CN202844936U - Intelligent monitoring diagnosis system for mixer - Google Patents
Intelligent monitoring diagnosis system for mixer Download PDFInfo
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- CN202844936U CN202844936U CN201220406501.6U CN201220406501U CN202844936U CN 202844936 U CN202844936 U CN 202844936U CN 201220406501 U CN201220406501 U CN 201220406501U CN 202844936 U CN202844936 U CN 202844936U
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- batch mixer
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Abstract
The utility model relates to an intelligent monitoring diagnosis system for a mixer, comprising a torsion transmitter and a computer information detection processing system which consists of an electronic computer, a server, an electronic controller and an electric automatic control cabinet. The torsion transmitter comprises a torsion sensor, an electronic amplifier and an A/D (analog-to-digital) converter, and the torsion transmitter is arranged at a shaft head of a driving pinion shaft of the mixer through an electromechanical integration mode. In the process of working of the mixer, a digital signal from the torsion transmitter directly enters the electronic computer, the electronic computer performs intelligent analysis and judgment through a graphic fuzzy neural network expert database and then sends an instruction to the electronic controller, and the electronic controller controls the electric automatic control cabinet to realize the effective reasonable control of a frequency conversion speed adjustment motor; and an on-line self-adapting trend prediction method based on a GA technology is introduced, and BP (back propagation) network structural parameters are dynamically optimized by using GA.
Description
Technical field
The utility model belongs to device intelligence monitoring, diagnosing control technology field, is specifically related to a kind of batch mixer intelligent monitoring diagnosis system.
Background technology
Batch mixer is a kind of industrial furnace equipment that reaches the mixing granular material by the cylindrical shell revolution, be widely used in the industrial enterprises such as metallurgy, smelting, chemical industry, building materials, it is the main production equipments of above-mentioned enterprise, its equipment availability, and can start the economic benefit that three indexs of equipment control such as rate, utilization rate and efficient directly have influence on enterprise.
Batch mixer is when work, under the certain prerequisite in cylindrical shell inclination angle, the speed of gyration of cylindrical shell, the time of staying of material and the mixture homogeneity of material have been determined, thereby directly cause the height of product yield and the quality of quality, and the speed of gyration of batch mixer cylindrical shell is controlled by slip electric motor or frequency control motor.Therefore, slip electric motor or frequency control motor being carried out intellectuality control, is very necessary.Take frequency control motor as example, choose reasonable alternating current frequency, particularly dynamically the parameter such as controlled frequency instantaneous value, frequency change gradient is important content in the batch mixer safe practice operational procedure, but also is a long-term problem undecided and anxious to be resolved.The factor that affects the adjusting of batch mixer speed of gyration is a lot, according to equipment breakdown and fault statistics analysis, is main cause owing to the batch mixer drum shaft causes the increase of equipment steering resistance to " vibration ".At present a lot of enterprises only with operative employee's experience, this method is extremely inaccurate when the equipment of use because it and operator's experience accumulation, the state of mind and sense of responsibility link directly, uncertain factor is a lot.
In equipment running process, it is a lot of to affect the factor that input torque increases, and how from complicated factor, finds out in real time real reason, is the key of intelligent monitoring diagnostic techniques.The structural parameters of current device status predication neutral net determine by artificial experience and experimental data mainly that still because network structure is poor, precision of prediction is low, thereby can not satisfy the working condition of industry spot complexity.In order to settle the matter once and for all, a lot of factory and enterprises and universities and colleges, research institution have all carried out exploring widely, and the utility model produces under this technical background.
The utility model content
In order to overcome the above-mentioned shortcoming of prior art
,The utility model provides a kind of intelligent monitoring control system of the Neural Network Online self adaptation trend predicting method based on the GA technology, thereby realize effectively dynamic monitoring and the diagnosis of alternating current frequency, guaranteed the batch mixer intelligent monitoring diagnosis system to the accurate control of batch mixer speed of gyration.
The technical scheme that its technical problem that solves the utility model adopts is: a kind of batch mixer intelligent monitoring diagnosis system comprises torsion transmitter and the computerized information detecting processing system that is made of electronic computer, server, electronic controller and electric automatic control cabinet; Described torsion speed changer comprises torsion torque sensor, electron-amplifier and A/D converter, and the torsion speed changer is installed in batch mixer driving pinion axle spindle nose by the electromechanical integration mode.
The configuration of described electronic computer is not less than Duo two generations double-core CPU, the 4G internal memory, and the 500G hard drive space, its input and torsion transmitter, server link, and its output and electronic controller, server link.
Described server is equipped with graphical fuzzy neural network expert knowledge library.
Described electronic controller is a kind of servo actuator based on electromechanical integration technology, and its input and electronic computer link, and its output and electric automatic control cabinet link.
Input and the electronic controller of described electric automatic control cabinet link, and its output and frequency control motor link.
The beneficial effects of the utility model are: in the batch mixer course of work, data signal from the torsion transmitter directly taps into electronic computer, electronic computer carries out intelligent analysis by the graphical fuzzy neural network experts database that is arranged at server and judges, again electronic controller is sent instruction, handle the realization of electric automatic control cabinet to the effective and reasonable control of frequency control motor by electronic controller; The utility model is when carrying out described network structure design, introduce a kind of online adaptive trend predicting method based on the GA technology, utilize GA that the BP network architecture parameters is carried out dynamic optimization, adopt simultaneously GA and BP combined training that existing Learning Algorithms is improved, thereby dynamically determine optimum network architecture parameters, in the batch mixer the field, obtain satisfied on-line prediction effect.
Description of drawings
Fig. 1 is the utility model structural principle schematic diagram.
Among the figure: 1-torsion transmitter, 2-electronic computer, 3-server, 4-electronic controller, 5-electric automatic control cabinet.
The specific embodiment
Below in conjunction with drawings and Examples the utility model is further specified.
Referring to Fig. 1, a kind of batch mixer intelligent monitoring diagnosis system comprises torsion transmitter 1 and the computerized information detecting processing system that is made of electronic computer 2, server 3, electronic controller 4 and electric automatic control cabinet 5; Described torsion speed changer 1 comprises torsion torque sensor, electron-amplifier and A/D converter, and torsion speed changer 1 is installed in batch mixer driving pinion axle spindle nose by the electromechanical integration mode; The configuration of described electronic computer 2 is not less than Duo two generations double-core CPU, the 4G internal memory, and the 500G hard drive space, its input and torsion transmitter 1, server 3 link, and its output and electronic controller 4, server 3 link; Still the present situation of mainly determining by artificial experience and experimental data for the structural parameters that thoroughly change current device status predication neutral net, the utility model is introduced a kind of structural parameters of determining neutral net based on the online adaptive trend predicting method of GA technology, thereby dynamically determine optimum network architecture parameters value, so be equipped with graphical fuzzy neural network expert knowledge library at described server 3; Described electronic controller is a kind of servo actuator based on electromechanical integration technology, and its input and electronic computer 2 link, and its output and electric automatic control cabinet 5 link; Described electric automatic control cabinet 5 belongs to the chief component of frequency control motor control system, and its function is to reach the purpose of adjusting the frequency control motor rotating speed by the alternating current frequency of adjusting input.
The utility model is in the batch mixer course of work, data signal from the torsion transmitter directly taps into electronic computer, electronic computer carries out intelligent analysis by the graphical fuzzy neural network experts database that is arranged at server and judges, again electronic controller is sent instruction, handle the realization of electric automatic control cabinet to the effective and reasonable control of frequency control motor by electronic controller.The utility model is when carrying out described network structure design, introduce a kind of online adaptive trend predicting method based on the GA technology, utilize GA that the BP network architecture parameters is carried out dynamic optimization, adopt simultaneously GA and BP combined training that existing Learning Algorithms is improved, thereby dynamically determine optimum network architecture parameters, in the batch mixer the field, obtain satisfied on-line prediction effect.
Claims (4)
1. a batch mixer intelligent monitoring diagnosis system is characterized in that: comprise torsion transmitter and the computerized information detecting processing system that is made of electronic computer, server, electronic controller and electric automatic control cabinet; Described torsion speed changer comprises torsion torque sensor, electron-amplifier and A/D converter, and the torsion speed changer is installed in batch mixer driving pinion axle spindle nose by the electromechanical integration mode.
2. batch mixer intelligent monitoring diagnosis system as claimed in claim 1, it is characterized in that: the input of described electronic computer and torsion transmitter, server link, and its output and electronic controller, server link.
3. batch mixer intelligent monitoring diagnosis system as claimed in claim 1, it is characterized in that: described electronic controller is a kind of servo actuator based on electromechanical integration technology, its input and electronic computer link, and its output and electric automatic control cabinet link.
4. batch mixer intelligent monitoring diagnosis system as claimed in claim 1, it is characterized in that: input and the electronic controller of described electric automatic control cabinet link, and its output and frequency control motor link.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201220406501.6U CN202844936U (en) | 2012-08-16 | 2012-08-16 | Intelligent monitoring diagnosis system for mixer |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201220406501.6U CN202844936U (en) | 2012-08-16 | 2012-08-16 | Intelligent monitoring diagnosis system for mixer |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN202844936U true CN202844936U (en) | 2013-04-03 |
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Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201220406501.6U Expired - Fee Related CN202844936U (en) | 2012-08-16 | 2012-08-16 | Intelligent monitoring diagnosis system for mixer |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN202844936U (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104353387A (en) * | 2014-11-06 | 2015-02-18 | 南通大学 | Control method of intelligent column type hopper mixing machine |
| CN106964287A (en) * | 2017-05-01 | 2017-07-21 | 无锡市翱宇特新科技发展有限公司 | A kind of intelligent chemical industry blending tank |
| CN110954264A (en) * | 2019-12-12 | 2020-04-03 | 浙江省计量科学研究院 | Wireless state monitoring and diagnosing system of chassis dynamometer for detecting automobile exhaust pollutants |
-
2012
- 2012-08-16 CN CN201220406501.6U patent/CN202844936U/en not_active Expired - Fee Related
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104353387A (en) * | 2014-11-06 | 2015-02-18 | 南通大学 | Control method of intelligent column type hopper mixing machine |
| CN104353387B (en) * | 2014-11-06 | 2016-05-11 | 南通大学 | The control method of intelligence pillar hopper mixer |
| CN106964287A (en) * | 2017-05-01 | 2017-07-21 | 无锡市翱宇特新科技发展有限公司 | A kind of intelligent chemical industry blending tank |
| CN110954264A (en) * | 2019-12-12 | 2020-04-03 | 浙江省计量科学研究院 | Wireless state monitoring and diagnosing system of chassis dynamometer for detecting automobile exhaust pollutants |
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Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20130403 Termination date: 20150816 |
|
| EXPY | Termination of patent right or utility model |