Three-phase asynchronous motor

Abstract

The invention relates to a three-phase asynchronous motor which comprises a three-phase asynchronous motor body, an open-phase detection mechanism, an environment input mechanism, a configuration detection mechanism, an intelligent identification device, a learning execution device and a wireless transmission interface. The open-phase state burnout data identification system of the three-phase asynchronous motor is intelligent in operation and wide in application, and provides key data for equipment management and maintenance of the three-phase asynchronous motor.

Description

Technical Field

[0001] The invention relates to the field of three-phase asynchronous motors, and in particular to a three-phase asynchronous motor. Background Art

[0002] The three-phase asynchronous motor is a type of induction motor that is powered by 380V three-phase AC current (phase difference of 120 degrees) at the same time. Since the rotor and stator rotating magnetic field of the three-phase asynchronous motor rotate in the same direction and at different speeds, there is a slip rate, so it is called a three-phase asynchronous motor. The speed of the rotor of the three-phase asynchronous motor is lower than the speed of the rotating magnetic field. The rotor winding generates electromotive force and current due to the relative motion between the rotor winding and the magnetic field, and interacts with the magnetic field to generate electromagnetic torque to achieve energy conversion.

[0003] There is a certain time interval between the time when the three-phase asynchronous motor is in an abnormal state of phase loss and the time when the three-phase asynchronous motor actually burns out. When the time interval is determined, the three-phase asynchronous motor can be controlled in various ways to avoid the occurrence of the burning scene. Obviously, the prior art lacks a targeted analysis mechanism for the time interval between the current moment of the abnormal state of the three-phase asynchronous motor and the actual occurrence of the burning scene. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a three-phase asynchronous motor. Only when the three-phase asynchronous motor is in an abnormal state of phase loss, the intelligent identification of the time interval between the current moment of the abnormal state and the actual occurrence of the burning scene of the three-phase asynchronous motor is triggered, thereby avoiding excessive unnecessary energy consumption of the entire system. What is particularly critical is that based on the convolutional neural network model, the burning time interval of the 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 working current of the three-phase asynchronous motor and multiple configuration information of the three-phase asynchronous motor. The burning time interval takes the current moment as the timing starting point and the burning moment as the timing end point, thereby realizing the intelligent identification of the time interval between the current moment of the abnormal state of the three-phase asynchronous motor and the actual occurrence of the burning scene, providing key 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, wherein the phase loss state burnout data identification system comprises: A 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, and to issue an analysis start instruction when a phase loss exists, and to issue an analysis end instruction when no phase loss exists; An environment recording mechanism, connected to the phase-loss detection mechanism, for starting to record 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 working current of the three-phase asynchronous motor upon receiving the analysis start instruction; A configuration detection mechanism is connected to the three-phase asynchronous motor and is used to obtain multiple configuration information of the three-phase asynchronous motor, wherein the multiple configuration information of the three-phase asynchronous motor includes volume data, weight data, rated speed, power factor, maximum torque and output power of the three-phase asynchronous motor; An intelligent identification device, connected to the phase loss detection mechanism, the environment input mechanism and the configuration detection mechanism, respectively, for entering a working mode upon receiving an analysis start instruction, entering a sleep mode upon receiving an analysis reception instruction, and for intelligently identifying a burnout time interval of the three-phase asynchronous motor based on a convolutional neural network model 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 working current of the three-phase asynchronous motor and multiple configuration information of the three-phase asynchronous motor when in the working mode, wherein the burnout time interval takes the current time as the timing start point and the burnout time as the timing end point; A learning execution device connected to the intelligent identification device, used to supply the convolutional neural network model to the intelligent identification device, wherein the convolutional neural network model is a convolutional neural network after a set number of learnings, and the value of the set number is proportional to the volume data of the three-phase asynchronous motor; A wireless transmission interface connected to the intelligent identification device, used to package the received burn-out time interval and the current time and send them to a remote device management server; Among them, the received burning time interval and the current time are packaged together and sent to a remote device management server, including: the remote device management server is a big data server or a blockchain server.

[0006] Therefore, the present invention has at least the following beneficial technical effects: Technical effect A: A convolutional neural network model for intelligent identification of the burnout time interval of a three-phase asynchronous motor is established, wherein the convolutional neural network model is a convolutional neural network after a set number of learnings, and 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 result of the convolutional neural network model; Technical effect B: Based on the convolutional neural network model, the burning time interval of the three-phase asynchronous motor is intelligently identified 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 working current of the three-phase asynchronous motor, and multiple configuration information of the three-phase asynchronous motor. The burning time interval takes the current moment as the timing start point and the burning moment as the timing end point, thereby realizing the intelligent identification of the time interval between the current moment of the abnormal state of the three-phase asynchronous motor and the actual burning scene, providing key data for equipment management and maintenance; Technical effect C: Only when the three-phase asynchronous motor is in an abnormal state of phase loss, the intelligent identification of the time interval between the current moment of the abnormal state of the three-phase asynchronous motor and the actual burning scene is triggered, thereby avoiding excessive unnecessary energy consumption of the entire system.

[0007] The three-phase asynchronous motor phase loss state burnout data identification system of the present invention operates intelligently and has wide applications. Only when the three-phase asynchronous motor is in an abnormal state of phase loss, the intelligent identification of the time interval between the current moment of the abnormal state of the three-phase asynchronous motor and the actual occurrence of the burnout scene is triggered, and the burnout time interval of the three-phase asynchronous motor is intelligently identified based on the convolutional neural network model, thereby providing key data for the equipment management and maintenance of the three-phase asynchronous motor. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The embodiments of the present invention will be described below with reference to the accompanying drawings, wherein:

[0009] Figure 1 The figure is a schematic diagram of the internal structure of a three-phase asynchronous motor's phase loss state burnout data identification system according to Embodiment A of the present invention.

[0010] Figure 2 The figure is a schematic diagram of the internal structure of a burnout data identification system for a three-phase asynchronous motor in a phase loss state according to Embodiment B of the present invention.

[0011] Figure 3 The figure is a schematic diagram of the internal structure of a three-phase asynchronous motor's phase loss state burnout data identification system according to Embodiment C of the present invention. DETAILED DESCRIPTION

[0012] Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0013] The present invention provides 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. Here, the three-phase asynchronous motor body can be understood as a conventional three-phase asynchronous motor body in the cost field, and those skilled in the art can understand this point, and no further description is required again.

[0014] Figure 1 The internal structure diagram of the burnout data identification system of the three-phase asynchronous motor in the phase loss state according to the embodiment A of the present invention is shown, and the system comprises: A 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, and to issue an analysis start instruction when a phase loss exists, and to issue an analysis end instruction when no phase loss exists; For example, the phase loss detection mechanism is connected to the three-phase asynchronous motor, used to detect whether the three-phase asynchronous motor has a phase loss, and when there is a phase loss, an analysis start instruction is issued, and when there is no phase loss, an analysis end instruction is issued, including: an FPGA chip can be selected to implement the phase loss detection mechanism, connected to the three-phase asynchronous motor, used to detect whether the three-phase asynchronous motor has a phase loss, and when there is a phase loss, an analysis start instruction is issued, and when there is no phase loss, an analysis end instruction is issued; An environment recording mechanism, connected to the phase-loss detection mechanism, for starting to record 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 working current of the three-phase asynchronous motor upon receiving the analysis start instruction; A configuration detection mechanism is connected to the three-phase asynchronous motor and is used to obtain multiple configuration information of the three-phase asynchronous motor, wherein the multiple configuration information of the three-phase asynchronous motor includes volume data, weight data, rated speed, power factor, maximum torque and output power of the three-phase asynchronous motor; An intelligent identification device, connected to the phase loss detection mechanism, the environment input mechanism and the configuration detection mechanism, respectively, for entering a working mode upon receiving an analysis start instruction, entering a sleep mode upon receiving an analysis reception instruction, and for intelligently identifying a burnout time interval of the three-phase asynchronous motor based on a convolutional neural network model 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 working current of the three-phase asynchronous motor and multiple configuration information of the three-phase asynchronous motor when in the working mode, wherein the burnout time interval takes the current time as the timing start point and the burnout time as the timing end point; A learning execution device connected to the intelligent identification device, used to supply the convolutional neural network model to the intelligent identification device, wherein the convolutional neural network model is a convolutional neural network after a set number of learnings, and the value of the set number is proportional to the volume data of the three-phase asynchronous motor; A wireless transmission interface connected to the intelligent identification device, used to package the received burn-out time interval and the current time and send them to a remote device management server; The received burning time interval and the current time are packaged and sent to a remote device management server, including: the remote device management server is a big data server or a blockchain server; The intelligent identification device is also used to temporarily suspend the burning time interval of the three-phase asynchronous motor based on the convolutional neural network model 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 working current of the three-phase asynchronous motor and multiple configuration information of the three-phase asynchronous motor when in the sleep mode; Wherein, the environment input mechanism is also used to stop inputting 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 working current of the three-phase asynchronous motor when receiving the analysis end instruction; Among them, when in working mode, the burning time interval of the three-phase asynchronous motor is intelligently identified based on the convolutional neural network model 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 working current of the three-phase asynchronous motor and multiple configuration information of the three-phase asynchronous motor. The burning time interval takes the current time as the timing starting point and the burning time as the timing end point, including: using the MATLAB toolbox to complete the simulation processing and test processing of the intelligent identification.

[0015] Figure 2 The figure is a schematic diagram of the internal structure of a burnout data identification system for a three-phase asynchronous motor in a phase loss state according to Embodiment B of the present invention.

[0016] and Figure 1 different, Figure 2 The three-phase asynchronous motor phase loss state burnout data identification system can also include the following components: A power detection mechanism, comprising a plurality of power detection units, for respectively detecting the current input power of the environment input mechanism, the configuration detection mechanism, the intelligent identification device and the learning execution device; The power detection mechanism includes a plurality of power detection units, and is used to respectively detect the current input power of the environment input mechanism, the configuration detection mechanism, the intelligent identification device, and the learning execution device, including: calculating the current input power of each of the environment input mechanism, the configuration detection mechanism, the intelligent identification device, and the learning execution device by respectively measuring the real-time power supply voltage and the real-time power supply current for the environment input mechanism, the configuration detection mechanism, the intelligent identification device, and the learning execution device; The calculation of the current input power of each of the environment input mechanism, the configuration detection mechanism, the intelligent identification device and the learning execution device is completed by measuring the real-time power supply voltage and the real-time power supply current respectively, including: for any device of the environment 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; Wherein, the power detection mechanism includes a plurality of power detection units for respectively detecting the current input power of the environment input mechanism, the configuration detection mechanism, the intelligent identification device and the learning execution device, and further includes: the plurality of power detection units respectively used for the environment input mechanism, the configuration detection mechanism, the intelligent identification device and the learning execution device have the same power measurement upper limit threshold and power measurement lower limit threshold; Among them, the power detection mechanism includes multiple power detection units, which are used to respectively detect the current input power of the environment input mechanism, the configuration detection mechanism, the intelligent identification device and the learning execution device. It also includes: the internal structures of the multiple power detection units respectively used for the environment input mechanism, the configuration detection mechanism, the intelligent identification device and the learning execution device are the same.

[0017] Figure 3 The figure is a schematic diagram of the internal structure of a three-phase asynchronous motor's phase loss state burnout data identification system according to Embodiment C of the present invention.

[0018] and Figure 1 different, Figure 3 The three-phase asynchronous motor phase loss state burnout data identification system can also include the following components: An on-site notification mechanism, connected to a plurality of power detection units respectively used by the environment input mechanism, the configuration detection mechanism, the intelligent identification device and the learning execution device, for performing corresponding power notification operations based on power measurement results of the plurality of power detection units respectively used by the environment input mechanism, the configuration detection mechanism, the intelligent identification device and the learning execution device; Wherein, the on-site notification mechanism is respectively connected to a plurality of power detection units respectively used for the environment entry 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 plurality of power detection units respectively used for the environment entry mechanism, the configuration detection mechanism, the intelligent identification device and the learning execution device, including: the on-site notification mechanism includes a static storage unit for storing a power notification threshold; Among them, the on-site notification mechanism is respectively connected to multiple power detection units used for the environment input 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 used for the environment input mechanism, the configuration detection mechanism, the intelligent identification device and the learning execution device, and also includes: the on-site notification mechanism performs corresponding power notification operations for the environment input mechanism, the configuration detection mechanism, the intelligent identification device and the learning execution device based on the voice playback mode.

[0019] In addition, in the phase-missing state burning data identification system of the three-phase asynchronous motor, when in working mode, the burning time interval of the three-phase asynchronous motor is intelligently identified based on the convolutional neural network model 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 working current of the three-phase asynchronous motor and multiple configuration information of the three-phase asynchronous motor. The burning time interval takes the current moment as the timing starting point and the burning moment as the timing end point, and also includes: 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 working current of the three-phase asynchronous motor and multiple configuration information of the three-phase asynchronous motor are input into the convolutional neural network model in parallel to run the convolutional neural network model to obtain the burning time interval of the three-phase asynchronous motor output by the convolutional neural network model.

[0020] The specific implementation modes and examples described above are only used to clarify the technical content of the present invention, and should not be interpreted narrowly limited to the specific examples. Various changes and implementations can be made within the spirit and scope 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 comprises: A 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, and to issue an analysis start instruction when a phase loss exists, and to issue an analysis end instruction when no phase loss exists; An environment recording mechanism, connected to the phase-loss detection mechanism, for starting to record 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 working current of the three-phase asynchronous motor upon receiving the analysis start instruction; A configuration detection mechanism is connected to the three-phase asynchronous motor and is used to obtain multiple configuration information of the three-phase asynchronous motor, wherein the multiple configuration information of the three-phase asynchronous motor includes volume data, weight data, rated speed, power factor, maximum torque and output power of the three-phase asynchronous motor; An intelligent identification device, connected to the phase loss detection mechanism, the environment input mechanism and the configuration detection mechanism, respectively, for entering a working mode upon receiving an analysis start instruction, entering a sleep mode upon receiving an analysis reception instruction, and for intelligently identifying a burnout time interval of the three-phase asynchronous motor based on a convolutional neural network model 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 working current of the three-phase asynchronous motor and multiple configuration information of the three-phase asynchronous motor when in the working mode, wherein the burnout time interval takes the current time as the timing start point and the burnout time as the timing end point; A learning execution device connected to the intelligent identification device, used to supply the convolutional neural network model to the intelligent identification device, wherein the convolutional neural network model is a convolutional neural network after a set number of learnings, and the value of the set number is proportional to the volume data of the three-phase asynchronous motor; A wireless transmission interface connected to the intelligent identification device, used to package the received burn-out time interval and the current time and send them to a remote device management server; Among them, the received burning time interval and the current time are packaged together and sent to a remote device management server, including: the remote device management server is a big data server or a blockchain server.

2. The three-phase asynchronous motor according to claim 1, characterized in that: The intelligent identification device is also used to temporarily suspend the burning time interval of the three-phase asynchronous motor based on the convolutional neural network model 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 working current of the three-phase asynchronous motor and multiple configuration information of the three-phase asynchronous motor when in the sleep mode; Wherein, the environment input mechanism is also used to stop inputting 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 working current of the three-phase asynchronous motor when receiving the analysis end instruction; Among them, when in working mode, the burning time interval of the three-phase asynchronous motor is intelligently identified based on the convolutional neural network model 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 working current of the three-phase asynchronous motor and multiple configuration information of the three-phase asynchronous motor. The burning time interval takes the current time as the timing starting point and the burning time as the timing end point, including: using the MATLAB toolbox to complete the simulation processing and test processing of the intelligent identification.

3. The three-phase asynchronous motor according to claim 2, characterized in that: The system further comprises: A power detection mechanism, comprising a plurality of power detection units, for respectively detecting the current input power of the environment input mechanism, the configuration detection mechanism, the intelligent identification device and the learning execution device; Among them, the power detection mechanism includes multiple power detection units, which are used to respectively detect the current input power of the environment input mechanism, the configuration detection mechanism, the intelligent identification device and the learning execution device, including: calculating the current input power of each of them by measuring the real-time power supply voltage and the real-time power supply current for the environment input mechanism, the configuration detection mechanism, the intelligent identification device and the learning execution device respectively.

4. The three-phase asynchronous motor according to claim 3, characterized in that: The calculation of the current input power of each is completed by measuring the real-time power supply voltage and the real-time power supply current for the environment input mechanism, the configuration detection mechanism, the intelligent identification device and the learning execution device respectively, including: for any device of the environment 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 according to claim 3, characterized in that: The power detection mechanism includes multiple power detection units, which are used to respectively detect the current input power of the environment input mechanism, the configuration detection mechanism, the intelligent identification device and the learning execution device. It also includes: the multiple power detection units respectively used for the environment input mechanism, the configuration detection mechanism, the intelligent identification device and the learning execution device have the same power measurement upper limit threshold and power measurement lower limit threshold.

6. The three-phase asynchronous motor according to claim 5, characterized in that: The power detection mechanism includes multiple power detection units, which are used to respectively detect the current input power of the environment input mechanism, the configuration detection mechanism, the intelligent identification device and the learning execution device. It also includes: the internal structures of the multiple power detection units respectively used for the environment input mechanism, the configuration detection mechanism, the intelligent identification device and the learning execution device are the same.

7. The three-phase asynchronous motor according to any one of claims 3 to 6, characterized in that: The system further comprises: The on-site notification mechanism is respectively connected to multiple power detection units respectively used for the environment entry 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 entry mechanism, the configuration detection mechanism, the intelligent identification device and the learning execution device.

8. The three-phase asynchronous motor according to claim 7, characterized in that: The on-site notification mechanism is respectively connected to multiple power detection units respectively used for the environment entry 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 entry mechanism, the configuration detection mechanism, the intelligent identification device and the learning execution device, including: the on-site notification mechanism includes a static storage unit for storing power notification thresholds.

9. The three-phase asynchronous motor according to claim 8, characterized in that: The on-site notification mechanism is respectively connected to multiple power detection units respectively used for the environment input 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 input mechanism, the configuration detection mechanism, the intelligent identification device and the learning execution device. It also includes: the on-site notification mechanism performs corresponding power notification operations for the environment input mechanism, the configuration detection mechanism, the intelligent identification device and the learning execution device based on the voice playback mode.

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