CN105003453A - Online monitoring and fault diagnosis system of mine fan - Google Patents
Online monitoring and fault diagnosis system of mine fan Download PDFInfo
- Publication number
- CN105003453A CN105003453A CN201410353496.0A CN201410353496A CN105003453A CN 105003453 A CN105003453 A CN 105003453A CN 201410353496 A CN201410353496 A CN 201410353496A CN 105003453 A CN105003453 A CN 105003453A
- Authority
- CN
- China
- Prior art keywords
- data
- module
- monitoring
- fan
- diagnosis
- 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
Landscapes
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention provides a remote real-time monitoring and fault diagnosis system of a mine fan based on an LabVIEW platform. The system comprises hardware of an industrial control computer, a terminal board, a PCI data acquiring card, signal conditioning equipment and a vibration speed sensor, and comprises software of a data acquiring module, a data memory and management module, a data display module, a history inquiring module, a fault diagnosis module and a network distribution module. The system realizes real-time acquisition of fan vibration signals, dynamic monitoring of equipment operation states, the setting, display and warning prompt of equipment operation normal values, warning values and stop range values and the storage and inquiry of such related data as various fault data and diagnosis results, and adopts an analytic hierarchy process algorithm to obtain various fault generation probabilities. A wire communication network is built; and the remote real-time monitoring and diagnosis is realized by using a share variable technology. The system can improve the big data processing capacity, and realizes the local and remote monitoring and fuzzy fault diagnosis of the fan operation states.
Description
Technical field
The present invention relates to a kind of mine fan on-line monitoring and fault diagnosis system.
Background technique
Mine fan is for delivered downhole fresh air, and harmful, toxic gas and dust is taken away from down-hole, ensures that underground work is carried out smoothly, ensures the personal safety of staff.According to statistics, the coal mining accident of more than 70% causes improper ventilation because ventilation equipment break down, and ventilating management to be not good at etc. that reason causes, and the generation of coal mining accident not only can bring economic loss to enterprise, time serious, the life safety of more entail dangers to staff, produces severe social influence.Mostly the maintenance model of current mining equiment is to adopt traditional periodic maintenance, namely the maintenance of machine was undertaken by the cycle, to the stipulated time, no matter machine has fault-free all will tear machine overhauling open to whole machine, and not to the stipulated time, there is defect little a bit in machine operation, sometimes also can continuous running.This maintenance mode not only can cause maintenance surplus, reduces the utilization ratio of equipment, affect economic benefit, and " in spite of illness " operation probably causes a serious accident.Periodic maintenance also can increase the total breakdown rate of equipment in addition, there are many case explanations, originally stable equipment, easily break down all the better through maintenance, this is because system itself is in stable state before maintenance, but through maintenance dismounting, when carrying out recombinant, installation precision has not reached original requirement.In addition, current diagnosis provides unique diagnosis according to certain diagnostic method usually, have ignored multiple failure occur possibility, cause diagnostic result single, think in absolute terms.
Summary of the invention
The object of the invention is to provide a kind of mine fan on-line monitoring and fault diagnosis system, with the Fault monitoring and diagnosis technology of advanced person, completes the improvement of traditional maintenance mode, realizes pressing State Maintenance; Fan vibration signal Real-time Collection is realized, equipment running status dynamic monitoring, equipment normal operation value, pre-alarm value and shut down the setting of value range, display and alarm by native system; Utilize analytic hierarchy process (AHP) to diagnose according to machine operation, draw fault rate, change traditional single diagnosis.
For solving the problem, the invention provides a kind of mine fan on-line monitoring and fault diagnosis system, comprising:
Be installed on vibration transducer on each monitoring point of blower fan to obtain fan vibration state simulation data;
Be connected with sensor in order to complete the pretreated signal condition equipment of sensing data;
The terminal board be connected with signal condition equipment and data collecting card, gather the analog data after conditioning, convert analog data to digital data through A/D;
Data collecting card inserts in the pci bus slot of industrial control computer, for completing the collecting work of multiple sensor signal under the control of the computer, as the industrial control computer of control core being provided with local monitoring and diagnosis software, remote monitoring and diagnosis software is installed on remote terminal simultaneously.Include acquisition control module, Real-Time Monitoring module, inline diagnosis module, information management module and Web Publishing module; realize fan vibration signal Real-time Collection; equipment running status dynamic monitoring; equipment normal operation value, pre-alarm value and shut down the setting of value range, display and alarm; diagnosed by analytic hierarchy process (AHP) according to machine operation; draw fault rate; set up wire communication network; utilize shared variable Techno-sharing data information, realize strange land Real-Time Monitoring and diagnosis.
Further, in said system, described sensor is vibrating speed sensors and Hall transducer, and vibrating speed sensors obtains fan vibration analog data, and Hall transducer obtains fan operation position data; Select fan motor output terminal, fan rotor front-end and back-end as vibration monitoring point, often arranges horizontal and vertical monitoring point respectively; Rotating shaft a certain position mounting magnetic steel sheet, as signal initial position, this position of hall sensing utensil 10mm installs.
Further, in said system, described signal condition equipment is vibration severity monitor, has signal amplification, filter function, can carry out the calculating of effective value linear detection, and calculated value is shown by hardware display screen and early warning and alarming sets, instruction.
Further, in said system, described data acquisition equipment is pci data capture card, can realize multi-channel high-speed synchronous acquisition.
Further, in said system, described software systems comprise acquisition control module, Real-Time Monitoring module, inline diagnosis module, information management module and Web Publishing module.
Further, in said system, described acquisition control module controls capture card and carries out data capture and conversion, and carries out pretreatment to data; Acquisition mode control, procedure parameter and channel setting, collecting device function setting can be realized by acquisition module, realize automatically gathering; Described Real-Time Monitoring module realizes monitoring to unit vibration measuring point, comprise real-time earthquake intensity value, earthquake intensity rank, early warning and alarming condition monitoring, achieve the instrument display of vibration severity, trend display and time-domain signal and show three kinds of display interfaces switchings, and manual switching can be carried out to watch-dog; Described field diagnostic module is respectively vibrated measuring point to equipment and is carried out real time data diagnosis and historical data diagnosis, diagnostic procedure is according to time-domain analysis, frequecny domain analysis, stability analysis and analysis of orbit process and corresponding 22 kinds of sign parameters thereof, utilize multi-objective layer fractional analysis, fuzzy comprehensive evaluation method, quantitative calculating fault right weight matrix, is combined the probability providing various fault and occur with sign parameter; Diagnostic result can Word textual form export; Described information management module comprises sampled data, user profile, diagnostic message, warning message, parameter threshold value and SIM system information management; Build linked database and preserve sampled data, user profile, diagnostic message, warning message; Constructing system configuration file preserves parameter threshold value and system information; The feature large according to sampled data output, devises a kind of data-storage method that temporally distance is successively decreased piecemeal, significantly reduces data volume.
Further, in said system, Web Publishing module, by Ethernet, utilizes shared variable technology, adopts C/S network structure, realizes strange land data transmission; Local side takes into account web server function, and remote port, by reception server data, realizes monitor and diagnosis; Remote port can carry out information management operations, and information operating result and operational order are sent to server simultaneously, and server carries out local data base amendment according to operational order.
In said system, described software section is realized by LabVIEW software programming.
Compared with prior art, this invention comprehensively adopts gauge pointer display, the display of real-time tendency curve, real time analogue signals display and parameter display, improves interactive capability; Achieve to blower fan by State Maintenance, improve fan safe, reduce corrective maintenance cost; Choose at failure mode analysis and characteristic parameter and reach 22, improve fault recognition rate; Analytic hierarchy process (AHP) and fuzzy synthesis algorithm are used for Trouble Diagnosis System of Fan.
Accompanying drawing explanation
Fig. 1 is the overall structure figure of the embodiment of the present invention;
Fig. 2 is the hardware system structure figure of the embodiment of the present invention;
Fig. 3 is the software system function figure of the embodiment of the present invention;
Fig. 4 is the data acquisition flow figure of the embodiment of the present invention;
Fig. 5 is the diagnostic flow chart of the embodiment of the present invention.
Embodiment
For making above-mentioned purpose of the present invention, feature more understandable, below by embodiment and accompanying drawing, the present invention is elaborated.
The invention provides a kind of mine fan on-line monitoring and fault diagnosis system, as shown in Figure 1, this system is made up of hardware system and software systems, and hardware system comprises sensor 2, signal condition equipment 3, conversion terminal 4, collecting device 5, monitoring and diagnosis software (computer) 6; Software systems comprise acquisition control module 7, Real-Time Monitoring module 8, inline diagnosis module 9, information management module 10, Web Publishing module 11.Native system is specifically constructed as follows:
For obtaining fan operation process, each monitors the analog data at position with the sensor 2 of blower fan 1 communication; Specifically, sensor can obtain the status information of measured part, and status information is converted to can be used for measuring, transmit, the analog amount of analysing and processing, be generally voltage or current value, kind of sensor is various, need select no measuring transducer according to different monitoring parameters.As shown in Figure 2, native system is according to Devices to test parameter and monitoring location, and optional vibration transducer 21 ~ 26, for measuring fan motor output terminal, fan rotor front-end and back-end oscillating signal, gathers the current value of each point; Optional Hall transducer 27, for obtaining blower fan sampled signal initial position, gathers on-off model.
With the signal condition equipment 3 of operative sensor communication in sensor 2 for nursing one's health the analog amount of collection, analogue signal to be amplified, filtering process; Particularly, as Fig. 2, optional vibration severity monitor as signal condition equipment 3, sensor 21,22 and vibration severity monitor 31 communication; Sensor 23,24 and vibration severity monitor 32 communication; Sensor 25,26 and vibration severity monitor 33 communication, monitor is powered by AC220V stabilized power supply, it has, and signal amplifies, filter function, can carry out the calculating of effective value linear detection, and calculated value is shown by hardware display screen and early warning and alarming sets, instruction.
Collecting device 5, through conversion terminal 4 and signal condition equipment 3 communication, gathers the analog data after conditioning, converts analog data to digital data through A/D; Concrete, the model of collecting device 5 and heterogeneous, device parameter need not, the signal accuracy gathered, signal type are different, according to actual needs, as Fig. 2, pci data capture card can be selected to gather analogue signal that vibration transducer 21 ~ 26 nurses one's health through conditioning device and the on-off model that Hall transducer 27 provides, through quantizing, encoding and the conversion of LSB true form, obtain the voltage signal being applicable to collecting device range.
With the monitoring and fault diagnosis system software (computer) 6 of collecting device 5 communication, include acquisition control module 7, Real-Time Monitoring module 8, inline diagnosis module 9, information management module 10 and Web Publishing module 11, realize fan vibration signal Real-time Collection, equipment running status dynamic monitoring, equipment normal operation value, the setting of pre-alarm value and shutdown value range, display and alarm, diagnosed by analytic hierarchy process (AHP) according to machine operation, draw fault rate, set up wire communication network, utilize shared variable Techno-sharing data information, realize strange land Real-Time Monitoring and diagnosis.
In detail, as Fig. 3, software system function is as follows:
Acquisition control module 7 controls capture card and carries out data capture and conversion, and carries out pretreatment to data; Acquisition mode control, procedure parameter and channel setting, collecting device function setting can be realized by acquisition control module 7, realize automatically gathering;
Shown in Fig. 4, be data acquisition flow:
Step 1: create capture card CreatDevice, and judgment device state, meet Rule of judgment (return of value≤-1) release capture card ReleaseDevice;
Step 2: obtain number of devices GetDeviceCount, and judgment device state, meet Rule of judgment (return of value≤0) release capture card;
Step 3, configuration device parameter, initialization capture card A/D equipment I nitDeviceProAD, and judgment device state, meet Rule of judgment (return of value=0) release capture card;
Step 4, performs gatherer process StartDeviceProAD;
Step 5, reads image data;
Step 6, by passage fractionation, reconstruct data;
Step 7, data transfer becomes magnitude of voltage;
Step 8, data processing;
Step 9, terminates once to gather, judges acquisition state, gathers if stop, then stop A/D equipment StopDeviceProAD, release A/D equipment, ReleaseDeviceProAD, and discharges capture card, otherwise continues to perform step 4;
Real-Time Monitoring module 8 pairs of unit vibration measuring points realize monitoring, comprise real-time earthquake intensity value, earthquake intensity rank, early warning and alarming condition monitoring, achieve the instrument display of vibration severity, trend display and time-domain signal and show three kinds of display interfaces switchings, and manual switching can be carried out to watch-dog.
Inline diagnosis module 9 pairs of equipment respectively vibrate measuring point and carry out real time data diagnosis and historical data diagnosis, diagnostic procedure is according to time-domain analysis, frequecny domain analysis, stability analysis and analysis of orbit process and corresponding 22 kinds of sign parameters thereof, utilize multi-objective layer fractional analysis, fuzzy comprehensive evaluation method, quantitative calculating fault right weight matrix, is combined the probability providing various fault and occur with sign parameter; Diagnostic result can Word textual form export.
Further, in said system, described time-domain analysis by real time data or historical data through time-domain calculation, obtain the time domain indexes such as earthquake intensity, nargin, flexure, kurtosis, determine time-domain analysis process sign parameter according to the change of time domain index; Frequecny domain analysis obtains data overall situation frequency spectrum with the data of time-domain analysis synchronization through FFT by extracting, and adopts EMD technology, data is separated from high to low by frequency, extracts data characteristics frequency, determine frequecny domain analysis process sign parameter; The vibration severity value within the scope of the fixed time is extracted in stability analysis from sampling database, shows with the form of tendency chart, carries out stability analysis by the fluctuation situation of tendency chart, determines stability analysis process sign parameter; Analysis of orbit extracts the burst data of a certain monitoring location, is carried out numerical integration respectively, is converted into vibration displacement value, obtains axle center locus figure, according to figure determination analysis of orbit process sign parameter after combination; The sign parameter of time-domain analysis, frequecny domain analysis, stability analysis, analysis of orbit four kinds of analytic processes is combined into the sign parameter of diagnostic procedure.
Further, in said system, described diagnostic method adopts multi-objective layer fractional analysis, fuzzy comprehensive evaluation method, carries out fault diagnosis, is analyzed by vibration signal characteristics, provide most possible several faults through probability calculation to Mine Ventilator.Fault signature is divided into two-layer i.e. sign layer and character pair layer, utilizes step analysis 1-9 scaling law to calculate the relative order weight vectors of each element for this level criterion.Adopt root method to try to achieve weight vectors, and be normalized.Finally carry out consistency check.
Root method asks weight vectors method:
Weight vectors method for normalizing:
Consistency check method:
Calculate each column element sum in multilevel iudge matrix, obtain vectorial S=(s
1, s
2..., s
n), wherein s
iit is each element sum of the i-th row; Calculate λ
maxvalue, with random index λ '
maxrelatively, λ is worked as
max< λ '
maxtime the weight vectors of trying to achieve be effective value, and meet consistency check.
λ
maxcomputational methods:
Information management module 10 comprises sampled data, user profile, diagnostic message, warning message, parameter threshold value and SIM system information management; Build linked database and preserve sampled data, user profile, diagnostic message, warning message; Constructing system configuration file preserves parameter threshold value and system information; The feature large according to sampled data output, devises a kind of data-storage method that temporally distance is successively decreased piecemeal, significantly reduces data volume.
Web Publishing module 11, by Ethernet, utilizes shared variable technology, adopts C/S network structure, realizes strange land data transmission; Local side takes into account web server function, and remote port, by reception server data, realizes monitor and diagnosis; Remote port can carry out information management operations, and information operating result and operational order are sent to server simultaneously, and server carries out local data base amendment according to operational order.
Professional workforce can recognize further, the each functions of modules described in conjunction with embodiment disclosed in this specification and related algorithm, step, realize by hardware, software or the two combination, in the above description general set forth each module composition and step, specific design condition and applied environment are depended in the realization of above-mentioned functions.Professional workforce realizes by different approach the function that each module realizes, but this realization should not thought and exceeds scope of the present invention.
Professional workforce can modify to each several part of the present invention, modification and do not depart from scope of the present invention, if amendment, modification meet within the claims in the present invention and equivalent technology scope, then the invention is intended to comprise these and changes and modification.
Claims (8)
1. mine fan on-line monitoring and a fault diagnosis system, is characterized in that, comprising:
Be installed on vibration transducer on each monitoring point of blower fan to obtain fan vibration state simulation data;
Be connected with sensor in order to complete the pretreated signal condition equipment of sensing data;
The terminal board be connected with signal condition equipment and data collecting card, gather the analog data after conditioning, convert analog data to digital data through A/D;
Data collecting card inserts in the pci bus slot of industrial control computer, for completing the collecting work of multiple sensor signal under the control of the computer, as the industrial control computer of control core being provided with local monitoring and diagnosis software, remote monitoring and diagnosis software is installed on remote terminal simultaneously.Include acquisition control module, Real-Time Monitoring module, inline diagnosis module, information management module and Web Publishing module; realize fan vibration signal Real-time Collection; equipment running status dynamic monitoring; equipment normal operation value, pre-alarm value and shut down the setting of value range, display and alarm; diagnosed by analytic hierarchy process (AHP) according to machine operation; draw fault rate; set up wire communication network; utilize shared variable Techno-sharing data information, realize strange land Real-Time Monitoring and diagnosis.
2. mine fan on-line monitoring according to claim 1 and fault diagnosis system, is characterized in that:
Described sensor is vibrating speed sensors and Hall transducer, and vibrating speed sensors obtains fan vibration analog data, and Hall transducer obtains fan vibration phase signal.
3. mine fan on-line monitoring according to claim 1 and fault diagnosis system, is characterized in that:
Described signal condition equipment is vibration severity monitor, has signal amplification, filter function, can carry out the calculating of effective value linear detection, and calculated value is set by the other instrument display of machine and early warning and alarming, indicated.
4. mine fan on-line monitoring according to claim 1 and fault diagnosis system, is characterized in that, described data acquisition equipment is pci data capture card, can realize multi-channel high-speed synchronous acquisition.
5. mine fan on-line monitoring according to claim 1 and fault diagnosis system, is characterized in that, described software systems comprise acquisition control module, Real-Time Monitoring module, inline diagnosis module, information management module and Web Publishing module.
6. according to claim 1 or the mine fan on-line monitoring based on oscillating signal according to claim 5 and fault diagnosis system, it is characterized in that, described acquisition control module controls capture card and carries out data capture and conversion; Described Real-Time Monitoring module realizes monitoring to unit vibration measuring point; Described field diagnostic module is respectively vibrated measuring point to equipment and is carried out real-time diagnosis and historical diagnostic; Described information management module comprises sampled data, user profile, diagnostic message, warning message, parameter threshold value and SIM system information management.
7. according to claims 1, Real-Time Monitoring module can realize pointer, trend, chart three kinds of monitoring states switchings; Failure mode analysis and characteristic parameter are chosen and are reached 22; Analytic hierarchy process (AHP) and fuzzy synthesis algorithm are used for Trouble Diagnosis System of Fan.
8. according to claim 1 or the mine fan on-line monitoring based on oscillating signal according to claim 5 and fault diagnosis system, it is characterized in that, described Web Publishing module, by Ethernet, utilizes shared variable technology, adopt C/S network structure, realize strange land data transmission; Local side takes into account web server function, and remote port, by reception server data, realizes monitor and diagnosis; Remote port can carry out information management operations, and information operating result and operational order are sent to server simultaneously, and server carries out local data base amendment according to operational order.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410353496.0A CN105003453A (en) | 2014-07-18 | 2014-07-18 | Online monitoring and fault diagnosis system of mine fan |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410353496.0A CN105003453A (en) | 2014-07-18 | 2014-07-18 | Online monitoring and fault diagnosis system of mine fan |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN105003453A true CN105003453A (en) | 2015-10-28 |
Family
ID=54376265
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410353496.0A Pending CN105003453A (en) | 2014-07-18 | 2014-07-18 | Online monitoring and fault diagnosis system of mine fan |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105003453A (en) |
Cited By (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105807756A (en) * | 2016-03-07 | 2016-07-27 | 大唐淮南洛河发电厂 | IoT-based distributed heat-engine plant fan condition monitoring and fault diagnosis system |
| CN105929738A (en) * | 2016-05-20 | 2016-09-07 | 北京汇路鑫科技有限公司 | Intelligent control device and intelligent control method |
| CN106769142A (en) * | 2016-12-23 | 2017-05-31 | 潘敏 | A kind of metallurgic fan machinery method for diagnosing faults |
| CN106950895A (en) * | 2017-05-17 | 2017-07-14 | 贵州大学 | A kind of manganese ore recovery process monitoring system and monitoring method based on Internet of Things |
| CN108154166A (en) * | 2017-11-30 | 2018-06-12 | 辽宁科技大学 | A kind of fan intelligent fault diagnosis system and method |
| CN109324599A (en) * | 2018-09-27 | 2019-02-12 | 蔚来汽车有限公司 | Determine method, apparatus, equipment and the medium of mechanical breakdown and mechanical breakdown compensation |
| CN110177017A (en) * | 2019-06-04 | 2019-08-27 | 沃德(天津)智能技术有限公司 | A kind of speed reducer intelligent Fault Diagnose Systems and its diagnostic method |
| CN111693726A (en) * | 2019-03-14 | 2020-09-22 | 辽宁工程技术大学 | Ventilation system fault diagnosis wind speed sensor arrangement method based on neighborhood rough set |
| CN112085930A (en) * | 2020-09-14 | 2020-12-15 | 武汉瑞莱保能源技术有限公司 | Intelligent monitoring and early warning system and method for generator set |
| CN112268703A (en) * | 2020-09-08 | 2021-01-26 | 安徽千禧精密轴承制造有限公司 | Mine ventilation machine bearing detecting system |
| CN112465164A (en) * | 2020-11-20 | 2021-03-09 | 中国人民解放军63921部队 | Integrated fault diagnosis and decision-making assisting system of space launching field service system |
| CN112539193A (en) * | 2020-12-07 | 2021-03-23 | 广州市耀安实业发展有限公司 | Fan vibration monitoring method and system, computer equipment and storage medium |
| CN113044723A (en) * | 2021-03-16 | 2021-06-29 | 永富建工集团有限公司 | State monitoring, analyzing and fault diagnosing integrated system for hoisting machinery |
| CN113158705A (en) * | 2020-01-07 | 2021-07-23 | 株洲中车时代电气股份有限公司 | Fan fault prediction and health management device and method |
| CN113217412A (en) * | 2021-06-22 | 2021-08-06 | 乐博工程(杭州)有限公司 | Data storage method, device and medium of centrifugal compressor on-line monitoring system |
| CN113253705A (en) * | 2021-05-18 | 2021-08-13 | 北京航空航天大学 | Fault diagnosis method and device for air blower |
| CN113432242A (en) * | 2021-06-29 | 2021-09-24 | 河南中烟工业有限责任公司 | Comprehensive detection method for running state of air conditioner fan |
| CN113916302A (en) * | 2021-10-28 | 2022-01-11 | 承德睿民科技有限公司 | Overhead traveling crane online detection and diagnosis system and method based on artificial intelligence technology |
| CN114893428A (en) * | 2022-04-02 | 2022-08-12 | 上海大学 | Fine pre-diagnosis and accurate operation and maintenance device and system for ventilator |
| CN115396461A (en) * | 2022-05-10 | 2022-11-25 | 湖南科技大学 | Trigger type vibration data acquisition system suitable for low-speed variable working condition |
| CN115898925A (en) * | 2022-10-27 | 2023-04-04 | 华能国际电力股份有限公司上海石洞口第二电厂 | Fan fault early warning method based on vibration signal multi-order moment |
| CN116044802A (en) * | 2023-03-08 | 2023-05-02 | 淮南矿业(集团)有限责任公司张集煤矿 | State fault monitoring and diagnosing system for mine ventilator |
| CN116335977A (en) * | 2023-03-31 | 2023-06-27 | 苏州瑞波机械有限公司 | Axial flow fan, control method and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20120065537A (en) * | 2010-12-13 | 2012-06-21 | 주식회사 뉴로스 | Device for preventing the failure of air bearing in turbo blower |
| CN202614367U (en) * | 2012-04-28 | 2012-12-19 | 长安大学 | Fault diagnosis apparatus for induced draught fan |
| CN202946424U (en) * | 2012-11-19 | 2013-05-22 | 桂林电子科技大学 | Mine draught fan mechanical fault diagnosis device based on embedded-type data collection system |
| CN103195728A (en) * | 2013-03-22 | 2013-07-10 | 济钢集团有限公司 | Large-scale fan on-line monitoring and diagnosing system |
| CN203130579U (en) * | 2013-03-28 | 2013-08-14 | 安徽工程大学 | Coal mine ventilator online monitoring and diagnosing device based on vibration signal detection |
| CN204113701U (en) * | 2014-07-18 | 2015-01-21 | 河北联合大学 | A kind of mine fan on-line monitoring and fault diagnosis system |
-
2014
- 2014-07-18 CN CN201410353496.0A patent/CN105003453A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20120065537A (en) * | 2010-12-13 | 2012-06-21 | 주식회사 뉴로스 | Device for preventing the failure of air bearing in turbo blower |
| CN202614367U (en) * | 2012-04-28 | 2012-12-19 | 长安大学 | Fault diagnosis apparatus for induced draught fan |
| CN202946424U (en) * | 2012-11-19 | 2013-05-22 | 桂林电子科技大学 | Mine draught fan mechanical fault diagnosis device based on embedded-type data collection system |
| CN103195728A (en) * | 2013-03-22 | 2013-07-10 | 济钢集团有限公司 | Large-scale fan on-line monitoring and diagnosing system |
| CN203130579U (en) * | 2013-03-28 | 2013-08-14 | 安徽工程大学 | Coal mine ventilator online monitoring and diagnosing device based on vibration signal detection |
| CN204113701U (en) * | 2014-07-18 | 2015-01-21 | 河北联合大学 | A kind of mine fan on-line monitoring and fault diagnosis system |
Non-Patent Citations (4)
| Title |
|---|
| 玄兆燕等: "矿井通风机在线监测系统总体设计", 《机械工程师》 * |
| 闵华松等: "高速旋转机械嵌入式状态监测与故障诊断系统研究", 《信息与控制》 * |
| 高兵兵等: "基于LabVIEW的矿用通风机故障监测诊断系统设计", 《矿山机械》 * |
| 魏大伟: "矿井风机实时监测与诊断系统的研发与应用", 《科技风》 * |
Cited By (32)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105807756A (en) * | 2016-03-07 | 2016-07-27 | 大唐淮南洛河发电厂 | IoT-based distributed heat-engine plant fan condition monitoring and fault diagnosis system |
| CN105929738A (en) * | 2016-05-20 | 2016-09-07 | 北京汇路鑫科技有限公司 | Intelligent control device and intelligent control method |
| CN106769142A (en) * | 2016-12-23 | 2017-05-31 | 潘敏 | A kind of metallurgic fan machinery method for diagnosing faults |
| CN106950895A (en) * | 2017-05-17 | 2017-07-14 | 贵州大学 | A kind of manganese ore recovery process monitoring system and monitoring method based on Internet of Things |
| CN108154166A (en) * | 2017-11-30 | 2018-06-12 | 辽宁科技大学 | A kind of fan intelligent fault diagnosis system and method |
| CN109324599A (en) * | 2018-09-27 | 2019-02-12 | 蔚来汽车有限公司 | Determine method, apparatus, equipment and the medium of mechanical breakdown and mechanical breakdown compensation |
| CN111693726A (en) * | 2019-03-14 | 2020-09-22 | 辽宁工程技术大学 | Ventilation system fault diagnosis wind speed sensor arrangement method based on neighborhood rough set |
| CN110177017A (en) * | 2019-06-04 | 2019-08-27 | 沃德(天津)智能技术有限公司 | A kind of speed reducer intelligent Fault Diagnose Systems and its diagnostic method |
| CN110177017B (en) * | 2019-06-04 | 2023-09-19 | 沃德(天津)智能技术有限公司 | Intelligent fault diagnosis system and diagnosis method for speed reducer |
| CN113158705A (en) * | 2020-01-07 | 2021-07-23 | 株洲中车时代电气股份有限公司 | Fan fault prediction and health management device and method |
| CN112268703A (en) * | 2020-09-08 | 2021-01-26 | 安徽千禧精密轴承制造有限公司 | Mine ventilation machine bearing detecting system |
| CN112085930A (en) * | 2020-09-14 | 2020-12-15 | 武汉瑞莱保能源技术有限公司 | Intelligent monitoring and early warning system and method for generator set |
| CN112465164A (en) * | 2020-11-20 | 2021-03-09 | 中国人民解放军63921部队 | Integrated fault diagnosis and decision-making assisting system of space launching field service system |
| CN112539193A (en) * | 2020-12-07 | 2021-03-23 | 广州市耀安实业发展有限公司 | Fan vibration monitoring method and system, computer equipment and storage medium |
| CN113044723A (en) * | 2021-03-16 | 2021-06-29 | 永富建工集团有限公司 | State monitoring, analyzing and fault diagnosing integrated system for hoisting machinery |
| CN113253705A (en) * | 2021-05-18 | 2021-08-13 | 北京航空航天大学 | Fault diagnosis method and device for air blower |
| CN113253705B (en) * | 2021-05-18 | 2022-05-03 | 北京航空航天大学 | Fault diagnosis method and device for air blower |
| CN113217412A (en) * | 2021-06-22 | 2021-08-06 | 乐博工程(杭州)有限公司 | Data storage method, device and medium of centrifugal compressor on-line monitoring system |
| CN113217412B (en) * | 2021-06-22 | 2022-06-24 | 乐博工程(杭州)有限公司 | Data storage method, device and medium of centrifugal compressor on-line monitoring system |
| CN113432242A (en) * | 2021-06-29 | 2021-09-24 | 河南中烟工业有限责任公司 | Comprehensive detection method for running state of air conditioner fan |
| CN113916302A (en) * | 2021-10-28 | 2022-01-11 | 承德睿民科技有限公司 | Overhead traveling crane online detection and diagnosis system and method based on artificial intelligence technology |
| CN113916302B (en) * | 2021-10-28 | 2024-06-11 | 承德睿民科技有限公司 | Crown block online detection and diagnosis system and method based on artificial intelligence technology |
| CN114893428A (en) * | 2022-04-02 | 2022-08-12 | 上海大学 | Fine pre-diagnosis and accurate operation and maintenance device and system for ventilator |
| CN114893428B (en) * | 2022-04-02 | 2023-02-21 | 上海大学 | Fine pre-diagnosis and accurate operation and maintenance device and system for ventilator |
| CN115396461A (en) * | 2022-05-10 | 2022-11-25 | 湖南科技大学 | Trigger type vibration data acquisition system suitable for low-speed variable working condition |
| CN115396461B (en) * | 2022-05-10 | 2024-04-30 | 湖南科技大学 | Trigger type vibration data acquisition system suitable for low-speed variable working conditions |
| CN115898925A (en) * | 2022-10-27 | 2023-04-04 | 华能国际电力股份有限公司上海石洞口第二电厂 | Fan fault early warning method based on vibration signal multi-order moment |
| CN115898925B (en) * | 2022-10-27 | 2024-06-04 | 华能国际电力股份有限公司上海石洞口第二电厂 | Fan fault early warning method based on vibration signal multi-order moment |
| CN116044802A (en) * | 2023-03-08 | 2023-05-02 | 淮南矿业(集团)有限责任公司张集煤矿 | State fault monitoring and diagnosing system for mine ventilator |
| CN116044802B (en) * | 2023-03-08 | 2024-02-27 | 淮南矿业(集团)有限责任公司张集煤矿 | State fault monitoring and diagnosing system for mine ventilator |
| CN116335977A (en) * | 2023-03-31 | 2023-06-27 | 苏州瑞波机械有限公司 | Axial flow fan, control method and storage medium |
| CN116335977B (en) * | 2023-03-31 | 2023-12-29 | 苏州瑞波机械有限公司 | Axial flow fan, control method and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105003453A (en) | Online monitoring and fault diagnosis system of mine fan | |
| CN204113701U (en) | A kind of mine fan on-line monitoring and fault diagnosis system | |
| CN107941537B (en) | A kind of mechanical equipment health state evaluation method | |
| US9996405B2 (en) | Embedded prognostics on PLC platforms for equipment condition monitoring, diagnosis and time-to-failure/service prediction | |
| CN102520697B (en) | Onsite information preprocessing method of remote cooperative diagnosis | |
| CN104614179B (en) | A kind of gearbox of wind turbine state monitoring method | |
| CN113110386A (en) | GIS/GIL equipment mechanical vibration state on-line monitoring and mechanical fault cloud diagnosis system | |
| CN103901882A (en) | Online monitoring fault diagnosis system and method of train power system | |
| CN111209934B (en) | Fan fault pre-alarm method and system | |
| CN105571638A (en) | Machinery device fault combination prediction system and method | |
| CN104390657A (en) | Generator set operating parameter measuring sensor fault diagnosis method and system | |
| CN109583386A (en) | A kind of intelligent rotating mechanical breakdown depth network characterization discrimination method | |
| CN107291991B (en) | Early defect early warning method for wind turbine generator based on dynamic network sign | |
| CN104737029A (en) | System and method for monitoring an electrically-connected system having a periodic behavior | |
| CN105205569A (en) | Draught fan gear box state on-line evaluation model building method and on-line evaluation method | |
| CN103115789A (en) | Second generation small-wave support vector machine assessment method for damage and remaining life of metal structure | |
| CN101799674A (en) | Method for analyzing service state of numerical control equipment | |
| CN103163389A (en) | Intelligent bus on-line monitoring and condition evaluation system | |
| CN114723285A (en) | Power grid equipment safety evaluation prediction method | |
| CN111125895B (en) | On-line assessment method for service power characteristics of complete wind driven generator | |
| CN111878322B (en) | Wind power generator device | |
| CN111260822A (en) | Rail transit vehicle health state analysis method and terminal based on big data | |
| CN102682180A (en) | Evaluation method for performance degradation of rotary mechanical equipment | |
| CN105572492A (en) | City rail train auxiliary inverter fault diagnosis device | |
| CN104425046A (en) | Method and system for quantizing operation security indexes of nuclear power station |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information |
Address after: 063009 Hebei province Tangshan City Xinhua West Road No. 46 Applicant after: North China Polytechnics Address before: 063009 Hebei province Tangshan City Xinhua West Road No. 46 Applicant before: Hebei United University |
|
| COR | Change of bibliographic data | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20151028 |