampling rate of 10kHz and stores them in the internal FIFO buffer.

[0050] (3) When the buffer data reaches 1024 points, the DFT calculation task is triggered to calculate the active power, reactive power, apparent power and power factor.

[0051] (4) Update the power value every second and perform integral calculation on the power to accumulate the energy consumption;

[0052] (5) The power data is stored in EEPROM (AT24C02) in hours, with a storage period of 10 years.

[0053] 4. Communication module workflow:

[0054] (1) The 4G module connects to the preset 4G network and establishes a TCP connection to the cloud server during system initialization;

[0055] The installation and commissioning of the above-mentioned edge-cloud-based integrated monitoring circuit for wind turbine motor faults and energy consumption includes the following steps:

[0056] 1. Installation steps:

[0057] (1) Place the current sensor on the motor power supply line and ensure that the current direction is consistent with the sensor marking;

[0058] (2) Fix the vibration sensor to the bearing part of the motor housing using magnetic or adhesive methods;

[0059] (3) Place the temperature sensor probe tightly against the surface of the motor windings or bearings and fix it with thermally conductive silicone.

[0060] (4) Install the monitoring circuit module in the motor control cabinet and connect the power supply, sensor and communication line through the terminal block;

[0061] (5) After checking that the wiring is correct, turn on the power and observe the status of the circuit working indicator light.

[0062] 2. Debugging process:

[0063] (1) Use a serial port debugging assistant to connect to the main control unit via the TTL interface, set the baud rate to 115200bps, and view the system startup information;

[0064] (2) Configure 4G network parameters, cloud server IP address and port number using configuration commands;

[0065] (3) Simulate different load conditions of the motor to verify the accuracy of current, voltage and power measurements;

[0066] (4) Use a vibration simulator to generate vibration signals of different frequencies to test the sensitivity of the fault diagnosis algorithm;

[0067] (5) Access monitoring data remotely via mobile APP to verify remote communication function.

[0068] 3. System calibration method:

[0069] (1) Current calibration: Use a standard ammeter to measure the actual current value, compare it with the value displayed by the monitoring circuit, and calibrate it by software correction factor;

[0070] (2) Voltage calibration: Use a high-precision multimeter to measure the actual voltage value and adjust the voltage divider resistor ratio to make the measurement error <±0.5%;

[0071] (3) Power calibration: Using a standard power meter as a reference, adjust the phase compensation parameters in the power calculation algorithm to make the power measurement error <±1%;

[0072] (4) Temperature calibration: Place the temperature sensor and the standard thermometer in the same constant temperature environment, compare the measured values, and correct the temperature compensation coefficient.

[0073] The above description of the disclosed embodiments enables those skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An integrated edge-cloud-based wind turbine motor fault and energy consumption monitoring circuit, comprising a main control unit circuit, a fault detection module, an energy consumption monitoring module, a communication module, and a power supply module, characterized in that, The main control unit circuit has a core processor, which is equipped with 64KB Flash memory, 4KB RAM, a 12-bit ADC, and SPI, I2C, and UART communication interfaces. The fault detection module includes a current monitoring circuit, a vibration monitoring circuit, and a temperature monitoring circuit. Each circuit has a sensor, and the output signal of each sensor is input to the main control unit. The energy consumption monitoring module includes a voltage sampling circuit, a power calculation unit, and an energy accumulation unit. The fault detection module and the energy consumption monitoring module are respectively connected to the main control unit circuit. The communication module is also connected to the main control unit circuit. The power supply module provides operating power to each module and circuit.

2. The integrated monitoring circuit for wind turbine motor faults and energy consumption based on edge-cloud as described in claim 1, characterized in that, The core processor is an STC8H3K64S2-TSSOP20 chip with a working frequency of 40MHz and a task scheduling cycle of 10ms.

3. The integrated monitoring circuit for wind turbine motor faults and energy consumption based on edge-cloud as described in claim 1, characterized in that, The current monitoring circuit of the fault detection module uses a Hall current sensor ACS758. The Hall current sensor has a measurement range of ±50A and an accuracy of ±1.5%. The output signal is processed by an RC low-pass filter with a cutoff frequency of 10kHz.

4. The integrated monitoring circuit for wind turbine motor faults and energy consumption based on edge-cloud as described in claim 1, characterized in that, The vibration monitoring circuit of the fault detection module uses a triaxial MEMS accelerometer ADXL345. The accelerometer has a measurement range of ±16g, a resolution of 13 bits, and a sampling frequency of 1kHz. It communicates with the main control unit via an I2C interface at a communication rate of 400kHz.

5. The integrated monitoring circuit for wind turbine motor faults and energy consumption based on edge-cloud as described in claim 1, characterized in that, The temperature monitoring circuit of the fault detection module uses a DS18B20 digital temperature sensor.

6. The integrated monitoring circuit for wind turbine motor faults and energy consumption based on edge-cloud as described in claim 1, characterized in that, The voltage sampling circuit of the energy consumption monitoring module includes a resistor divider network and an operational amplifier.

7. The integrated monitoring circuit for wind turbine motor faults and energy consumption based on edge-cloud as described in claim 1, characterized in that, The power module includes an AC-DC conversion unit and a DC-DC conversion unit. The AC-DC conversion unit uses a TOP258PN control chip, and the DC-DC conversion unit uses an LM2576 and an AMS1117-3.3 voltage regulator.

8. The integrated monitoring circuit for wind turbine motor faults and energy consumption based on edge cloud as described in claim 7, characterized in that, The AC-DC conversion unit has an input voltage range of 85V-264VAC and an output of +12VDC / 1A. It adopts a flyback topology and has a conversion efficiency of >80%. The DC-DC conversion unit converts +12VDC to +5VDC / 1A and +3.3VDC / 500mA, and each output terminal is equipped with an LC filter circuit.

9. The integrated monitoring circuit for wind turbine motor faults and energy consumption based on edge-cloud as described in claim 1, characterized in that, The communication module integrates a 4G communication unit, which uses an ESP8266 module, and the communication module adopts a data priority scheduling mode.

10. The integrated monitoring circuit for wind turbine motor faults and energy consumption based on edge-cloud as described in claim 1, characterized in that, The monitoring circuit adopts a four-layer PCB design, with the top layer being the signal layer, the bottom layer being the ground layer, and the middle two layers being the power plane and the signal plane. The critical signal traces adopt a differential routing method with a trace width between 5mil and 20mil.

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