Three-phase asynchronous motor

Abstract

This invention relates to a three-phase asynchronous motor, comprising a three-phase asynchronous motor body, a phase loss detection mechanism, an environment input mechanism, a configuration detection mechanism, an intelligent identification device, a learning execution device, and a wireless transmission interface. The phase loss state burnout data identification system of this invention for three-phase asynchronous motors is intelligent in operation and widely applicable, providing crucial data for the equipment management and maintenance of three-phase asynchronous motors.

Description

Technical Field

[0001] This invention relates to the field of three-phase asynchronous motors, and more particularly to a three-phase asynchronous motor. Background Technology

[0002] A three-phase asynchronous motor is a type of induction motor that is powered by three-phase 380V alternating current (with a 120-degree phase difference). Because the rotor and stator rotating magnetic fields of a three-phase asynchronous motor rotate in the same direction but at different speeds, there is slip, hence the name three-phase asynchronous motor. The rotor speed of a three-phase asynchronous motor is lower than the speed of the rotating magnetic field. The rotor windings generate electromotive force and current due to the relative motion between them and the magnetic field, and interact with the magnetic field to produce electromagnetic torque, thus achieving energy conversion.

[0003] There is a certain time interval between the occurrence of a phase loss abnormality in a three-phase asynchronous motor and the actual time when the three-phase asynchronous motor burns out. When the time interval is determined, various controls can be performed on the three-phase asynchronous motor to avoid the burnout. Obviously, the existing technology lacks a specific analytical mechanism for the time interval between the current time of the abnormal state of the three-phase asynchronous motor and the actual burnout. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides a three-phase asynchronous motor that intelligently identifies the time interval between the current moment of the abnormal state and the actual burnout scenario only when the three-phase asynchronous motor exhibits a phase loss anomaly. This avoids excessive and unnecessary energy consumption across the entire system. Crucially, based on a convolutional neural network model, the burnout time interval of the three-phase asynchronous motor is intelligently identified according to the noise amplitude of the motor's environment, the motor's overall vibration data, its current operating current, and various configuration information. The burnout time interval is timed from the current moment to the moment of burnout, thus enabling intelligent identification of the time interval between the current moment of the abnormal state and the actual burnout scenario, providing critical data for equipment management and maintenance.

[0005] According to the present invention, a three-phase asynchronous motor is provided, comprising a three-phase asynchronous motor body and a phase loss state burnout data identification system connected to the three-phase asynchronous motor body, the phase loss state burnout data identification system comprising:

[0006] A phase loss detection mechanism is connected to a three-phase asynchronous motor to detect whether the three-phase asynchronous motor has a phase loss. When a phase loss is found, an analysis start command is issued, and when no phase loss is found, an analysis end command is issued.

[0007] An environmental data entry mechanism, connected to the phase loss detection mechanism, is used to start recording the noise amplitude of the environment where the three-phase asynchronous motor is located at the current moment, the overall vibration data of the three-phase asynchronous motor, and the current operating current of the three-phase asynchronous motor when the analysis start command is received.

[0008] A detection mechanism is configured and connected to the three-phase asynchronous motor to obtain multiple configuration information of the three-phase asynchronous motor, including the motor's volume data, weight data, rated speed, power factor, maximum torque, and output power.

[0009] The intelligent identification device is connected to the phase loss detection mechanism, the environment recording mechanism, and the configuration detection mechanism, respectively. It is used to enter the working mode when receiving the analysis start command and enter the sleep mode when receiving the analysis receive command. When in the working mode, it intelligently identifies the burnout time interval of the three-phase asynchronous motor based on the noise amplitude of the environment where the three-phase asynchronous motor is located, the overall vibration data of the three-phase asynchronous motor, the current operating current of the three-phase asynchronous motor, and multiple configuration information of the three-phase asynchronous motor, based on the convolutional neural network model. The burnout time interval is calculated with the current time as the starting point and the burnout time as the ending point.

[0010] A learning execution device is connected to the intelligent identification device and is used to supply the convolutional neural network model to the intelligent identification device. The convolutional neural network model is a convolutional neural network after a set number of learning iterations. The value of the set number is proportional to the volume data of the three-phase asynchronous motor.

[0011] A wireless transmission interface, connected to the intelligent identification device, is used to package the received burn time interval and the current time together and send them to a remote device management server.

[0012] The process of packaging the received burn time interval and the current time together and sending them to the remote device management server includes: the remote device management server being a big data server or a blockchain server.

[0013] Therefore, the present invention has at least the following beneficial technical effects:

[0014] Technical Effect A: A convolutional neural network model for intelligent identification of the burnout time interval of a three-phase asynchronous motor is established. The convolutional neural network model is a convolutional neural network after a set number of learning iterations. The value of the set number is proportional to the volume data of the three-phase asynchronous motor, thereby improving the reliability and effectiveness of the intelligent identification results of the convolutional neural network model.

[0015] Technical Effect B: Based on a convolutional neural network model, the burnout time interval of a three-phase asynchronous motor is intelligently identified according to the noise amplitude of the environment in which the three-phase asynchronous motor is located, the overall vibration data of the three-phase asynchronous motor, the current operating current of the three-phase asynchronous motor, and multiple configuration information of the three-phase asynchronous motor. The burnout time interval is calculated with the current moment as the starting point and the burnout moment as the ending point, thereby realizing the intelligent identification of the time interval between the current moment of the three-phase asynchronous motor in an abnormal state and the actual burnout scenario, providing key data for equipment management and maintenance.

[0016] Technical effect C: Intelligent identification of the time interval between the current moment of the abnormal state of the three-phase asynchronous motor and the actual burnout scenario is triggered only when the three-phase asynchronous motor has a phase loss abnormal state, thereby avoiding excessive and unnecessary energy consumption of the entire system.

[0017] The three-phase asynchronous motor burnout data identification system of the present invention is intelligent in operation and widely applicable. Because it only triggers intelligent identification of the time interval between the current moment of the abnormal state and the actual burnout scenario when the three-phase asynchronous motor is in an abnormal state of phase loss, and simultaneously intelligently identifies the burnout time interval of the three-phase asynchronous motor based on a convolutional neural network model, it provides crucial data for the equipment management and maintenance of three-phase asynchronous motors. Attached Figure Description

[0018] The embodiments of the present invention will now be described with reference to the accompanying drawings, wherein:

[0019] Figure 1 This is a schematic diagram of the internal structure of a three-phase asynchronous motor burnout data identification system according to embodiment A of the present invention.

[0020] Figure 2 This is a schematic diagram of the internal structure of a three-phase asynchronous motor burnout data identification system according to embodiment B of the present invention.

[0021] Figure 3 This is a schematic diagram of the internal structure of a three-phase asynchronous motor burnout data identification system according to embodiment C of the present invention. Detailed Implementation

[0022] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0023] This invention provides a three-phase asynchronous motor, including a three-phase asynchronous motor body and a phase loss state burnout data identification system connected to the three-phase asynchronous motor body. Here, the three-phase asynchronous motor body can be understood as a conventional three-phase asynchronous motor body in this field, which will be understood by those skilled in the art, and will not be described further.

[0024] Figure 1 This is a schematic diagram of the internal structure of a three-phase asynchronous motor burnout data identification system according to embodiment A of the present invention. The system includes:

[0025] A phase loss detection mechanism is connected to a three-phase asynchronous motor to detect whether the three-phase asynchronous motor has a phase loss. When a phase loss is found, an analysis start command is issued, and when no phase loss is found, an analysis end command is issued.

[0026] For example, a phase loss detection mechanism connected to a three-phase asynchronous motor is used to detect whether the three-phase asynchronous motor has a phase loss, and to issue an analysis start command when a phase loss exists and an analysis end command when no phase loss exists. This includes: optionally using an FPGA chip to implement the phase loss detection mechanism, connected to the three-phase asynchronous motor, for detecting whether the three-phase asynchronous motor has a phase loss, and to issue an analysis start command when a phase loss exists and an analysis end command when no phase loss exists;

[0027] An environmental data entry mechanism, connected to the phase loss detection mechanism, is used to start recording the noise amplitude of the environment where the three-phase asynchronous motor is located at the current moment, the overall vibration data of the three-phase asynchronous motor, and the current operating current of the three-phase asynchronous motor when the analysis start command is received.

[0028] A detection mechanism is configured and connected to the three-phase asynchronous motor to obtain multiple configuration information of the three-phase asynchronous motor, including the motor's volume data, weight data, rated speed, power factor, maximum torque, and output power.

[0029] The intelligent identification device is connected to the phase loss detection mechanism, the environment recording mechanism, and the configuration detection mechanism, respectively. It is used to enter the working mode when receiving the analysis start command and enter the sleep mode when receiving the analysis receive command. When in the working mode, it intelligently identifies the burnout time interval of the three-phase asynchronous motor based on the noise amplitude of the environment where the three-phase asynchronous motor is located, the overall vibration data of the three-phase asynchronous motor, the current operating current of the three-phase asynchronous motor, and multiple configuration information of the three-phase asynchronous motor, based on the convolutional neural network model. The burnout time interval is calculated with the current time as the starting point and the burnout time as the ending point.

[0030] A learning execution device is connected to the intelligent identification device and is used to supply the convolutional neural network model to the intelligent identification device. The convolutional neural network model is a convolutional neural network after a set number of learning iterations. The value of the set number is proportional to the volume data of the three-phase asynchronous motor.

[0031] A wireless transmission interface, connected to the intelligent identification device, is used to package the received burn time interval and the current time together and send them to a remote device management server.

[0032] Among them, the process of packaging the received burn time interval and the current time together and sending them to the remote device management server includes: the remote device management server being a big data server or a blockchain server;

[0033] The intelligent identification device is also used to temporarily suspend the intelligent identification of the burnout time interval of the three-phase asynchronous motor based on the noise amplitude of the environment where the three-phase asynchronous motor is located, the overall vibration data of the three-phase asynchronous motor, the current operating current of the three-phase asynchronous motor, and multiple configuration information of the three-phase asynchronous motor when it is in sleep mode.

[0034] The environmental input mechanism is also used to stop inputting the noise amplitude of the environment where the three-phase asynchronous motor is located, the overall vibration data of the three-phase asynchronous motor, and the current operating current of the three-phase asynchronous motor when the analysis end instruction is received.

[0035] Specifically, when in working mode, the convolutional neural network model intelligently identifies the burnout time interval of the three-phase asynchronous motor based on the noise amplitude of the environment where the three-phase asynchronous motor is located, the overall vibration data of the three-phase asynchronous motor, the current operating current of the three-phase asynchronous motor, and multiple configuration information of the three-phase asynchronous motor. The burnout time interval is calculated with the current time as the starting point and the burnout time as the ending point. This includes using the MATLAB toolbox to complete the simulation and testing of the intelligent identification.

[0036] Figure 2 This is a schematic diagram of the internal structure of a three-phase asynchronous motor burnout data identification system according to embodiment B of the present invention.

[0037] and Figure 1 different, Figure 2 The three-phase asynchronous motor burnout data identification system may also include the following components:

[0038] The power detection mechanism includes multiple power detection units, which are used to detect the current input power of the environment recording mechanism, the configuration detection mechanism, the intelligent identification device, and the learning execution device, respectively.

[0039] The power detection mechanism includes multiple power detection units, which are used to detect the current input power of the environment recording mechanism, the configuration detection mechanism, the intelligent identification device, and the learning execution device respectively. This includes calculating the current input power of each of the environment recording mechanism, the configuration detection mechanism, the intelligent identification device, and the learning execution device by measuring the real-time supply voltage and real-time supply current of each device respectively.

[0040] The calculation of the current input power of the environment recording mechanism, the configuration detection mechanism, the intelligent identification device, and the learning execution device by measuring the real-time power supply voltage and real-time power supply current respectively includes: for any device of the environment recording mechanism, the configuration detection mechanism, the intelligent identification device, and the learning execution device, its current input power is the product of its real-time power supply voltage and its real-time power supply current;

[0041] The power detection mechanism includes multiple power detection units for detecting the current input power of the environment recording mechanism, the configuration detection mechanism, the intelligent identification device, and the learning execution device, respectively. It also includes multiple power detection units used by the environment recording mechanism, the configuration detection mechanism, the intelligent identification device, and the learning execution device, each having the same upper and lower power measurement thresholds.

[0042] The power detection mechanism includes multiple power detection units for detecting the current input power of the environment input mechanism, the configuration detection mechanism, the intelligent identification device, and the learning execution device, respectively. It also includes multiple power detection units with identical internal structures used by the environment input mechanism, the configuration detection mechanism, the intelligent identification device, and the learning execution device.

[0043] Figure 3This is a schematic diagram of the internal structure of a three-phase asynchronous motor burnout data identification system according to embodiment C of the present invention.

[0044] and Figure 1 different, Figure 3 The three-phase asynchronous motor burnout data identification system may also include the following components:

[0045] The on-site notification mechanism is connected to multiple power detection units respectively used for the environment recording mechanism, the configuration detection mechanism, the intelligent identification device, and the learning execution device, and is used to perform corresponding power notification operations based on the power measurement results of the multiple power detection units respectively used for the environment recording mechanism, the configuration detection mechanism, the intelligent identification device, and the learning execution device;

[0046] The on-site notification mechanism is connected to multiple power detection units respectively used for the environment recording mechanism, the configuration detection mechanism, the intelligent identification device, and the learning execution device. It is used to perform corresponding power notification operations based on the power measurement results of the multiple power detection units used for the environment recording mechanism, the configuration detection mechanism, the intelligent identification device, and the learning execution device. The on-site notification mechanism includes a static storage unit for storing power notification thresholds.

[0047] The on-site notification mechanism is connected to multiple power detection units respectively used for the environment recording mechanism, the configuration detection mechanism, the intelligent identification device, and the learning execution device. It is used to perform corresponding power notification operations based on the power measurement results of the multiple power detection units used for the environment recording mechanism, the configuration detection mechanism, the intelligent identification device, and the learning execution device. The on-site notification mechanism also performs corresponding power notification operations for the environment recording mechanism, the configuration detection mechanism, the intelligent identification device, and the learning execution device based on a voice playback mode.

[0048] Furthermore, in the three-phase asynchronous motor burnout data identification system, when in working mode, a convolutional neural network model is used to intelligently identify the burnout time interval of the three-phase asynchronous motor based on the noise amplitude of the environment where the three-phase asynchronous motor is located, the overall vibration data of the three-phase asynchronous motor, the current operating current of the three-phase asynchronous motor, and multiple configuration information of the three-phase asynchronous motor. The burnout time interval, with the current time as the starting point and the burnout time as the ending point, also includes: inputting the noise amplitude of the environment where the three-phase asynchronous motor is located, the overall vibration data of the three-phase asynchronous motor, the current operating current of the three-phase asynchronous motor, and multiple configuration information of the three-phase asynchronous motor in parallel into the convolutional neural network model to run the convolutional neural network model and obtain the burnout time interval of the three-phase asynchronous motor output by the convolutional neural network model.

[0049] The specific implementation methods and embodiments described above are only used to clarify the technical content of the present invention and should not be interpreted narrowly as limited to the specific examples. Various changes and implementations can be made within the scope of the spirit and claims of the present invention.

Claims

1. A three-phase asynchronous motor, comprising a three-phase asynchronous motor body and a phase loss state burnout data identification system connected to the three-phase asynchronous motor body, characterized in that, The phase loss state burnout data identification system includes: The phase loss detection mechanism is connected to the three-phase asynchronous motor and is used to detect whether the three-phase asynchronous motor has a phase loss. When a phase loss is found, it issues an analysis start command, and when no phase loss is found, it issues an analysis end command. An environmental data entry mechanism, connected to a phase loss detection mechanism, is used to start recording the noise amplitude of the environment where the three-phase asynchronous motor is located, the overall vibration data of the three-phase asynchronous motor, and the current operating current of the three-phase asynchronous motor at the current moment when an analysis start command is received. A testing mechanism is configured and connected to the three-phase asynchronous motor to obtain various configuration information of the three-phase asynchronous motor, including its volume data, weight data, rated speed, power factor, maximum torque, and output power. The intelligent identification device is connected to the phase loss detection mechanism, the environmental recording mechanism, and the configuration detection mechanism, respectively. It is used to enter the working mode when it receives the analysis start command and enter the sleep mode when it receives the analysis receive command. When it is in the working mode, it uses a convolutional neural network model to intelligently identify the burnout time interval of the three-phase asynchronous motor based on the noise amplitude of the environment where the three-phase asynchronous motor is located, the overall vibration data of the three-phase asynchronous motor, the current operating current of the three-phase asynchronous motor, and multiple configuration information of the three-phase asynchronous motor. The burnout time interval is started from the current moment and ends at the burnout moment. The learning execution device is connected to the intelligent identification device and is used to supply the intelligent identification device with a convolutional neural network model. The convolutional neural network model is a convolutional neural network after a set number of learning iterations. The value of the set number is proportional to the volume data of the three-phase asynchronous motor. The wireless transmission interface connects to the intelligent identification device and is used to package the received burn time interval and the current time together and send them to the remote device management server. The remote device management server is either a big data server or a blockchain server.

2. The three-phase asynchronous motor as described in claim 1, characterized in that: The intelligent identification device is also used to temporarily suspend the intelligent identification of the burnout time interval of the three-phase asynchronous motor based on the convolutional neural network model when it is in sleep mode, according to the noise amplitude of the environment where the three-phase asynchronous motor is located, the overall vibration data of the three-phase asynchronous motor, the current operating current of the three-phase asynchronous motor, and multiple configuration information of the three-phase asynchronous motor. The environmental input mechanism is also used to stop inputting the noise amplitude of the environment where the three-phase asynchronous motor is located, the overall vibration data of the three-phase asynchronous motor, and the current operating current of the three-phase asynchronous motor when the analysis end instruction is received. The simulation and testing of the intelligent identification were carried out using the MATLAB toolbox.

3. The three-phase asynchronous motor as described in claim 2, characterized in that, The system also includes: The power detection mechanism includes multiple power detection units, which are used to detect the current input power of the environment recording mechanism, the configuration detection mechanism, the intelligent identification device, and the learning execution device, respectively. Specifically, the current input power of each of the environmental input mechanism, the configuration detection mechanism, the intelligent identification device, and the learning execution device is calculated by measuring the real-time supply voltage and real-time supply current.

4. The three-phase asynchronous motor as described in claim 3, characterized in that: For any of the environmental input mechanism, the configuration detection mechanism, the intelligent identification device, and the learning execution device, its current input power is the product of its real-time power supply voltage and its real-time power supply current.

5. The three-phase asynchronous motor as described in claim 3, characterized in that: The multiple power detection units used by the environment input mechanism, the configuration detection mechanism, the intelligent identification device, and the learning execution device have the same upper limit threshold and lower limit threshold for power measurement.

6. The three-phase asynchronous motor as described in claim 5, characterized in that: The multiple power detection units used in the environment input mechanism, the configuration detection mechanism, the intelligent identification device, and the learning execution device have the same internal structure.

7. The three-phase asynchronous motor as described in any one of claims 3-6, characterized in that, The system also includes: The on-site notification mechanism is connected to multiple power detection units respectively used for the environment recording mechanism, the configuration detection mechanism, the intelligent identification device, and the learning execution device, and is used to perform corresponding power notification operations based on the power measurement results of the multiple power detection units respectively used for the environment recording mechanism, the configuration detection mechanism, the intelligent identification device, and the learning execution device.

8. The three-phase asynchronous motor as described in claim 7, characterized in that: The on-site notification mechanism includes a static storage unit for storing power notification thresholds.

9. The three-phase asynchronous motor as described in claim 8, characterized in that: The on-site notification mechanism performs corresponding power notification operations for the environment recording mechanism, the configuration detection mechanism, the intelligent identification device, and the learning execution device based on the voice playback mode.

Images