CN111459906A - Method for establishing motor database - Google Patents
Method for establishing motor database Download PDFInfo
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- CN111459906A CN111459906A CN202010135060.XA CN202010135060A CN111459906A CN 111459906 A CN111459906 A CN 111459906A CN 202010135060 A CN202010135060 A CN 202010135060A CN 111459906 A CN111459906 A CN 111459906A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
- G06F16/258—Data format conversion from or to a database
Abstract
The invention relates to a method for establishing a motor database, which comprises the following steps: step 1: manually inputting real-time data obtained through experiments and field tests, and continuously correcting data of fault influence factors; step 2: measuring real-time data, inputting the real-time data as the current amount of historical data, and storing the current amount of the historical data in a database; meanwhile, inputting real-time data into a fault prediction model; and step 3: the database stores the real-time data and the historical data after conversion calculation; and 4, step 4: the fault prediction model is combined with historical data of a database and input real-time data to carry out fault analysis and storage; and 5: aiming at the storage form of data in a database, a one-machine-one-module database architecture is adopted; step 6: and displaying the analysis result of the real-time fault prediction model by using a display screen. The invention can provide effective data basis for the later motor online diagnosis and can effectively solve the difficulty in unified determination of the threshold value during fault judgment.
Description
Technical Field
The invention relates to the technical field of electrical equipment detection and diagnosis, in particular to a method for establishing a motor database.
Background
With the development of modern industrial technology and the improvement of the manufacturing level of equipment, the number of motors as the most important transmission and execution mechanism in the production system is continuously increasing. Whether the motor can operate normally, safely, efficiently and with low consumption or not in the industrial production manufacturing process has very important significance on the operation and development of industry, the motor fault can not only lead the load operated by the motor to work normally, but also can influence the operation of the whole production system sometimes, even endanger the safety of personnel, cause great economic loss and generate severe social influence.
The current fault detection equipment of the motor is mainly relay protection, which can only perform simple judgment and power-off protection on the voltage, current phase loss and amplitude of the motor and cannot judge the detailed running state of the motor. Whether motor body has the trouble at the operation in-process, still need professional patrolling and examining personnel to carry out periodic inspection, have certain requirement to patrolling and examining personnel's professional level like this, detection efficiency is lower, and the cost is higher, and detection effect is not good. The motor fault monitoring system capable of realizing online continuous automatic realization becomes an urgent requirement for motor application occasions such as factories and the like. In the operation of the motor online real-time fault monitoring system, not only the motor data detected in real time needs to be utilized, but also a model and a parameter database of the motor need to be established in advance, so that the fault condition of the motor can be diagnosed. Namely, the accuracy of the diagnosis result is not only influenced by the real-time detection data, but also influenced by the model and the parameter database. How to build a database capable of reflecting motor failure data becomes an important content.
Disclosure of Invention
The invention relates to a method for establishing a motor database, which solves the problems of low detection efficiency, high cost and poor detection effect in the prior art.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a motor database establishing method comprises the following steps:
step 1: manually inputting real-time data obtained through experiments and field tests, and continuously correcting data of fault influence factors;
step 2: measuring real-time data, inputting the real-time data as the current amount of historical data, and storing the current amount of the historical data in a database; meanwhile, inputting real-time data into a fault prediction model;
and step 3: the database stores the real-time data and the historical data after conversion calculation;
and 4, step 4: the fault prediction model is combined with historical data of the database and input real-time data to carry out fault analysis, and data analysis results are continuously and reversely input into the database and are stored after being converted in the database;
and 5: aiming at the storage form of data in a database, a one-machine-one-module database architecture is adopted, namely, the motor fault is preliminarily judged by using priori knowledge, and meanwhile, the database parameters are continuously updated by self in an online mode;
step 6: displaying the analysis result of the real-time fault prediction model by using a display screen;
further, the self-updating database parameter process is divided into two types, one is to update unified prior data, and the other is to update the fault related parameter information of a single motor individual.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a fault data framework of an early motor 'one machine and one module' for constructing multi-mode parameters with relativity on a time axis, a database with self-learning capability is established, and historical parameters are added or replaced by combining an effectiveness analysis learning method of a proximity rule. The database has a unified common data area and a 'one machine one module' dedicated data area. The data sources of the database to be established are three, which are respectively the manual input of professional technicians, the real-time data input of a preceding-stage data mapping module and the data analysis result of a fault prediction model. The framework can provide effective data basis for the online diagnosis of the motor in the later period, and can effectively solve the difficulty in unified determination of the threshold value during fault judgment.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings that should be used will be briefly described below.
FIG. 1 is a schematic diagram of a "one-machine-one-module" database update architecture according to the present invention;
FIG. 2 is a schematic diagram of the data update process of the database of the present invention.
Detailed Description
To facilitate an understanding of the invention, the invention will now be described more fully with reference to the accompanying drawings. Preferred embodiments of the present invention are shown in the drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
The method of the invention aims at the problems that the mathematical model of the motor is high in coupling, and motor parameters of different specifications, different manufacturers and different working conditions are different, and provides a one-machine-one-module fault data framework of an early motor with relative multi-mode parameters on a time axis, a database with self-learning capability is established, and historical parameters are added or replaced by combining an effectiveness analysis learning method of adjacent rules. The framework can provide effective data basis for the online diagnosis of the motor in the later period, and can effectively solve the difficulty in unified determination of the threshold value during fault judgment.
Example (b):
a motor database establishing method comprises the following steps:
step 1: manually inputting real-time data obtained through experiments and field tests, and continuously correcting data of fault influence factors;
step 2: measuring real-time data, inputting the real-time data as the current amount of historical data, and storing the current amount of the historical data in a database; meanwhile, inputting real-time data into a fault prediction model;
and step 3: the database stores the real-time data and the historical data after conversion calculation;
and 4, step 4: the fault prediction model is combined with historical data of the database and input real-time data to carry out fault analysis, and data analysis results are continuously and reversely input into the database and are stored after being converted in the database;
and 5: aiming at the storage form of data in a database, a one-machine-one-module database architecture is adopted, namely, the motor fault is preliminarily judged by using priori knowledge, and meanwhile, the database parameters are continuously updated by self in an online mode;
step 6: displaying the analysis result of the real-time fault prediction model by using a display screen;
in the step 5, the process of self-updating the database parameters is divided into two types, one is to update unified prior data, and the other is to update the fault related parameter information of a single motor individual.
The database is not only a data storage unit, but also a deep processing unit based on historical relativity parameters of large data streams. The database not only stores motor steady state parameters, transient parameters, time domain distortion and each subharmonic content acquired by the preceding stage data conversion module, but also contains data processed secondarily in the database, such as parameters of the ratio of the third harmonic content to the fundamental wave content, the ratio of the fundamental wave sideband content to the fundamental wave content and the like. For the data of the secondary processing, the related fluctuation of the slip deviation, the harmonic wave and other contents caused by the load change in the asynchronous motor is considered. Meanwhile, the database parameters are divided into two sets of storage mechanisms which are respectively used for storing power frequency and variable frequency data.
Aiming at the problem of storage capacity of a database, an increase and replacement mechanism for effective learning of data proximity is established, and the relation between transient model parameters and load change and harmonic content introduced during power supply variable frequency speed regulation is contrastively analyzed. And comparing and analyzing the relation between the current time domain distortion signal and the frequency domain harmonic content and the relation between the load change and the harmonic content introduced during the variable frequency speed regulation of the power supply. Data updating is performed by a time axis sampling and removing method using a unit transient resistance, a unit transient inductance and a unit transient reactance as a guide, and fig. 2 is a data updating process of a database.
In the present invention, in the databaseThe sample sets for the forward and reverse distortion amounts with respect to the time axis are { x, respectively1,x2,…,xnAnd { y }1,y2,…,ynAnd calculating the average value of each data by using a formula (1) according to the new input value x, obtaining the difference value between the real-time data and the average value by using a formula (2), obtaining the maximum difference value between each sample data by using a formula (3), comparing the difference value between the sample maximum difference value data and the real-time data, and replacing the sample data when the difference value of the real-time data is greater than the sample maximum difference value data. The new input value y is calculated in the same way.
The correlation R of the forward distortion amount and the reverse distortion amount is calculated using formula (4), which reflects the ratio of the total variation of the dependent variable that can be autoregressive.
Claims (2)
1. A motor database establishing method is characterized by comprising the following steps:
step 1: manually inputting real-time data obtained through experiments and field tests, and continuously correcting data of fault influence factors;
step 2: measuring real-time data, inputting the real-time data as the current amount of historical data, and storing the current amount of the historical data in a database; meanwhile, inputting real-time data into a fault prediction model;
and step 3: the database stores the real-time data and the historical data after conversion calculation;
and 4, step 4: the fault prediction model is combined with historical data of the database and input real-time data to carry out fault analysis, and data analysis results are continuously and reversely input into the database and are stored after being converted in the database;
and 5: aiming at the storage form of data in a database, a one-machine-one-module database architecture is adopted, namely, the motor fault is preliminarily judged by using priori knowledge, and meanwhile, the database parameters are continuously updated by self in an online mode;
step 6: and displaying the analysis result of the real-time fault prediction model by using a display screen.
2. The method for establishing the motor database according to claim 1, wherein in the step 5, the self-updating database parameter process is divided into two types, one is to update uniform prior data, and the other is to update the fault-related parameter information of a single motor.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112350529A (en) * | 2020-10-13 | 2021-02-09 | 深圳微米自动化科技有限公司 | Servo motor maintenance method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1997049977A1 (en) * | 1996-06-24 | 1997-12-31 | Arcelik A.S. | Model-based fault detection system for electric motors |
| AU2017100471A4 (en) * | 2016-07-22 | 2017-06-01 | Abb Schweiz Ag | A condition monitoring device for monitoring an electric machine |
| CN107229017A (en) * | 2017-01-06 | 2017-10-03 | 科诺伟业风能设备（北京）有限公司 | A kind of wind generating set pitch control battery abnormal failure Forecasting Methodology |
| CN108414864A (en) * | 2018-03-16 | 2018-08-17 | 贵州乌江水电开发有限责任公司东风发电厂 | A kind of method for diagnosing faults for protective relaying device |
| CN108427986A (en) * | 2018-02-26 | 2018-08-21 | 中车青岛四方机车车辆股份有限公司 | A kind of production line electrical fault prediction technique and device |
| CN109765484A (en) * | 2018-10-25 | 2019-05-17 | 青岛鹏海软件有限公司 | Motor on-line monitoring and fault diagonosing method based on " correct tree " model |
-
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- 2020-03-02 CN CN202010135060.XA patent/CN111459906B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1997049977A1 (en) * | 1996-06-24 | 1997-12-31 | Arcelik A.S. | Model-based fault detection system for electric motors |
| AU2017100471A4 (en) * | 2016-07-22 | 2017-06-01 | Abb Schweiz Ag | A condition monitoring device for monitoring an electric machine |
| CN107229017A (en) * | 2017-01-06 | 2017-10-03 | 科诺伟业风能设备（北京）有限公司 | A kind of wind generating set pitch control battery abnormal failure Forecasting Methodology |
| CN108427986A (en) * | 2018-02-26 | 2018-08-21 | 中车青岛四方机车车辆股份有限公司 | A kind of production line electrical fault prediction technique and device |
| CN108414864A (en) * | 2018-03-16 | 2018-08-17 | 贵州乌江水电开发有限责任公司东风发电厂 | A kind of method for diagnosing faults for protective relaying device |
| CN109765484A (en) * | 2018-10-25 | 2019-05-17 | 青岛鹏海软件有限公司 | Motor on-line monitoring and fault diagonosing method based on " correct tree " model |
Non-Patent Citations (1)
| Title |
|---|
| 肖丽等: ""按照故障检测原理分类"", 《现代电机故障诊断与容错技术》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112350529A (en) * | 2020-10-13 | 2021-02-09 | 深圳微米自动化科技有限公司 | Servo motor maintenance method |
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