CN104386449A - Intelligent protection device for online detection of head/tail wheels of mining belt conveyor - Google Patents
Intelligent protection device for online detection of head/tail wheels of mining belt conveyor Download PDFInfo
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- CN104386449A CN104386449A CN201410587674.6A CN201410587674A CN104386449A CN 104386449 A CN104386449 A CN 104386449A CN 201410587674 A CN201410587674 A CN 201410587674A CN 104386449 A CN104386449 A CN 104386449A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G43/00—Control devices, e.g. for safety, warning or fault-correcting
- B65G43/02—Control devices, e.g. for safety, warning or fault-correcting detecting dangerous physical condition of load carriers, e.g. for interrupting the drive in the event of overheating
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G43/00—Control devices, e.g. for safety, warning or fault-correcting
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G2203/00—Indexing code relating to control or detection of the articles or the load carriers during conveying
- B65G2203/02—Control or detection
- B65G2203/0266—Control or detection relating to the load carrier(s)
- B65G2203/0275—Damage on the load carrier
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G2203/00—Indexing code relating to control or detection of the articles or the load carriers during conveying
- B65G2203/04—Detection means
- B65G2203/042—Sensors
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- Control Of Conveyors (AREA)
Abstract
The invention relates to an intelligent protection device for online detection of head/tail wheels of a mining belt conveyor. The intelligent protection device comprises a data collection module, an intelligent protection module, a data transmission module and an abnormity alarm system, wherein the data transmission module is respectively connected with the data collection module and the intelligent protection module; the intelligent protection device is characterized in that the data collection module is a multi-dimensional data collection module, the abnormity alarm system is in communication connection with an output end of the data transmission module through Ethernet or a Wifi signal, and the abnormity alarm system is formed by a remote service management module and a big data-based diagnostic analysis module which is used for collecting and modeling normal multi-dimensional data which are collected by the multi-dimensional data collection module by adopting an abnormity detection algorithm, classifying future data and transmitting an alarm signal to abnormity data. According to the intelligent protection device disclosed by the invention, remote monitoring, fault alarm and remote control on the mining belt conveyor are realized, comprehensive and all-weather diagnostic service is realized, in-time fault discovery and in-time shut down can be carried out through remote control, and the loss is reduced to be minimum.
Description
Technical field
The present invention relates to mining belt conveyer monitoring technology, particularly a kind of mining belt conveyer that is used for takes turns on-line checkingi intelligent protection device end to end.
Background technology
Mining belt conveyer continuative transport ability is strong, operating efficiency is high, be easy to realize automatic control, has been widely used in the transport of various bulk material.Load-transfer device is the important component part of ribbon conveyer, load-transfer device mainly contains plain sail core belt, syntheticfibres core belt, Steel cord belt etc., along with mining belt conveyer is towards high-speed, extensive, extra long distance, high spud angle future development, Steel cord belt is more and more widely used.Steel cable core conveying belt drastically increases pulling strengrth, but the ability of its longitudinal anti tear is not improved, and is only the intensity of rubber itself, thus easily causes longitudinal tear.Mining belt conveyer is the main artery of factories and miness production and transport, once have an accident, will bring great direct and consequential damage, especially the steel cable core conveying belt of high speed, long distance, high spud angle, and its loss is larger.According to statistics, a longitudinal tear possibility occurs a belt in its life cycle about has 20%, is worth the even more load-transfer device of millions of unit, once there is longitudinal tear accident, all may damages, cause huge economic loss within very short time.Namely allow to repair, also need suitable manpower and time, impact is greatly produced on normal production.The use amount of China Leather belt conveyor was increasing in recent years, and the increasing extent of its application is wide, and the accident that belt longitudinal tear occurs is also more and more frequent, and major cause great majority are all that the et out of order of taking turns end to end of mining belt conveyer causes.
At present, in order to monitor the running state of belt transporter, the on-the-spot PLC of many employings controls, shown by the read-out of on-the-spot PLC, and adopt CAN technology, change into RS232, RS485 signal, realize the data exchange with other system, but there are the following problems for it: first, the grab type of data is more single, part equipment mainly gathers vibrations or the noise figure of mining belt conveyer parts, the equipment of another part then lays particular emphasis on the ambient temperature of collecting device, based on the data acquisition of this kind of mode, the situation of multi-angle when effectively comprehensively can not show that mining belt conveyer works, thus it is not accurate enough to make the diagnostic system based on single-dimensional data judge, second, not yet have efficiently based on the mining belt conveyer fault localization system of multidimensional data at present, most system is all diagnosed the fault of mining belt conveyer by the judgement of threshold, and corresponding control is taked to it, this kind of judgment mode, when noise data is larger, false sample is higher, and fault usually can be caused to judge by accident.
Summary of the invention
The object of the invention is in order to provide solve existing mining belt conveyer remote monitor and control and the problem such as fault localization system cost is high, need that dedicated network, diagnostic accuracy are low, poor real and fault solution shortcoming take turns on-line checkingi intelligent protection device end to end for mining belt conveyer.
Technical scheme of the present invention is:
A kind of mining belt conveyer that is used for takes turns on-line checkingi intelligent protection device end to end; comprise data acquisition module, intelligent protection module, the data transmission module be connected respectively with data acquisition module and intelligent protection module and abnormal alarm system; it is characterized in that: described data acquisition module is multidimensional data acquisition module; described abnormal alarm system is established a communications link by the mouth of Ethernet or Wifi signal and described data transmission module
Described multidimensional data acquisition module is by being respectively used to measure three-dimensional vibrating frequency that mining belt conveyer takes turns end to end, peripheral temperature and the shock sensor of wheel speed, temperature sensor and Hall element end to end, and the internal processor to be electrically connected with each sensor signal mouth forms, the mouth of described internal processor is electrically connected with data transmission module
Described data transmission module is made up of cloud treater, Ethernet interface, built-in memory module and Wifi communication module, is provided with the microsd card realizing asynchronous transmission function in described built-in memory module,
Described abnormal alarm system by for telemanagement multidimensional data acquisition module and data transmission module, long-range multidimensional data acquisition module is set sampling frequency, sampled data type, on-off state, IP address, transmission network type and access code remote service management module, and adopt anomaly algorithm to gather modeling to the normal multidimensional data that multidimensional data acquisition module collects and the diagnositc analysis module based on large data of alerting signal of classifying to Future Data, send abnormal data forms
Described intelligent protection device is made up of the relay protection circuit receiving the output control circuit of the control signal based on abnormal alarm system stoppage protection that data link transmits, the power circuit be connected with output control circuit, Drive and Control Circuit and control mine leather belt driving motor.
Above-mentioned takes turns on-line checkingi intelligent protection device end to end for mining belt conveyer, and the anomaly algorithm of the described diagnositc analysis module based on large data is specially:
1), to the multidimensional original temporal signal that multidimensional data acquisition module collects feature extraction is carried out, feature extraction employing mel-frequency cepstrum coefficient algorithm (
mel-frequency cepstral coefficients, (
mFCCs)), the signal segment one group being included discrete data point utilizes frequecy characteristic to represent on a timeline;
2), by repeatedly obtaining multi-group data to the sampling repeatedly of the clock signal of mining belt conveyer normal condition, each group data adopt mel-frequency cepstrum coefficient algorithm to carry out feature extraction morphogenesis characters sample space respectively;
3), mel-frequency cepstrum coefficient algorithm is adopted to carry out feature extraction to data segment to be measured, contiguous classification method (K-Nearest Neighbours (KNN)) is adopted to carry out normal or abnormal classification to the data to be sorted after feature extraction, namely the distance of the characteristic data of data to be sorted and sample space is calculated, for the calculating of this distance, relative entropy/KL distance (Kullback – Leibler divergence) is used to calculate, when the value of KL distance exceedes preset value, data to be sorted can be marked as exception, otherwise for making normal labeled, be stored in the lump in the data bank of abnormal alarm system,
4), abnormal alarm system can scan new collection in its database system and the data point of key words sorting with fixed time interval, and find the running state of the mining belt conveyer in each time gap, the data for abnormal marking send alerting signal.
Above-mentioned takes turns on-line checkingi intelligent protection device end to end for mining belt conveyer, described mel-frequency cepstrum coefficient algorithm (
mel-frequency cepstral coefficients, (
mFCCs)) concrete steps as follows: first pending signal segment is cut into little signal segment on a timeline; Then each signal segment is done to the cycle estimation of spectral density; Re-use MFCC
sthe multiple filter provided, to process the spectral density of each signal segment and to carry out energy supposition to each filter be employed, carries out logarithm operation to the energy after each filter superposition, finally does inverse discrete cosine transform to each logarithm operation result.
Above-mentioned takes turns on-line checkingi intelligent protection device end to end for mining belt conveyer; mining belt conveyer is normally worked continuously the data in a week by within each hour, being divided into 24 × 7 samples as extracting the sample space with training; sampling frequency is per minute 6 data; 360 data points per hour, then carry out MFCC to 168 samples
sfeature extraction, forms new feature samples space.
Above-mentioned takes turns on-line checkingi intelligent protection device end to end for mining belt conveyer; described multidimensional data acquisition module is by arranging frequency sampling; and automatically by data transmission module by the data upload that gathers in abnormal alarm system, and automatically delete the data self retained.
Above-mentioned takes turns on-line checkingi intelligent protection device end to end for mining belt conveyer; the alerting signal of abnormal alarm system comprises alarm and stoppage protection; if stoppage protection; then by network, control signal is dealt into data link; intelligent protection device is transferred to through data link; and then export corresponding control signal, it is out of service to control mining belt conveyer.
The invention has the beneficial effects as follows:
1, the present invention is judged by threshold, but gathered by multidimensional data, based on the diagnositc analysis resume module of large data, when finding that the ambient temperature that mine leather belt is taken turns end to end is abnormal, directly can infer that wheel has exception end to end, if exception has also appearred in the vibration frequency of the linkage part of taking turns end to end, the ambient temperature of linkage part has raised simultaneously, describe the problem and appear at linkage part but not these directly measured parts, achieve about the breakdown judge between linkage part and passive components.
2, the present invention can pass through wireless network transmissions, not traditional CAN or ModBus, pass through data link, realize multidimensional data acquisition module and the wireless telecommunications between remote service management module and diagnostic module, avoid installation difficulty in the presence of a harsh environment, bring very large convenience to Installation and Debugging.
3, the mine leather belt based on large data takes turns abnormal alarm system end to end; bring very large convenience to user and operating personal, be out of order by diagnositc analysis simultaneously, and to fault and alarm and protection; avoid the damage of belt, prevent dangerous generation and the diffusion of accident.
Accompanying drawing explanation
Fig. 1 is functional block diagram of the present invention;
Fig. 2 is multidimensional data acquisition module block diagram in Fig. 1;
Fig. 3 is data transmission module block diagram in Fig. 1;
Fig. 4 is intelligent protection module block diagram in Fig. 1;
Fig. 5 is abnormal alarm system block diagram in Fig. 1.
Detailed description of the invention
As shown in Figure 1; this is used for mining belt conveyer and takes turns on-line checkingi intelligent protection device end to end; comprise data acquisition module, intelligent protection module, the data transmission module be connected respectively with data acquisition module and intelligent protection module and abnormal alarm system; described data acquisition module is multidimensional data acquisition module, and described abnormal alarm system is established a communications link by the mouth of Ethernet or Wifi signal and described data transmission module.
Wherein, as shown in Figure 2, the internal processor that described multidimensional data acquisition module is electrically connected by shock sensor, temperature sensor, Hall element and each sensor signal mouth forms, wherein, the both sides bearing shell three-dimensional vibrating frequency that shock sensor, temperature sensor real-time synchronization measurement ore deposit belt transporter is taken turns end to end and bearing bush temperature, utilize Hall element measuring head tail wheel rotating speed, the mouth of described internal processor is electrically connected with data transmission module.Multidimensional data acquisition module by arrange frequency to three-dimensional vibrating frequency, bearing bush temperature and end to end wheel speed sample, and automatically by data transmission module, by the data upload that gathers in abnormal alarm system, and automatically delete the data self retained.
By the use of the sensor, multiple situations of the mode of operation of mining belt conveyer can by Real-Time Monitoring.Utilize the multidimensional data that sensor obtains, this equipment can gather the running condition data being directed to particular mine belt member effectively simultaneously, comprise take turns end to end both sides bearing shell three-dimensional vibrating frequency, bearing bush temperature, end to end wheel speed etc.It is comprehensively inferred by measured multidimensional data and draw that key because of mine leather belt runs index, at data acquisition end, needs to do unified normalisation on a timeline to the data of each dimension.That is, collected multidimensional data can form man-to-man corresponding relation (but might not be based on identical sampling frequency) on a timeline.But not judged by threshold.Such as: as abnormal in found the ambient temperature of mine leather belt parts, directly can not infer that these parts have exception, if also there is exception in the vibration frequency of the linkage part of these parts, the ambient temperature of linkage part raises simultaneously, describes the problem and appears at linkage part but not these directly measured parts.
As shown in Figure 3, described data transmission module is made up of cloud treater, Ethernet interface, built-in memory module and Wifi communication module, is provided with the microsd card realizing asynchronous transmission function in described built-in memory module.Described data transmission module is by Ethernet interface or Wifi communication module, the multidimensional data gathered is transferred to abnormal alarm system, when data transmission goes wrong, can the data of collection be kept in built-in memory module, built-in memory module can be read by the form of microsd card, and asynchronous transmission is in abnormal alarm system.
The transmission principle of data transmission module: when data acquisition module initiates data sending request, data link can check current connected network condition, if find that Ethernet connects existence, preferential use Ethernet has connected the transmission of data, if under Ethernet connects unavailable situation, data transmission set can be attempted connecting with Wifi sending data, if Wifi connects also unavailable, data transmission module can notify corresponding multidimensional data acquisition module, makes it preserve corresponding data with local storage.
Described abnormal alarm system by for telemanagement multidimensional data acquisition module and data transmission module, long-range multidimensional data acquisition module is set sampling frequency, sampled data type, on-off state, IP address, transmission network type and access code remote service management module, and adopt anomaly algorithm to gather modeling to the normal multidimensional data that multidimensional data acquisition module collects and the diagnositc analysis module based on large data of alerting signal of classifying to Future Data, send abnormal data forms.The multidimensional data of all parts of the different mining belt conveyer of storage that abnormal alarm system is a large amount of, the data template under mining belt conveyer normal operating condition is set up according to a large amount of data, thus use the Future Data of this template to mine leather belt to compare, according to the difference of compare of analysis, draw the running state that mine leather belt is current.If mine leather belt running state occurs abnormal, related personnel is reported to the police.
The described analyzing and diagnosing module based on large data wherein, comprise anomaly algorithm, the data that data acquisition module gathers are the time series data (see figure 5) of multidimensional, through the collection modeling to normal multidimensional data, machine learning algorithm of the present invention, the data of actv. to a period of time can classify (normal, abnormal).Specific algorithm step is as follows:
1), to the multidimensional original temporal signal that multidimensional data acquisition module collects feature extraction is carried out, feature extraction employing mel-frequency cepstrum coefficient algorithm (
mel-frequency cepstral coefficients, (
mFCCs)), the signal segment one group being included discrete data point utilizes frequecy characteristic to represent on a timeline.The effect of this step is because clock signal is made up of discrete data point, these type of data effectively cannot be analyzed by computing machine, after MFCCs process, the signal segment (being such as made up of 2000 sampling points) of one group of original signal, can represented by little several frequecy characteristics (frequency period of such as signal, displacement etc.).
It is as follows that MFCCs is applied to concrete steps of the present invention: first original signal is cut on a timeline little signal segment (for the present invention, experiment proves, using 100 sampling points as the length of signal segment, feature extraction effect is better); Then each signal segment is done to the cycle estimation of spectral density; Then use multiple filter that MFCCs algorithm provides to process the spectral density of each signal segment and to carry out energy supposition to each filter be employed, and logarithm operation is carried out to the energy after each filter superposition, inverse discrete cosine transform is done to each logarithm operation result simultaneously.
2), by repeatedly obtaining multi-group data to the sampling repeatedly of the clock signal of mining belt conveyer normal condition, each group data adopt mel-frequency cepstrum coefficient algorithm to carry out feature extraction morphogenesis characters sample space respectively.In the present embodiment, the data (sampling frequency is per minute 6 data) in the week that normally worked continuously by mining belt conveyer, are divided into 168 samples (24 × 7) as extracting the sample space with training by each hour (360 data point).Then MFCC feature extraction is carried out to these 168 samples, form new feature samples space (168 samples).The effect of this step is, gathers a large amount of data samples, for step 3 provides learned sample space.
3), mel-frequency cepstrum coefficient algorithm is adopted to carry out feature extraction to data segment to be measured, contiguous classification method (K-Nearest Neighbours (KNN)) is adopted to carry out normal or abnormal classification to the data to be sorted after feature extraction, namely the distance of the characteristic data of data to be sorted and sample space is calculated, for the calculating of this distance, relative entropy/KL distance (Kullback – Leibler divergence) is used to calculate, when the value of KL distance exceedes preset value, data to be sorted can be marked as exception, otherwise for making normal labeled, be stored in the lump in the data bank of abnormal alarm system.The effect of this step is, step 1), 2) provide the feature of data under belt normal operating condition, then cluster analysis can be carried out to the feature of each small-signal section, under finding belt normal operation state, the general character of the feature extracted by MFCC feature, by feature clustering the strongest for general character, form Clustering Model, thus instruct with this model in actual production afterwards, the state that notes abnormalities (because the data characteristics of the data characteristics under error state and normal condition can not be divided into a cluster when cluster).
4), abnormal alarm system can with fixed time interval (in the present embodiment, this unit is per hour), carry out the new collection in scan database system and the data point of key words sorting, find the running state of the mining belt conveyer in certain hour, the data for abnormal marking send alerting signal.The effect of this step is, the feature extraction of the data point of collection per hour out (is passed through MFCCs), then the feature of these data points and existing Clustering Model is compared, in order to find that whether new data point feature is normal or abnormal.See Fig. 5, be the three-dimensional vibrating figure of equipment, abscissa is the time, and ordinate is the amplitude of vibration, and be have vibration alarming at 10:55 to 10:56 in morning place as we can see from the figure, its chain lines represents X-axis, and solid line represents Y-axis, represented by dotted arrows Z axis.
As shown in Figure 4, described intelligent protection device is made up of the relay protection circuit receiving the output control circuit of the control signal based on abnormal alarm system stoppage protection that data link transmits, the power circuit be connected with output control circuit, Drive and Control Circuit and control mine leather belt driving motor.System is based on the alerting signal of the mine leather belt abnormal alarm system of large data; judge it is alarm; or stoppage protection; if stoppage protection; then by network, control signal is dealt into data link; be transferred to intelligent protection device through data link, and then export corresponding control signal, control mining belt conveyer.
In sum, multidimensional data acquisition module in device gathers the status information data of mining belt conveyer, and pass through data transmission module, utilize network by data remote transmission in abnormal alarm system, gathered data are processed, obtain result and by result presentation to related management personnel, realize the remote monitor and control to mine leather belt and fault alarm.Checking system of the present invention, can realize any Wifi signal or the trouble diagnosing of mining belt conveyer and the monitoring that are covered region by Ethernet, realize the seamless link of the wireless of mining belt conveyer fault localization system and cable network, the mining belt conveyer being equipped with this fault localization system is carried out to the diagnosis service of all-dimensional all-weather.
Claims (6)
1. take turns on-line checkingi intelligent protection device end to end for mining belt conveyer for one kind; comprise data acquisition module, intelligent protection module, the data transmission module be connected respectively with data acquisition module and intelligent protection module and abnormal alarm system; it is characterized in that: described data acquisition module is multidimensional data acquisition module; described abnormal alarm system is established a communications link by the mouth of Ethernet or Wifi signal and described data transmission module
Described multidimensional data acquisition module is by being respectively used to measure three-dimensional vibrating frequency that mining belt conveyer takes turns end to end, peripheral temperature and the shock sensor of wheel speed, temperature sensor and Hall element end to end, and the internal processor to be electrically connected with each sensor signal mouth forms, the mouth of described internal processor is electrically connected with data transmission module
Described data transmission module is made up of cloud treater, Ethernet interface, built-in memory module and Wifi communication module, is provided with the microsd card realizing asynchronous transmission function in described built-in memory module,
Described abnormal alarm system by for telemanagement multidimensional data acquisition module and data transmission module, long-range multidimensional data acquisition module is set sampling frequency, sampled data type, on-off state, IP address, transmission network type and access code remote service management module, and adopt anomaly algorithm to gather modeling to the normal multidimensional data that multidimensional data acquisition module collects and the diagnositc analysis module based on large data of alerting signal of classifying to Future Data, send abnormal data forms
Described intelligent protection device is made up of the relay protection circuit receiving the output control circuit of the control signal based on abnormal alarm system stoppage protection that data link transmits, the power circuit be connected with output control circuit, Drive and Control Circuit and control mine leather belt driving motor.
2. according to claim 1ly take turns on-line checkingi intelligent protection device end to end for mining belt conveyer, it is characterized in that, the anomaly algorithm of the described diagnositc analysis module based on large data is specially:
1), to the multidimensional original temporal signal that multidimensional data acquisition module collects feature extraction is carried out, feature extraction employing mel-frequency cepstrum coefficient algorithm (
mel-frequency cepstral coefficients, (
mFCCs)), the signal segment one group being included discrete data point utilizes frequecy characteristic to represent on a timeline;
2), by repeatedly obtaining multi-group data to the sampling repeatedly of the clock signal of mining belt conveyer normal condition, each group data adopt mel-frequency cepstrum coefficient algorithm to carry out feature extraction morphogenesis characters sample space respectively;
3), mel-frequency cepstrum coefficient algorithm is adopted to carry out feature extraction to data segment to be measured, contiguous classification method (K-Nearest Neighbours (KNN)) is adopted to carry out normal or abnormal classification to the data to be sorted after feature extraction, namely the distance of the characteristic data of data to be sorted and sample space is calculated, for the calculating of this distance, relative entropy/KL distance (Kullback – Leibler divergence) is used to calculate, when the value of KL distance exceedes preset value, data to be sorted can be marked as exception, otherwise for making normal labeled, be stored in the lump in the data bank of abnormal alarm system,
4), abnormal alarm system can scan new collection in its database system and the data point of key words sorting with fixed time interval, and find the running state of the mining belt conveyer in each time gap, the data for abnormal marking send alerting signal.
3. according to claim 2ly take turns on-line checkingi intelligent protection device end to end for mining belt conveyer, it is characterized in that, the concrete steps of described mel-frequency cepstrum coefficient algorithm (Mel-frequency cepstral coefficientts, (MFCCs)) are as follows: first pending signal segment is cut into little signal segment on a timeline; Then each signal segment is done to the cycle estimation of spectral density; Re-use multiple filter that MFCCs provides to process the spectral density of each signal segment and to carry out energy supposition to each filter be employed, logarithm operation is carried out to the energy after each filter superposition, finally inverse discrete cosine transform is done to each logarithm operation result.
4. according to claim 2ly take turns on-line checkingi intelligent protection device end to end for mining belt conveyer; it is characterized in that: the data in the week that normally worked continuously by mining belt conveyer were divided into 24 × 7 samples as extracting the sample space with training by each hour; sampling frequency is per minute 6 data; 360 data points per hour, then carry out MFCC to 168 samples
sfeature extraction, forms new feature samples space.
5. according to claim 1ly take turns on-line checkingi intelligent protection device end to end for mining belt conveyer; it is characterized in that: described multidimensional data acquisition module is by arranging frequency sampling; and automatically by data transmission module by the data upload that gathers in abnormal alarm system, and automatically delete the data self retained.
6. according to claim 1ly take turns on-line checkingi intelligent protection device end to end for mining belt conveyer; it is characterized in that: the alerting signal of abnormal alarm system comprises alarm and stoppage protection; if stoppage protection; then by network, control signal is dealt into data link; intelligent protection device is transferred to through data link; and then export corresponding control signal, it is out of service to control mining belt conveyer.
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| WO2017016666A1 (en) * | 2015-07-29 | 2017-02-02 | Schenck Process S.R.O. | Tube conveyor with a device for protecting the conveyor belt against volumetric overloading and releasing stand for said tube conveyor |
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| CN107199719A (en) * | 2017-07-19 | 2017-09-26 | 辽宁工程技术大学 | A kind of online prosthetic device worn and torn for steel cable core conveying belt and method |
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| CN109720814A (en) * | 2019-01-08 | 2019-05-07 | 四川省自贡运输机械集团股份有限公司 | A kind of belt conveyor remote failure monitoring and fault diagnosis system |
| CN110118657A (en) * | 2019-06-21 | 2019-08-13 | 杭州安脉盛智能技术有限公司 | Based on relative entropy and K nearest neighbor algorithm Fault Diagnosis of Roller Bearings and system |
| CN110540005A (en) * | 2019-10-11 | 2019-12-06 | 宁夏广天夏电子科技有限公司 | Belt feeder foreign matter detecting system based on three-dimensional image and intelligent video technique |
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0964325A1 (en) * | 1998-06-01 | 1999-12-15 | Neles Controls Oy | Field device management system |
| CN201222298Y (en) * | 2008-06-23 | 2009-04-15 | 山西科泰微技术有限公司 | Fire-fighting alarm control cabinet with data acquisition protecting equipment |
| US20130262033A1 (en) * | 2012-04-02 | 2013-10-03 | Greg Henson | Remote monitoring and management of sunless tanning devices |
| CN103699966A (en) * | 2013-04-12 | 2014-04-02 | 国家电网公司 | Multidimensional overall-process data control system and control method based on user requirement |
| CN103744358A (en) * | 2013-10-28 | 2014-04-23 | 武汉瑞芯科微电子技术有限公司 | System and method for monitoring subway tunnel construction management based on Internet of Things |
| EP2749975A1 (en) * | 2012-12-31 | 2014-07-02 | Efekt Technologies Sp. z o .o. | Method for collection, selection and conversion of measurement data enabling diagnosis of electricity devices, especially in industrial plants and a concentrator for applying this method |
-
2014
- 2014-10-29 CN CN201410587674.6A patent/CN104386449B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0964325A1 (en) * | 1998-06-01 | 1999-12-15 | Neles Controls Oy | Field device management system |
| CN201222298Y (en) * | 2008-06-23 | 2009-04-15 | 山西科泰微技术有限公司 | Fire-fighting alarm control cabinet with data acquisition protecting equipment |
| US20130262033A1 (en) * | 2012-04-02 | 2013-10-03 | Greg Henson | Remote monitoring and management of sunless tanning devices |
| EP2749975A1 (en) * | 2012-12-31 | 2014-07-02 | Efekt Technologies Sp. z o .o. | Method for collection, selection and conversion of measurement data enabling diagnosis of electricity devices, especially in industrial plants and a concentrator for applying this method |
| CN103699966A (en) * | 2013-04-12 | 2014-04-02 | 国家电网公司 | Multidimensional overall-process data control system and control method based on user requirement |
| CN103744358A (en) * | 2013-10-28 | 2014-04-23 | 武汉瑞芯科微电子技术有限公司 | System and method for monitoring subway tunnel construction management based on Internet of Things |
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