CN110597144A - Motor sensor data acquisition unit and data acquisition method - Google Patents
Motor sensor data acquisition unit and data acquisition method Download PDFInfo
- Publication number
- CN110597144A CN110597144A CN201910940885.6A CN201910940885A CN110597144A CN 110597144 A CN110597144 A CN 110597144A CN 201910940885 A CN201910940885 A CN 201910940885A CN 110597144 A CN110597144 A CN 110597144A
- Authority
- CN
- China
- Prior art keywords
- motor
- sampling
- data acquisition
- single chip
- chip microcomputer
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
- G05B19/0423—Input/output
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Control Of Electric Motors In General (AREA)
Abstract
A motor sensor data acquisition unit and a data acquisition method comprise a single chip microcomputer, and a temperature acquisition module, an ADC acquisition module, a 485 bus driving module and a storage module which are connected with the single chip microcomputer; the single chip microcomputer starts a DSP unit, processes collected temperature, voltage and current signals, removes periodic and sudden interference noise through a filtering algorithm, then identifies characteristic faults of the motor through a mechanism model algorithm, accelerates sampling speed when a known type is detected, identifies fault information more accurately, comprehensively collects signals of vibration, current and temperature of the motor, can judge edge calculation at a collection end, and does not need to wait for a network host to issue a command to a bottom layer to execute. In the field environment where the network bandwidth is not too high, the requirement of data uploading on the network can be reduced, and the real-time performance of the network can be improved. And when the network is abnormal, the interference of the network to the site can be prevented, and production accidents are prevented.
Description
Technical Field
The invention relates to the technical field of motor control, in particular to a motor sensor data acquisition unit and a data acquisition method.
Background
In the current industrial field, the vibration, the current and the temperature of the motor are rarely collected together, and in some fields, only the current and the temperature are collected to analyze the monitoring property, but the predicitive analysis is not carried out on the equipment, and only the period of the equipment with faults can be estimated to take charge of maintenance. Therefore, manpower and material resources can be wasted to periodically and comprehensively overhaul the equipment, and the production efficiency is not improved.
Disclosure of Invention
In order to solve the technical problems in the background art, the invention provides a motor sensor data acquisition unit and a data acquisition method, which can comprehensively acquire signals of vibration, current and temperature of a motor, judge the signals at an acquisition end and perform edge calculation without waiting for a network host to issue commands to a bottom layer to execute. In the field environment where the network bandwidth is not too high, the requirement of data uploading on the network can be reduced, and the real-time performance of the network can be improved. And when the network is abnormal, the interference of the network to the site can be prevented, and production accidents are prevented.
In order to achieve the purpose, the invention adopts the following technical scheme:
a motor sensor data acquisition unit comprises a single chip microcomputer, and a temperature acquisition module, an ADC acquisition module, a 485 bus driving module and a storage module which are connected with the single chip microcomputer;
the ADC acquisition module acquires three-phase current and voltage of a single motor;
the temperature acquisition module acquires the temperature of a single motor;
the ADC acquisition module is also used for acquiring vibration signals on two sides of the shaft of a single machine;
after signals are collected for a single motor, the signals are filtered and screened, and are uploaded according to a certain frequency.
According to the data acquisition method of the motor sensor data acquisition unit, the single chip microcomputer starts the DSP unit, acquired temperature, voltage and current signals are processed, a limiting filtering method, a jitter eliminating filtering method or a recursive average filtering method is adopted, periodic and sudden interference noise is removed through the filtering algorithms, then a mechanism model algorithm is used for identifying characteristic faults of the motor, when a known type is detected, the single chip microcomputer accelerates the sampling speed, fault information is identified more accurately, and then the data are uploaded to the cloud end of a server.
By adopting a dynamic acquisition method, the sampling speed and the number of sampling points are reduced to the minimum standard of sampling when the sampling is carried out at a low speed, the Nyquist sampling principle needs to be met, and the frequency of signal acquisition is achieved. And during high-speed sampling, the signal acquisition speed is increased to more than 10 times of the lowest sampling speed for processing.
The single chip microcomputer also performs comparison through the storage function of the storage module and periodically stores historical data to play a part of functions of the motor black box, characteristic data are obtained through analysis of damaged data of the motor, and the data are uploaded to the cloud, so that a new fault data model is obtained.
The single chip microcomputer is also reserved with a wireless communication interface and is butted with a 5G module in the future to realize 5G communication.
Compared with the prior art, the invention has the beneficial effects that:
1. under the complex working condition on site, the intelligent motor current, temperature and vibration acquisition system is constructed, data acquisition is carried out through various sensors, the acquired data are subjected to edge calculation analysis, the uploading bandwidth is reduced to reduce the burden of a communication network, and finally the data are transmitted to an upper computer engineer station through an uploading board for cloud platform analysis.
2. Due to the adoption of the dynamically adjustable sampling speed, the method can acquire the characteristic points slowly when the characteristic points are not detected at ordinary times, reduces network uploading, saves bandwidth, accelerates acquisition when the characteristic points appear, and solves the problem of more accurate positioning.
3. As a data acquisition node at the bottommost layer, some data can be stored periodically through a storage function for comparison, the function of a part of motor black boxes is achieved, characteristic data are obtained through analysis of the damaged data of the motor, and the data are uploaded to the cloud, so that a new fault data model is obtained.
Drawings
FIG. 1 is a block diagram of a motor sensor data acquisition unit of the present invention.
Detailed Description
The following detailed description of the present invention will be made with reference to the accompanying drawings.
As shown in fig. 1, the motor sensor data acquisition unit includes a single chip microcomputer, and a temperature acquisition module, an ADC acquisition module, a 485 bus driving module, and a storage module connected thereto;
the ADC acquisition module acquires three-phase current and voltage of a single motor;
the temperature acquisition module acquires the temperature of a single motor;
the ADC acquisition module is also used for acquiring vibration signals on two sides of the shaft of a single machine;
after signals are collected for a single motor, the signals are filtered and screened, and are uploaded according to a certain frequency.
The motor sensor data acquisition unit adopts an embedded 32-bit singlechip as a core and acquires vibration signals, current signals and temperature signals of a field motor. The voltage signal collected by the sensor is processed by using a 16-bit high-speed multi-channel ADC. And (3) using a DSP core of the embedded 32-bit singlechip to carry out numerical filtering on the acquired data and remove interference caused by periodic and burst noises by using an algorithm. Variable sampling speeds are employed so that the program can adapt to a wide variety of field scenarios.
According to the data acquisition method of the motor sensor data acquisition unit, the single chip microcomputer starts the DSP unit, acquired temperature, voltage and current signals are processed, a limiting filtering method, a jitter eliminating filtering method or a recursive average filtering method is adopted, periodic and sudden interference noise is removed through the filtering algorithms, then a mechanism model algorithm is used for identifying characteristic faults of the motor, when a known type is detected, the single chip microcomputer accelerates the sampling speed, fault information is identified more accurately, and then the data are uploaded to the cloud end of a server.
In the aspect of uploading data, due to the adoption of the dynamically adjustable sampling speed, the data can be slowly acquired when the characteristic points are not detected at ordinary times, the network uploading is reduced, the bandwidth is saved, and when the characteristic points appear, the acquisition is accelerated, and the problem of more accurate positioning is solved.
By adopting a dynamic sampling method, when the sampling value is not changed much, the sampling speed is reduced and the uploading speed is calculated, and under the condition of more acquisition units, the uploading bandwidth can be effectively reduced, so that the transmission network does not need to be in a saturated state all the time. When the sampling value has large variation amplitude, the sampling speed and the calculation uploading speed are accelerated, the fault information is judged more accurately by the sampling precision, the DSP algorithm is used for extracting the characteristic value with higher precision from the sampling value, and the data is uploaded to the cloud by adopting a dynamic sampling method. And under the condition that the number of the acquisition units is large, the uploading bandwidth can be effectively reduced, so that the transmission network does not need to be in a saturated state all the time. When the sampling value has large variation amplitude and the periodicity of characteristic collection is not strong, or a collected sample point exceeds a collected average value, or a frequency domain value calculated by FFT exceeds a conventional range, the sampling speed and the calculation uploading speed are increased, the method for improving the sampling precision can be used for more accurately judging fault information, a DSP algorithm is used for extracting the higher-precision characteristic value of the sampling value, and the application scene uploaded by a plurality of node collection nodes is more efficient.
According to the dynamic acquisition method, during low-speed sampling, the sampling speed and the number of sampling points are reduced to the minimum standard of sampling, the Nyquist sampling principle needs to be met, and the frequency of signal acquisition is achieved. And during high-speed sampling, the signal acquisition speed is increased to be more than 10 times of the lowest sampling speed for processing, and the specific times are determined according to the parameters of an actual motor manufacturer.
Minimum sampling standard: the current, the temperature and the vibration have different sampling standards, wherein the current of the alternating current has two different standards of 50Hz and 60 Hz. In the case of 50Hz a minimum of 50 sample points must be taken within 20ms to obtain an effective value of the current. The 60Hz case requires 50 samples to be acquired within 16.666 ms. The temperature was calculated over 20 acquisitions per second, since the temperature varied more slowly. The processing of vibration signal is then more complicated, and vibration signal needs set for according to the rotational speed of motor and the natural frequency of axle vibration, to ordinary small-size motor, generally sets up 5 KHz's sampling frequency, to the vibration signal below 2KHz gather can. Since the vibration signal needs to calculate the FFT frequency domain signal, the sampling calculation of the minimum 64 points needs to be satisfied.
High-speed sampling standard: the current, the temperature and the vibration have different sampling standards, 1000 points are required to be collected within 20ms under the condition of 50Hz of alternating current, the 1000 points are taken according to a mode of 10 groups for algorithm calculation, and 100 points are remained for calculation to calculate an effective value. The same point calculation needs to be collected within 16.666ms in the case of 60 Hz. The temperature signal also needs to collect 100 points per second for algorithm calculation to remove interference. The highest sampling of the vibration signal needs 1024 points for calculation, and if the lowest sampling frequency is 5KHz, the highest sampling frequency is at least 50 KHz.
And (3) algorithm calculation: the maximum and minimum values are removed by using an amplitude limiting filtering method, and then the numerical value is calculated by calculating the average value. For the calculation of the effective value, an RMS (root mean square) algorithm is adopted, namely each numerical value is squared and then added and divided by the number, and the numerical value of the square is taken as the effective value of the current. In the calculation of the vibration data, because the acquired numerical values are all acceleration numerical values, the acceleration is required to be subjected to integral calculation when the vibration speed is required to be obtained, and the vibration speed is required to be subjected to integral calculation when the vibration displacement is required to be obtained. And the time-frequency domain signal conversion needs an FFT algorithm for calculation.
The single chip microcomputer also performs comparison through the storage function of the storage module and periodically stores historical data to play a part of functions of the motor black box, characteristic data are obtained through analysis of damaged data of the motor, and the data are uploaded to the cloud, so that a new fault data model is obtained.
The single chip microcomputer is also reserved with a wireless communication interface and is butted with a 5G module in the future to realize 5G communication. The wired mode is still reserved in the wired aspect, RS485 communication is adopted to communicate with the existing equipment, and the wired mode and the wireless mode are mutually backup.
The above embodiments are implemented on the premise of the technical solution of the present invention, and detailed embodiments and specific operation procedures are given, but the scope of the present invention is not limited to the above embodiments. The methods used in the above examples are conventional methods unless otherwise specified.
Claims (5)
1. A motor sensor data acquisition unit is characterized by comprising a single chip microcomputer, and a temperature acquisition module, an ADC acquisition module, a 485 bus driving module and a storage module which are connected with the single chip microcomputer;
the ADC acquisition module acquires three-phase current and voltage of a single motor;
the temperature acquisition module acquires the temperature of a single motor;
the ADC acquisition module is also used for acquiring vibration signals on two sides of the shaft of a single machine;
after signals are collected for a single motor, the signals are filtered and screened, and are uploaded according to a certain frequency.
2. The data acquisition method of the motor sensor data acquisition unit of claim 1, wherein the single chip starts the DSP unit, processes the acquired temperature, voltage, and current signals, and uses a limiting filtering method, a jitter-eliminating filtering method, or a recursive average filtering method to remove periodic and sudden interference noise through these filtering algorithms, and then uses a mechanism model algorithm to identify a characteristic fault of the motor, and when a known type is detected, the single chip accelerates a sampling speed, more accurately identifies fault information, and then uploads the data to a server cloud.
3. The data acquisition method of the motor sensor data acquisition unit as claimed in claim 2, wherein a dynamic acquisition method is adopted, and in low-speed sampling, the sampling speed and the number of sampling points are reduced to the minimum standard of sampling, so that the nyquist sampling principle is satisfied, and the signal acquisition frequency is reached. And during high-speed sampling, the signal acquisition speed is increased to more than 10 times of the lowest sampling speed for processing.
4. The data acquisition method of the motor sensor data acquisition unit as claimed in claim 2, wherein the single chip microcomputer further performs comparison through periodically stored historical data of a storage function of the storage module to play a part of a function of a motor black box, analyzes the damaged data of the motor to obtain characteristic data, and uploads the characteristic data to a cloud so as to obtain a new fault data model.
5. The motor sensor data acquisition unit of claim 1, wherein the single chip microcomputer is further reserved with a wireless communication interface, and is to be docked with a 5G module in the future to realize 5G communication.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910940885.6A CN110597144A (en) | 2019-09-30 | 2019-09-30 | Motor sensor data acquisition unit and data acquisition method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910940885.6A CN110597144A (en) | 2019-09-30 | 2019-09-30 | Motor sensor data acquisition unit and data acquisition method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110597144A true CN110597144A (en) | 2019-12-20 |
Family
ID=68865135
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910940885.6A Pending CN110597144A (en) | 2019-09-30 | 2019-09-30 | Motor sensor data acquisition unit and data acquisition method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110597144A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112180299A (en) * | 2020-09-28 | 2021-01-05 | 国网山东省电力公司莱芜供电公司 | 10kV distribution transformer turn-to-turn short circuit fault online monitoring method |
| CN112382072A (en) * | 2020-10-29 | 2021-02-19 | 鞍钢集团自动化有限公司 | Non-standardized signal acquisition method based on 5G module |
| CN113359965A (en) * | 2021-06-18 | 2021-09-07 | 浪潮电子信息产业股份有限公司 | Temperature adjusting method and related assembly |
| CN113865695A (en) * | 2021-09-08 | 2021-12-31 | 杭州安脉盛智能技术有限公司 | Wireless vibration sensor integrated with fault judgment algorithm |
| CN113885420A (en) * | 2021-11-10 | 2022-01-04 | 山东电工电气集团有限公司 | Data acquisition control method for disconnecting link state monitoring device |
| WO2023045338A1 (en) * | 2021-09-27 | 2023-03-30 | 大连理工大学 | Method for real-time data dynamic online rapid processing at edge end |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201621056U (en) * | 2010-03-26 | 2010-11-03 | 哈尔滨国力电气有限公司 | Main-fan safe-running online monitoring system |
| CN105738809A (en) * | 2016-05-16 | 2016-07-06 | 成都慧成智能电气有限公司 | Monitoring device for temperature rise and vibration of motor and realizing method thereof |
| CN106535253A (en) * | 2016-11-23 | 2017-03-22 | 北京必创科技股份有限公司 | Method for dynamic acquisition and transmission of wireless data |
| CN106855787A (en) * | 2016-12-30 | 2017-06-16 | 深圳市易联网络技术有限公司 | A kind of method of sampling of Internet of Things analog signal |
| CN108387309A (en) * | 2018-04-25 | 2018-08-10 | 哈尔滨电气股份有限公司 | A kind of turbine shafting vibrating data collection, pretreatment and remotely send system |
| CN209118121U (en) * | 2019-01-17 | 2019-07-16 | 山东胜业智控科技有限公司 | Motor and connecting transmission mechanism running state monitoring device |
| CN110133500A (en) * | 2019-05-13 | 2019-08-16 | 中机国际工程设计研究院有限责任公司 | Motor on-line monitoring and failure omen diagnostic system and method based on multi-layer framework |
-
2019
- 2019-09-30 CN CN201910940885.6A patent/CN110597144A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201621056U (en) * | 2010-03-26 | 2010-11-03 | 哈尔滨国力电气有限公司 | Main-fan safe-running online monitoring system |
| CN105738809A (en) * | 2016-05-16 | 2016-07-06 | 成都慧成智能电气有限公司 | Monitoring device for temperature rise and vibration of motor and realizing method thereof |
| CN106535253A (en) * | 2016-11-23 | 2017-03-22 | 北京必创科技股份有限公司 | Method for dynamic acquisition and transmission of wireless data |
| CN106855787A (en) * | 2016-12-30 | 2017-06-16 | 深圳市易联网络技术有限公司 | A kind of method of sampling of Internet of Things analog signal |
| CN108387309A (en) * | 2018-04-25 | 2018-08-10 | 哈尔滨电气股份有限公司 | A kind of turbine shafting vibrating data collection, pretreatment and remotely send system |
| CN209118121U (en) * | 2019-01-17 | 2019-07-16 | 山东胜业智控科技有限公司 | Motor and connecting transmission mechanism running state monitoring device |
| CN110133500A (en) * | 2019-05-13 | 2019-08-16 | 中机国际工程设计研究院有限责任公司 | Motor on-line monitoring and failure omen diagnostic system and method based on multi-layer framework |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112180299A (en) * | 2020-09-28 | 2021-01-05 | 国网山东省电力公司莱芜供电公司 | 10kV distribution transformer turn-to-turn short circuit fault online monitoring method |
| CN112382072A (en) * | 2020-10-29 | 2021-02-19 | 鞍钢集团自动化有限公司 | Non-standardized signal acquisition method based on 5G module |
| CN113359965A (en) * | 2021-06-18 | 2021-09-07 | 浪潮电子信息产业股份有限公司 | Temperature adjusting method and related assembly |
| CN113865695A (en) * | 2021-09-08 | 2021-12-31 | 杭州安脉盛智能技术有限公司 | Wireless vibration sensor integrated with fault judgment algorithm |
| WO2023045338A1 (en) * | 2021-09-27 | 2023-03-30 | 大连理工大学 | Method for real-time data dynamic online rapid processing at edge end |
| CN113885420A (en) * | 2021-11-10 | 2022-01-04 | 山东电工电气集团有限公司 | Data acquisition control method for disconnecting link state monitoring device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110597144A (en) | Motor sensor data acquisition unit and data acquisition method | |
| Tang et al. | IoT-based signal enhancement and compression method for efficient motor bearing fault diagnosis | |
| CN105403402B (en) | The monitoring method of driving chain of wind generating set Torsional vibration state | |
| EP1531376B1 (en) | Monitoring and data processing equipment for wind turbines and predictive maintenance system for wind power stations | |
| CN103626003A (en) | Elevator fault detecting method and system | |
| CN110530507B (en) | Edge calculation method, medium, and system for monitoring rotating device | |
| CN104564542B (en) | A kind of fault diagnosis system and its diagnostic method based on big data technology | |
| CN105043770A (en) | Wind turbine generator abnormal vibration judging method and apparatus thereof | |
| CN113701877A (en) | Vibration monitoring method for overhead line system | |
| CN116434372B (en) | Intelligent data acquisition system and working condition identification system for variable working condition equipment | |
| CN102721462A (en) | Method for quickly computing Bode plot and Nyquist plot of rotary mechanical vehicle starting and parking processes | |
| CN104111154A (en) | Vibration trend analysis method and vibration trend analysis system of wind generating set | |
| CN113027658A (en) | Real-time state evaluation method for water turbine runner and application thereof | |
| CN112177865B (en) | Method for solving marking noise and insufficient marks in fan fault detection | |
| CN112861328B (en) | Generator damping evaluation device and method based on random response signals | |
| CN108106827B (en) | Method for identifying loosening of amplifier terminal under rotor platform rotation speed state | |
| CN112347944B (en) | Mechanical transmission fault detection method based on zero value proportion frequency spectrum characteristics | |
| CN110597145A (en) | Full life cycle management data acquisition system of electrical equipment | |
| CN113218653B (en) | Offshore wind power gear box monitoring method and device based on low-frequency disturbance elimination | |
| KR101468627B1 (en) | Wind Turbine Data Classification System | |
| CN111400959B (en) | Blade fault diagnosis method and device for wind generating set | |
| CN113138337B (en) | Three-phase motor fault diagnosis method based on state division and frequency band synchronous correction | |
| CN108520093A (en) | A kind of Trouble Diagnostic Method of Machinery Equipment and device in knowledge based library | |
| CN211874471U (en) | Intelligent monitoring device for electric parameter edge of oil well | |
| CN109782064B (en) | Wind power plant output impedance frequency characteristic test analysis method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20191220 |
|
| RJ01 | Rejection of invention patent application after publication |