CN113030723A - Alternating current asynchronous motor state monitoring system - Google Patents
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Abstract
The invention discloses a state monitoring system of an alternating current asynchronous motor, which comprises a data acquisition module, an embedded terminal module and a cloud computing and service center module. The data acquisition module acquires signals of the motor and the surrounding environment such as electricity, temperature, vibration and the like. The embedded terminal module is installed on a working site, collects collected data, extracts fault characteristic parameters, and carries out real-time rapid fault diagnosis and control on the motor to ensure the safe operation of the motor. The cloud computing and service center module is communicated with the embedded terminal module to obtain the running data of the motor, fault pre-diagnosis based on data driving is carried out, functions of enabling a user to carry out data query and sending out early warning signals to faults are provided, and remote operation and maintenance and visualization are achieved. The system introduces a collaborative cooperation concept of edge computing and cloud computing, comprehensively utilizes a fault diagnosis scheme based on a physical model and data driving, gives consideration to accuracy, real-time performance and economy, and is an effective way for monitoring the state of the alternating-current asynchronous motor.
Description
Technical Field
The invention relates to the technical field of motor state monitoring, in particular to a state monitoring system of an alternating current asynchronous motor, which is a state monitoring system comprehensively utilizing a physical model and data driving of the alternating current asynchronous motor.
Background
The AC asynchronous motor is used as the main power unit of various electric driving devices, has the advantages of simple structure, convenient control, high energy efficiency, no pollution and the like, and is widely applied to various electromechanical devices. Due to the long-term operation of the motor, some structures and parts can be gradually degraded, so that slight faults are converted into serious faults, even serious production accidents are caused, and the great loss of lives and properties is caused. Therefore, the method researches how to timely detect the signal characteristics of the asynchronous motor during abnormal operation or early failure, judges the failure according to the signal processing result, and implements targeted measures, thereby having great social and economic benefits.
The type of fault of an ac motor depends on the physical structure of the motor and the operating environment of the motor, and almost all types of faults develop according to a fixed damage trend or state pattern, i.e., from a slight initial sign of fault to complete damage of the equipment or components, resulting in equipment or system shutdown. Almost all equipment failures have some specific signal or characteristic change that occurs when they occur. Through the monitoring and detailed analysis of the signals or the characteristics, the signs of equipment faults can be found out, so that the specific type and the occurrence position of the equipment faults can be identified.
The existing state monitoring system is mostly based on a single current signal to monitor the state parameter, but the current signal is independently used as a characteristic parameter to be influenced by inherent asymmetry, power supply imbalance, sensor error and the like of a motor body, and the accurate diagnosis is often difficult to be carried out on the complex reasons behind the motor fault. Therefore, in order to ensure the long-time stable operation of the motor, what is really needed is to perform cross comparison and trend analysis on real-time information and historical data of various motor operation states including voltage, current, temperature, magnetic field, vibration and the like, perform evaluation based on the motor operation health condition, make a corresponding plan and flow for operation and maintenance of equipment, and take actions.
Disclosure of Invention
In view of the above, aiming at the defects of the prior art, the invention provides a state monitoring system based on an alternating current asynchronous motor, which realizes the sampling of the key parameters of the motor, the voltage, the stator current, the temperature and the vibration of the surrounding environment, and obtains the information of an air gap flux linkage by utilizing model calculation; edge calculation based on a physical model is carried out at an equipment end, fault characteristic parameters are extracted, and real-time rapid fault diagnosis and control are carried out on the motor to ensure the safe operation of the motor; the cloud end applies an intelligent algorithm to carry out data-driven fault pre-diagnosis, provides functions for a user to inquire data and send out early warning signals to faults, and realizes remote operation and maintenance and visualization. The system introduces an advanced concept of cooperative edge computing and cloud computing, comprehensively utilizes a fault diagnosis scheme based on a physical model and data driving, gives consideration to accuracy, real-time performance and economy, and is an effective way for monitoring the state of the current alternating current asynchronous motor.
In order to achieve the purpose, the invention adopts the following technical scheme:
an AC asynchronous motor condition monitoring system comprising: the system comprises a data acquisition module, an embedded terminal module and a cloud computing and service center module. The method is characterized in that: the data acquisition module acquires key parameters of the motor and the surrounding environment and transmits data to the embedded terminal module. The embedded terminal module preprocesses the acquired data, obtains stator flux linkage information of the motor through a model calculation method according to the acquired data, extracts fault characteristic parameters with strong correlation with fault or characteristic parameters in magnetic field and current characteristic parameters, performs edge calculation based on a physical model, performs rapid fault diagnosis and control on the motor, and transmits data to the cloud calculation and service center module. The cloud computing and service center module carries out data-driven fault pre-diagnosis according to the obtained data, and provides functions for a user to carry out data query and send out early warning signals to faults.
The data acquisition module comprises: the device comprises an electrical information acquisition unit, a temperature information acquisition unit and a vibration information acquisition unit. The electric information acquisition unit comprises a voltage acquisition unit and a current acquisition unit, the voltage acquisition unit is used for acquiring voltage signals when the motor runs, and the current acquisition unit is used for acquiring current signals of a motor stator. The temperature information acquisition unit is used for acquiring the temperature of the motor body and the temperature of the surrounding environment. The vibration information acquisition unit is used for acquiring a vibration signal of the motor shell. And transmitting the acquired data to the embedded terminal module in real time.
The embedded terminal module includes: the system comprises a data processing unit, a feature extraction unit and a real-time diagnosis unit. Wherein,
the data processing unit has the main functions of preprocessing data, collecting, cleaning and classifying the data acquired by the data acquisition module, and obtaining the air gap flux linkage of the motor through model calculation.
The feature extraction unit mainly has the functions of extracting fault features, analyzing magnetic field and current feature parameters for characterizing faults on the basis of preprocessing data by the data processing unit, and extracting fault feature parameters with strong correlation with the faults or the characterized parameters in the feature parameters, including correlation, linearity and the like.
The real-time diagnosis unit has the main functions of carrying out rapid fault diagnosis and control on the motor according to fault characteristic parameters extracted by the characteristic extraction unit, transmitting key parameter data obtained by the data processing unit, fault characteristic parameters extracted by the characteristic extraction unit and fault signals output by the real-time diagnosis unit to the cloud computing and service center module, and receiving decision signals of the cloud computing and service center module.
The cloud computing and service center module comprises: the device comprises a data storage unit, a data driving model unit, a fault alarm unit and a data query unit. Wherein,
and the data storage unit receives the data transmitted by the real-time diagnosis unit of the embedded terminal module and transmits a decision instruction to the real-time diagnosis unit. The data storage unit records various electrical and mechanical parameters and ambient environment parameters of the motor, and records time, reasons, equipment types, component types, characteristic parameters and the like of each fault in the data storage unit so as to enrich a knowledge base and provide data sources for subsequent intelligent analysis.
The data driving model unit carries out data driving-based fault pre-diagnosis according to the data obtained by the data storage unit, comprehensively evaluates the operation condition of the motor by using an intelligent algorithm, automatically analyzes the state of the motor, and carries out comprehensive state analysis and health management on the motor.
And the fault alarm unit sends a corresponding alarm signal to a user according to the fault pre-judgment made by the real-time diagnosis unit and the data driving model unit, and makes a decision instruction or gives a maintenance suggestion to remind the user to maintain the equipment in time.
The data query unit is mainly used for a user to query data. A user can access the cloud computing and service center module through the data query unit in real time by using various terminals to obtain state information, fault information, working logs, maintenance information and the like related to the motor.
Different from the traditional diagnosis technology for judging the fault by directly utilizing the current, the fault diagnosis method has the advantages that the current signal is easily influenced by the inherent asymmetry of the motor body, the unbalanced power supply, the sensor error and the like, and the fault of the motor cannot be accurately diagnosed, and the fault of the motor can be accurately diagnosed by the edge calculation in the embedded terminal module through the combined monitoring of the magnetic field and the current.
Different from the traditional fault diagnosis technology based on a single analytical model, modal analysis difficulty, unavoidable errors and unknown interference exist, the method based on the AC asynchronous motor physical model and the data driving combination not only meets the real-time performance of fault diagnosis, but also utilizes the data driving technology to mine the running state information of the motor, carries out fault pre-diagnosis on the motor, and carries out comprehensive state analysis and health management.
The edge computing provides high-value data for the cloud computing, the cloud computing is optimized and supplemented for the edge computing, and the cloud computing and the edge computing are cooperated with each other, so that the system has real-time performance and reliability. The edge calculation utilizes key parameters of a motor system to realize pre-diagnosis of equipment faults, and the monitored key parameters have the characteristics of easiness in measurement, difficulty in interference and strong correlation with the fault or the represented parameters, including correlation, linearity and the like; the cloud computing obtains a health model based on data through intelligent processing, namely learning and training, is a non-mathematical model, compares data acquired in real time, and judges the health condition of equipment.
Compared with the prior art, the invention has the following obvious prominent substantive characteristics and obvious advantages:
1. in the aspect of fault judgment based on a physical model, the problem that the fault is judged by directly utilizing the current is avoided, and the problem is found through the combined monitoring of the magnetic field and the current.
2. In the aspect of obtaining the air gap flux linkage information, the air gap magnetic field condition is obtained by adopting a model calculation method, so that the situation that a detection coil or a sensor is installed in the motor by adopting an invasive method is avoided, and the method is effective and economical.
3. In the aspect of fault pre-diagnosis, the invention is based on a data driving technology, and adopts an intelligent algorithm to mine the running state information of the motor, so as to carry out comprehensive state analysis and health management, thereby improving the reliability of motor fault pre-diagnosis.
4. The method introduces an advanced concept of edge computing and cloud computing cooperation, and the edge computing and the cloud computing cooperate with each other, optimize and supplement, and have real-time performance, reliability and economy.
Drawings
Fig. 1 is a block diagram of a state monitoring system for an ac asynchronous motor.
Fig. 2 is a functional schematic diagram of a state monitoring system of an alternating current asynchronous motor.
Fig. 3 is a block diagram of a data acquisition module.
Fig. 4 is a block diagram of the structure of an embedded terminal module.
Fig. 5 is a process diagram of the embedded terminal module performing physical model-based motor fault diagnosis.
Fig. 6 is a block diagram of a cloud computing and service center module structure.
Fig. 7 is a process diagram of the cloud computing and service center module performing data-drive-based motor fault diagnosis.
Detailed Description
The invention will be described in further detail below with reference to the drawings and preferred embodiments. It should be understood that the following examples are illustrative only and do not represent or limit the scope of the present invention, which is defined by the claims.
The first embodiment is as follows:
referring to fig. 1 to 7, a system for monitoring the state of an ac asynchronous motor includes: the system comprises a data acquisition module 1, an embedded terminal module 2 and a cloud computing and service center module 3. The data acquisition module 1 and the embedded terminal module 2 are arranged at the equipment end, and the cloud computing and service center module 3 is arranged at the cloud end. The data acquisition module 1 acquires key parameters of the motor and the surrounding environment and transmits data to the embedded terminal module 2. The embedded terminal module 2 preprocesses the acquired data, obtains air gap flux linkage information of the motor by a model calculation method according to the acquired data, extracts fault characteristic parameters, performs edge calculation based on a physical model, performs rapid fault diagnosis and control on the motor, and transmits data to the cloud computing and service center module 3. The cloud computing and service center module 3 performs data-driven fault pre-diagnosis according to the obtained data, and provides functions for a user to perform data query and send out an early warning signal for a fault. In the aspect of fault judgment based on a physical model, the system of the embodiment avoids the problem of judging the fault by directly utilizing the current, and finds the problem through the combined monitoring of the magnetic field and the current.
Example two:
this embodiment is substantially the same as the first embodiment, and is characterized in that:
the data acquisition module 1 comprises: electric information acquisition unit 11, temperature information acquisition unit 12 and vibration information acquisition unit 13, electric information acquisition unit 11 includes voltage acquisition unit 111 and current acquisition unit 112, voltage acquisition unit 111 is used for gathering the voltage signal when the motor moves, current acquisition unit 112 is used for gathering motor stator current signal. The temperature information collecting unit 12 is used for collecting the temperature of the motor body and the temperature of the surrounding environment. The vibration information acquisition unit 13 is used for acquiring a vibration signal of the motor shell. The acquired data is transmitted to the embedded terminal module 2 in real time.
The embedded terminal module 2 includes: a data processing unit 21, a feature extraction unit 22, and a real-time diagnosis unit 23. Wherein,
the data processing unit 21 has the main functions of preprocessing data, collecting, cleaning and classifying the data acquired by the data acquisition module 1, and obtaining the air gap flux linkage of the motor through model calculation.
The feature extraction unit 22 mainly functions to extract fault features, analyze the magnetic field and current, which are characteristic parameters for characterizing faults, and extract fault feature parameters, which have strong correlation with faults or characterized parameters, such as correlation and linearity, from the characteristic parameters on the basis of preprocessing the data by the data processing unit 21.
The real-time diagnosis unit 23 has a main function of performing rapid fault diagnosis and control on the motor according to the fault characteristic parameters extracted by the characteristic extraction unit 22, transmitting the key parameter data obtained by the data processing unit 21, the fault characteristic parameters extracted by the characteristic extraction unit 22 and the fault signal output by the real-time diagnosis unit 23 to the cloud computing and service center module 3, and receiving a decision signal of the cloud computing and service center module 3.
The cloud computing and service center module 3 includes: a data storage unit 31, a data driving model unit 32, a failure alarm unit 33, and a data query unit 34;
the data storage unit 31 receives the data transmitted by the real-time diagnosis unit 23 of the embedded terminal module 2 and transmits a decision instruction to the data storage unit; the data storage unit 31 records various electrical and mechanical parameters and ambient environment parameters of the motor, and records the time, reason, equipment type, component type and characteristic parameters of each fault in the data storage unit 31 so as to enrich a knowledge base and provide a data source for subsequent intelligent analysis;
the data driving model unit 32 performs data driving-based fault pre-diagnosis according to the data acquired by the data storage unit 31, comprehensively evaluates the operation condition of the motor by using an intelligent algorithm, automatically analyzes the state of the motor, and performs comprehensive state analysis and health management on the motor;
the fault alarm unit 33 sends a corresponding alarm signal to a user according to the fault pre-judgment made by the real-time diagnosis unit 23 and the data driving model unit 32, and makes a decision instruction or gives a maintenance suggestion to remind the user to perform equipment maintenance in time;
the data query unit 34 mainly functions to provide data query for users. The user can access the cloud computing and service center module 3 through the data query unit 34 in real time by using various terminals to acquire state information, fault information, working logs and maintenance information related to the motor.
The edge calculation in the embedded terminal module 2 accurately diagnoses the fault of the motor through the combined monitoring of the magnetic field and the current.
The fault diagnosis technology different from the traditional single analysis model has the defects of difficult modal analysis, unavoidable errors and unknown interference, the combined method based on the alternating current asynchronous motor physical model and the data driving not only meets the real-time performance of fault diagnosis, but also utilizes the data driving technology to mine the running state information of the motor, carries out fault pre-diagnosis on the motor, and carries out comprehensive state analysis and health management.
The edge computing provides high-value data for cloud computing, the cloud computing is optimized and supplemented for the edge computing, and the cloud computing and the edge computing are cooperated with each other, so that the system has real-time performance and reliability. The edge calculation utilizes the key parameters of the motor system, namely magnetic field and current, to realize the pre-diagnosis of equipment faults, and the monitored key parameters have the characteristics of easy measurement, difficult interference, strong correlation with the fault or the represented parameters, namely correlation and linearity; the cloud computing obtains a health model based on data, namely a non-mathematical model, through intelligent processing, learning and training, compares data acquired in real time, and judges the health condition of equipment.
Example three:
this embodiment is substantially the same as the above embodiment, and is characterized in that:
as shown in fig. 1 and 2, the ac asynchronous motor state monitoring system includes: the system comprises a data acquisition module 1, an embedded terminal module 2 and a cloud computing and service center module 3. The data acquisition module 1 and the embedded terminal module 2 are arranged at the equipment end, and the cloud computing and service center module 3 is arranged at the cloud end. The data acquisition module 1 acquires key parameters of the motor and the surrounding environment and transmits data to the embedded terminal module 2. The embedded terminal module 2 preprocesses the acquired data, obtains stator flux linkage information of the motor through a model calculation method according to the acquired data, extracts fault characteristic parameters, performs edge calculation based on a physical model, performs rapid fault diagnosis and control on the motor, and transmits data to the cloud computing and service center module 3. The cloud computing and service center module 3 performs data-driven fault pre-diagnosis according to the obtained data, and provides functions for a user to perform data query and send out an early warning signal for a fault.
As shown in fig. 3, the data acquisition module 1 includes: an electrical information acquisition unit 11, a temperature information acquisition unit 12, and a vibration information acquisition unit 13. The electrical information acquisition unit 11 includes a voltage acquisition unit 111 and a current acquisition unit 112, the voltage acquisition unit 111 is used for acquiring voltage signals when the motor operates, and the current acquisition unit 112 is used for acquiring current signals of a motor stator. The temperature information collecting unit 12 is used for collecting the temperature of the motor body and the temperature of the surrounding environment. The vibration information acquisition unit 13 is used for acquiring a vibration signal of the motor shell. The acquired data is transmitted to the embedded terminal module 2 in real time.
As shown in fig. 4, the embedded terminal module 2 includes: a data processing unit 21, a feature extraction unit 22, and a real-time diagnosis unit 23. The data processing unit 21 has the main functions of preprocessing data, collecting, cleaning and classifying the data acquired by the data acquisition module 1, and obtaining the air gap flux linkage of the motor through model calculation.
The feature extraction unit 22 mainly functions to extract fault features, analyze the feature parameters of the magnetic field and the current for characterizing the fault on the basis of preprocessing the data by the data processing unit 21, and extract fault feature parameters having strong correlation with the fault or the characterized parameters, including correlation, linearity, and the like, from the feature parameters.
The real-time diagnosis unit 23 has a main function of performing rapid fault diagnosis and control on the motor according to the fault characteristic parameters extracted by the characteristic extraction unit 22, transmitting the key parameter data obtained by the data processing unit 21, the fault characteristic parameters extracted by the characteristic extraction unit 22 and the fault signal output by the real-time diagnosis unit 23 to the cloud computing and service center module 3, and receiving a decision signal of the cloud computing and service center module 3.
As shown in fig. 5, the process of the embedded terminal module 2 performing the edge calculation based on the physical model includes: firstly, motor stator current and air gap flux linkage are obtained by a sensor or model calculation method, then the signals are respectively processed, characteristic parameters closely related to faults in each signal are extracted, then the extracted characteristic parameters are subjected to characteristic fusion, and the fault condition of the motor is comprehensively judged.
As shown in fig. 6, the cloud computing and service center module 3 includes: a data storage unit 31, a data driving model unit 32, a fault alarm unit 33 and a data query unit 34. The data storage unit 31 receives data transmitted by the real-time diagnosis unit 23 of the embedded terminal module 2, and transmits a decision instruction to the real-time diagnosis unit. The data storage unit 31 records various electrical and mechanical parameters and ambient environment parameters of the motor, and records time, reasons, equipment types, component types, characteristic parameters and the like of each fault in the data storage unit 31 so as to enrich a knowledge base and provide data sources for subsequent intelligent analysis.
The data driving model unit 32 performs data driving-based fault pre-diagnosis according to the data acquired by the data storage unit 31, comprehensively evaluates the operation condition of the motor by using an intelligent algorithm, automatically analyzes the state of the motor, and performs comprehensive state analysis and health management on the motor.
The fault alarm unit 33 sends a corresponding alarm signal to the user according to the fault pre-judgment made by the real-time diagnosis unit 23 and the data driving model unit 32, and makes a decision instruction or gives a maintenance suggestion to remind the user to perform equipment maintenance in time.
The data query unit 34 mainly functions to provide data query for users. The user can access the cloud computing and service center module 3 through the data query unit 34 in real time using various terminals, and obtain state information, fault information, work logs, maintenance information, and the like related to the motor.
As shown in fig. 7, the process of the cloud computing and service center module 3 performing data-driven cloud computing includes: the health model based on data is obtained after intelligent processing of learning and training by utilizing data of normal operation of the motor and the surrounding environment, the health model is a non-mathematical model, new data are input to further train the model, the more data are received along with the cloud end, the more accurate the trained health model is, and then the data collected in real time are compared to judge the health condition of the motor, so that the purpose of motor fault pre-diagnosis is achieved.
In summary, the ac asynchronous motor state monitoring system according to the above embodiment. The system comprises a data acquisition module, an embedded terminal module and a cloud computing and service center module. The data acquisition module acquires signals of the motor and the surrounding environment such as electricity, temperature, vibration and the like. The embedded terminal module is installed on a working site, collects collected data, performs edge calculation based on a physical model, extracts fault characteristic parameters, performs real-time rapid fault diagnosis and control on the motor, and ensures safe operation of the motor. The cloud computing and service center module is communicated with the embedded terminal module to obtain the running data of the motor, and the intelligent algorithm is used for carrying out fault pre-diagnosis based on data driving, so that the functions of data query by a user and early warning signal sending to the fault are provided, and remote operation and visualization are realized. The system introduces an advanced concept of cooperative edge computing and cloud computing, comprehensively utilizes a fault diagnosis scheme based on a physical model and data driving, gives consideration to accuracy, real-time performance and economy, and is an effective way for monitoring the state of the current alternating current asynchronous motor.
It will thus be seen that the objects of the invention have been fully and effectively accomplished. The functional and structural principles of the present invention have been shown and described in the embodiments, and the embodiments may be modified without departing from the principles. The invention includes all modifications encompassed within the spirit and scope of the claims.
Claims (7)
1. The alternating current asynchronous motor state monitoring system is characterized by comprising a data acquisition module (1), an embedded terminal module (2) and a cloud computing and service center module (3); the data acquisition module (1) and the embedded terminal module (2) are arranged at an equipment end, and the cloud computing and service center module (3) is arranged at a cloud end; the data acquisition module (1) acquires key parameters of a motor and the surrounding environment and transmits data to the embedded terminal module (2); the embedded terminal module (2) preprocesses the acquired data, obtains air gap flux linkage information of the motor by a model calculation method according to the acquired data, extracts fault characteristic parameters, performs edge calculation based on a physical model, performs rapid fault diagnosis and control on the motor, and transmits data to the cloud calculation and service center module (3); the cloud computing and service center module (3) carries out data-driven fault pre-diagnosis according to the obtained data, and provides functions for a user to inquire data and send out early warning signals for faults.
2. The alternating current asynchronous motor state monitoring system according to claim 1, characterized in that the data acquisition module (1) comprises an electrical information acquisition unit (11), a temperature information acquisition unit (12) and a vibration information acquisition unit (13), the electrical information acquisition unit (11) comprises a voltage acquisition unit (111) and a current acquisition unit (112), the voltage acquisition unit (111) is used for acquiring voltage signals when the motor runs, and the current acquisition unit (112) is used for acquiring motor stator current signals; the temperature information acquisition unit (12) is used for acquiring the temperature of the motor body and the temperature of the surrounding environment; the vibration information acquisition unit (13) is used for acquiring a vibration signal of the motor shell; and the acquired data is transmitted to the embedded terminal module (2) in real time.
3. An AC asynchronous machine state monitoring system according to claim 1, characterized in that said embedded terminal module (2) comprises a data processing unit (21), a feature extraction unit (22), a real-time diagnosis unit (23); the data processing unit (21) has the main functions of preprocessing data, collecting, cleaning and classifying the data acquired by the data acquisition module (1), and obtaining the air gap flux linkage of the motor through model calculation;
the feature extraction unit (22) is mainly used for extracting fault features, analyzing the magnetic field and current which are characteristic parameters for characterizing faults on the basis of preprocessing data by the data processing unit (21), and extracting fault feature parameters which have strong correlation with faults or the characteristic parameters, namely correlation and linearity;
the real-time diagnosis unit (23) has the main functions of performing rapid fault diagnosis and control on the motor according to fault characteristic parameters extracted by the characteristic extraction unit (22), transmitting key parameter data obtained by the data processing unit (21), the fault characteristic parameters extracted by the characteristic extraction unit (22) and fault signals output by the real-time diagnosis unit (23) to the cloud computing and service center module (3), and receiving decision signals of the cloud computing and service center module (3).
4. An AC asynchronous motor condition monitoring system according to claim 1, characterized in that said cloud computing and service center module (3) comprises a data storage unit (31), a data driven model unit (32), a fault alarm unit (33) and a data query unit (34);
the data storage unit (31) receives data transmitted by the real-time diagnosis unit (23) of the embedded terminal module (2) and transmits a decision instruction to the data storage unit; the data storage unit (31) records various electrical and mechanical parameters and ambient environment parameters of the motor, and records the time, reason, equipment type, component type and characteristic parameters of each fault in the data storage unit (31) so as to enrich a knowledge base for subsequent intelligent analysis and provide a data source;
the data driving model unit (32) carries out data driving-based fault pre-diagnosis according to the data obtained by the data storage unit (31), and applies an intelligent algorithm to comprehensively evaluate the operation condition of the motor, automatically analyze the state of the motor and carry out comprehensive state analysis and health management on the motor;
the fault alarm unit (33) sends a corresponding alarm signal to a user according to fault pre-judgment made by the real-time diagnosis unit (23) and the data driving model unit (32), and makes a decision instruction or gives a maintenance suggestion to remind the user to perform equipment maintenance in time;
the data query unit (34) is mainly used for a user to query data; a user can access the cloud computing and service center module (3) through the data query unit (34) in real time by using various terminals to obtain state information, fault information, working logs and maintenance information related to the motor.
5. The ac asynchronous machine condition monitoring system of claim 1, characterized in that: the edge calculation in the embedded terminal module (2) accurately diagnoses the fault of the motor through the combined monitoring of the magnetic field and the current.
6. The ac asynchronous machine condition monitoring system of claim 1, characterized in that: the fault diagnosis technology different from the traditional single analysis model has the defects of difficult modal analysis, unavoidable errors and unknown interference, the combined method based on the alternating current asynchronous motor physical model and the data driving not only meets the real-time performance of fault diagnosis, but also utilizes the data driving technology to mine the running state information of the motor, carries out fault pre-diagnosis on the motor, and carries out comprehensive state analysis and health management.
7. The ac asynchronous machine condition monitoring system of claim 1, characterized in that: the edge computing provides high-value data for cloud computing, the cloud computing is optimized and supplemented for the edge computing, and the cloud computing and the edge computing are cooperated with each other, so that the system has real-time performance and reliability; the edge calculation utilizes the key parameters of the motor system, namely magnetic field and current, to realize the pre-diagnosis of equipment faults, and the monitored key parameters have the characteristics of easy measurement, difficult interference, strong correlation with the fault or the represented parameters, namely correlation and linearity; the cloud computing obtains a health model based on data, namely a non-mathematical model, through intelligent processing, learning and training, compares data acquired in real time, and judges the health condition of equipment.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115561006A (en) * | 2022-12-07 | 2023-01-03 | 天津通信广播集团有限公司 | Modularized multi-core different-architecture remote real-time monitoring system for rotary mechanical equipment |
| EP4160233A1 (en) * | 2021-09-30 | 2023-04-05 | Siemens Aktiengesellschaft | System, apparatus and method for monitoring faults in an electric machine |
| CN117232564A (en) * | 2023-11-15 | 2023-12-15 | 长春市星途科技有限公司 | Cloud computing-based drive motor grating encoder real-time diagnosis and calibration system |
| CN117346842A (en) * | 2023-11-14 | 2024-01-05 | 广东材通实业有限公司 | Fault detection method and system for motor junction box |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1905330A (en) * | 2005-07-28 | 2007-01-31 | 三菱电机株式会社 | Air gap eccentric checking device and method for single-phase inductor |
| CN102495368A (en) * | 2011-12-16 | 2012-06-13 | 西南大学 | Non-invasive online detection method and system for rotor broken-bar fault of three-phase cage type asynchronous motor |
| CN106990357A (en) * | 2017-04-20 | 2017-07-28 | 哈尔滨理工大学 | Intelligent electric machine integrated form multi-parameter fusion Gernral Check-up and early warning system |
| CN107402350A (en) * | 2017-08-21 | 2017-11-28 | 西安交通大学 | A kind of threephase asynchronous machine fault of eccentricity detection method |
| CN108152736A (en) * | 2017-12-07 | 2018-06-12 | 上海大学 | Utilize electric system parameter monitoring load variation and the autonomous sensory perceptual system of system exception |
| CN109145886A (en) * | 2018-10-12 | 2019-01-04 | 西安交通大学 | A kind of asynchronous machine method for diagnosing faults of Multi-source Information Fusion |
| CN110501640A (en) * | 2019-07-10 | 2019-11-26 | 哈尔滨工业大学（威海） | A method of it is static eccentric directly to test detection magneto based on air-gap field |
| CN110728443A (en) * | 2019-09-30 | 2020-01-24 | 鞍钢集团自动化有限公司 | Motor full life cycle management and control system |
| CN111856268A (en) * | 2019-04-24 | 2020-10-30 | 中矿龙科能源科技(北京)股份有限公司 | Motor and generator fault diagnosis system based on harmonic method |
| CN112285562A (en) * | 2020-11-18 | 2021-01-29 | 中国海洋石油集团有限公司 | Asynchronous motor fault detection method based on multi-signal fusion of electromagnetic field and thermal field |
-
2021
- 2021-03-15 CN CN202110273601.XA patent/CN113030723A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1905330A (en) * | 2005-07-28 | 2007-01-31 | 三菱电机株式会社 | Air gap eccentric checking device and method for single-phase inductor |
| CN102495368A (en) * | 2011-12-16 | 2012-06-13 | 西南大学 | Non-invasive online detection method and system for rotor broken-bar fault of three-phase cage type asynchronous motor |
| CN106990357A (en) * | 2017-04-20 | 2017-07-28 | 哈尔滨理工大学 | Intelligent electric machine integrated form multi-parameter fusion Gernral Check-up and early warning system |
| CN107402350A (en) * | 2017-08-21 | 2017-11-28 | 西安交通大学 | A kind of threephase asynchronous machine fault of eccentricity detection method |
| CN108152736A (en) * | 2017-12-07 | 2018-06-12 | 上海大学 | Utilize electric system parameter monitoring load variation and the autonomous sensory perceptual system of system exception |
| CN109145886A (en) * | 2018-10-12 | 2019-01-04 | 西安交通大学 | A kind of asynchronous machine method for diagnosing faults of Multi-source Information Fusion |
| CN111856268A (en) * | 2019-04-24 | 2020-10-30 | 中矿龙科能源科技(北京)股份有限公司 | Motor and generator fault diagnosis system based on harmonic method |
| CN110501640A (en) * | 2019-07-10 | 2019-11-26 | 哈尔滨工业大学（威海） | A method of it is static eccentric directly to test detection magneto based on air-gap field |
| CN110728443A (en) * | 2019-09-30 | 2020-01-24 | 鞍钢集团自动化有限公司 | Motor full life cycle management and control system |
| CN112285562A (en) * | 2020-11-18 | 2021-01-29 | 中国海洋石油集团有限公司 | Asynchronous motor fault detection method based on multi-signal fusion of electromagnetic field and thermal field |
Non-Patent Citations (4)
| Title |
|---|
| YIN TIAN ET AL.: "《A Review of Fault Diagnosis for Traction Induction Motor》", 《2018 37TH CHINESE CONTROL CONFERENCE (CCC)》, 7 October 2018 (2018-10-07) * |
| 王攀攀 等: "《虚拟仿真实验中的断条故障异步电动机模型设计》", 《实验科学与技术》 * |
| 王攀攀 等: "《虚拟仿真实验中的断条故障异步电动机模型设计》", 《实验科学与技术》, 31 August 2020 (2020-08-31) * |
| 钱晓龙，闫士杰: "电气传动控制技术", 冶金工业出版社, pages: 182 - 187 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP4160233A1 (en) * | 2021-09-30 | 2023-04-05 | Siemens Aktiengesellschaft | System, apparatus and method for monitoring faults in an electric machine |
| WO2023052385A1 (en) * | 2021-09-30 | 2023-04-06 | Siemens Aktiengesellschaft | System, apparatus and method for monitoring faults in an electric machine |
| CN115561006A (en) * | 2022-12-07 | 2023-01-03 | 天津通信广播集团有限公司 | Modularized multi-core different-architecture remote real-time monitoring system for rotary mechanical equipment |
| CN115561006B (en) * | 2022-12-07 | 2023-03-07 | 天津通信广播集团有限公司 | Modularized multi-core different-architecture remote real-time monitoring system for rotary mechanical equipment |
| CN117346842A (en) * | 2023-11-14 | 2024-01-05 | 广东材通实业有限公司 | Fault detection method and system for motor junction box |
| CN117232564A (en) * | 2023-11-15 | 2023-12-15 | 长春市星途科技有限公司 | Cloud computing-based drive motor grating encoder real-time diagnosis and calibration system |
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