CN106698197A - System for online diagnosis and preventive maintenance of container crane based on big data - Google Patents
System for online diagnosis and preventive maintenance of container crane based on big data Download PDFInfo
- Publication number
- CN106698197A CN106698197A CN201611088589.0A CN201611088589A CN106698197A CN 106698197 A CN106698197 A CN 106698197A CN 201611088589 A CN201611088589 A CN 201611088589A CN 106698197 A CN106698197 A CN 106698197A
- Authority
- CN
- China
- Prior art keywords
- crane
- data
- layer
- model
- analysis
- 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.)
- Granted
Links
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
- B66C13/00—Other constructional features or details
- B66C13/16—Applications of indicating, registering, or weighing devices
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
- B66C15/00—Safety gear
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
- B66C15/00—Safety gear
- B66C15/06—Arrangements or use of warning devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/04—Ageing analysis or optimisation against ageing
Landscapes
- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention discloses a system for online diagnosis and preventive maintenance of a container crane based on big data. The system comprises a crane equipment layer, a sensor layer, a network layer and a data service layer, wherein the data service layer is further divided into a data analysis and model layer, a data application layer and a business system layer; the crane equipment layer receives operating parameters of the crane; the sensor layer receives sensor signals of key parts of the crane; the network layer transmits the operating parameters and the sensor signals to the data service layer; the data analysis and model layer presets a normal operating model and a fault model of the crane, and compares the operating parameters and the sensor signals with the model, so as to judge the operating state of the crane; the data application layer predicts the operating stable of the crane within a certain period of time in the future according to the operating parameters and the sensor signals, so as to generate a fault early warning; and the business system layer is provided with an external data interface, and outputs data of the data analysis and model layer as well as the data application layer to the outside world.
Description
Technical field
The present invention relates to crane maintenance system, more specifically to a kind of container crane based on big data
Inline diagnosis and preventative maintenance system.
Background technology
Container crane is the groundwork equipment of container terminal, and its kilter and efficient operation determine harbour
Production efficiency and economic well-being of workers and staff.Such crane is the structure Pang for integrating mechanical, electrical, liquid and Computer Control Technology simultaneously
Greatly, the handling and loading equipment of technical sophistication, single system procurement cost is very high, maintenance cost be also the daily operation of enterprise it is main into
This.Therefore for the O＆M department of container terminal, the operational maintenance and maintenance work of crane are carried out, ensures crane
Can run at any time and reduces cost is basic job requirement as far as possible.
The repair and maintenance work of usual crane is made up of two parts：One is patrolling and examining and trial run and run before driver starts shooting.Patrol and examine
Include foreign inspection, critical piece visual inspection etc.；Then driver drives at a slow speed each principal organ before formal operation, leads to
The means such as vision, the sense of hearing and tactile observation crane movements situation is crossed, judges whether crane can normally run.Two is harbour
Repair and maintenance department daily point inspection and the regularly work such as examination and maintenance.These repair and maintenance work be concentrated mainly on steel wire rope inspection,
On the rapid wear device such as decelerator, brake.
But above-mentioned conventional repair and maintenance work, is usually present problems with：Do not inspected periodically by maintenance manual；Missing inspection one
A little key positions；Inspection personnel's specialty degree is not high；Human factor is it is difficult to ensure that the accuracy for detecting every time；In structure and mechanism
Portion's cracking cannot effective detection.The problem for ultimately causing is that crane occurs sudden significant trouble, generally includes steel construction
Cracking, decelerator and the great part equivalent damage of electric machinery.Due to lacking expected preparation, this kind of failure runs band can typically to harbour
Carry out significant impact, because spare part arrival is time-consuming or maintenance is complicated, crane as a result can be caused to shut down for a long time；More seriously exist
Under extreme case, for example steel construction cracking causes crane to collapse, and brings great personal injury and related compounds damage.
In addition as automated container terminal rises, traditional dependence driver operates crane in hoisting box
Operating type, it is personnel by remote control room to be altered in steps, and by means of image and control technology, carries out semi-automatic auxiliary
Operation under pattern.Due to separate men from machines, originally had that driver undertakes according to operation when direct perception (sense of hearing, tactile, vision
Deng) find that the task of crane failure cannot be implemented.Thus it is also required to this long-range behaviour of repair and maintenance technical guarantee of correlation
The crane scientific discovery of work simultaneously eliminates potential faults.
The Reliable Products of above mentioned problem are there is no in the market, therefore researches and develops a kind of crane online diagnosing technique of support shaft, realize
Preventive maintenance, is one of emphasis demand of crane facility area of maintenance to prevent trouble before it happens.
The content of the invention
It is an object of the invention to provide a kind of container crane inline diagnosis based on big data and preventive maintenance system
System, solves the problems, such as that crane cannot inline diagnosis, maintenance in the prior art.
To achieve the above object, the present invention is adopted the following technical scheme that：
A kind of container crane inline diagnosis and preventative maintenance system based on big data, including：Crane facility
Layer, sensor layer, Internet, data service layer, wherein data service layer are further divided into data analysis and model layer, data should
With layer, business system layer.The operational factor of crane facility layer reception crane；Sensor layer receives crane key position
Sensor signal；Internet transmits to data service layer operational factor and sensor signal；Data analysis and model layer are default
The normal moving model of crane and fault model, operational factor and sensor signal are compared with model, so as to judge lifting
The running status of machine；Fortune of the data application layer according to operational factor and sensor signal prediction crane within following a period of time
Row state, and fault pre-alarming is generated with this；Business system layer setting external data interface, and by data analysis and model layer, number
Exported to outside according to the data of application layer.
Further, when the running status of crane is judged as failure in data analysis and model layer, data analysis
The parameter in normal moving model is transmitted to crane with model layer.
Further, the operational factor threshold value and key position threshold value of the default crane of data application layer.Data application layer
According to operational factor and sensor signal in a period of time, operational factor and sensor signal in prediction lower a period of time,
And the operational factor and sensor signal of prediction compare with operational factor threshold value and key position threshold value, so as to generate failure
Early warning.
Further, the operational factor of crane includes motor, crane structure, brake, dolly, decelerator, speed change
The operational factor of case.
Further, the sensor of key position includes grating strain transducer, displacement transducer, vibrating sensor, temperature
Degree sensor, pick up sensor, monitoring camera.
Further, data analysis and model layer carry out load analysis of spectrum, stress according to operational factor and sensor signal
Analysis of spectrum, fault model, life prediction, preventative maintenance analysis, audio analysis.
In the above-mentioned technical solutions, the present invention realizes the unconventional maintenance part such as crane steel structure and mechanism first
Science is detected, it is to avoid machine that is related unpredictable and happening suddenly damages failure；Second, the waste of spare part is avoided that, can be according to prevention
Property maintenance measures determine the purchase volume of spare part, save its repair and maintenance cost；Third, by preventive maintenance service, knowing in advance
Potential faults, can greatly reduce the downtime of equipment, reduce shutdown loss cost；It is real finally for goods producer
The whole process supervision that product uses process is showed.
Brief description of the drawings
Fig. 1 is the structural representation of present system；
Fig. 2 is the information content that operational factor and sensor signal are included.
Reference：
3：Crane facility layer；4：Sensor layer；5：Internet；6：Data service layer；7：Data analysis and model layer；8：
Data application layer；9：Business system layer；10：Motor；11：Crane structure；12：Brake；13：Dolly；14：Decelerator；15：
Gearbox；16：Grating strain transducer；17：Displacement transducer；18：Vibrating sensor；19：Temperature sensor；20：Pick up is passed
Sensor；21：Monitoring camera；22：Camera network；23：Signal condition collector；24：Intelligent gateway；25：3G/4G networks；
26：Industrial bus；27：Server；28：Data warehouse；29：Data modeling；30：Data Webization；31：Data visualization；
32：The general analysis of load；33：Stress analysis of spectrum；34：Fault model；35：Life prediction；36：Preventative maintenance is analyzed；47：Audio
Analysis；37：Failure mode identification；38：Fault pre-alarming；39：Health evaluating；40：Task scheduling；41：Presence is monitored；42：
Crane remote monitoring system；43：Crane fault diagnosis and Predict analysis system；44：Mechanism of crane monitoring and evaluation system
System；45：Steel construction health monitoring and evaluation system；46：Crackle remotely monitor and automatic recognition system；50：Data acquisition；51：
Crane real-time running data；52：Fault data；53：Crane essential information parameter；54：Video monitoring data；55：Lifting
Machine basic operating conditions；56：Crane operating status parameter；57：Crane does case statistics；58：Operator's operation statistics；59：
Machine halt trouble parameter；60：Real time fail is monitored；61：Driver operation is monitored；62：Working site monitors；63：Key equipment runs
Monitoring.
Specific embodiment
Technical scheme is further illustrated with reference to the accompanying drawings and examples.
The present invention is for the major hidden danger such as container crane steel construction and principal organ's fatigue, cracking, it is impossible to by passing
The present situation that system repair and maintenance means find and processed in time, research and development are a kind of to be based on Internet of Things, cloud platform and big data analytical technology
Crane inline diagnosis and preventative maintenance system.
As shown in figure 1, the general frame of present system can be divided into 2 big layers, respectively：Product interconnection is integrated with big number
According to acquisition layer and big data analysis optimization and intelligent Service layer.Wherein, in ground floor, substratum can be divided into again：The lifting of harbour
Machine equipment layer 3, sensor layer 4, Internet 5 and data service layer 6；Data service layer 6 can be divided into a substratum again：Data analysis
With model layer 7, data application layer 8 and business system layer 9.
Wherein, crane facility layer 3 receives the operational factor of crane, and sensor layer 4 receives crane key position
Sensor signal, Internet 5 transmits to data service layer 6 operational factor and sensor signal；Data analysis and model layer 7 are pre-
If the normal moving model of crane and fault model 34, operational factor and sensor signal are compared with model, so as to judge
The running status of crane, data application layer 8 is according to operational factor and sensor signal prediction crane in following a period of time
Interior running status, and fault pre-alarming 38 is generated with this；Business system layer 9 sets external data interface, and by data analysis and
Model layer 7, the data of data application layer 8 are exported to outside.
Monitoring that the present invention passes through and measurement pair as if respectively in numerous cranes of different regions distribution.Risen in harbour
Heavy-duty machine end includes crucial monitoring part：The structure of the motor 10, crane of crane, brake 12, dolly 13, decelerator 14,
Gearbox 15；
The present invention is by crane key position mount stress meter, camera, sound pick-up, displacement transducer 17, vibration
The various kinds of sensors such as sensor 18, video camera, grating strain transducer 16 and temperature measurer, continuous Real-time Collection crane key portion
The data of position, the data of main collection are as shown in Figure 2.
The data that the present invention gathers sensor, by camera network 22, signal condition collector 23, intelligent gateway
24th, the local bus such as 3G/4G networks 25, industrial bus 26 is aggregated on the server 27 of harbour.
The data of the single harbour being aggregated on harbour server 27 are passed through international and private network by the present invention by network
Network, is passed in cloud platform data bins.Carry out the classification of data.By Data fusion technique and data analysis algorithm etc., realize
The management such as the data modeling 29 of big data, data Webization 30 and data visualization 31.
The present invention realizes measuring right to crane on the basis of data cloud platform collects and the treatment such as sorts out with preliminary management
The more professional data analysis of elephant, such as：It is load analysis of spectrum 32, stress analysis of spectrum 33, fault model 34, life prediction 35, pre-
Anti- property maintenance analysis 36, failure mode identification 37, fault pre-alarming 38, health evaluating 39, the monitoring of task scheduling 40, presence
41st, audio analysis 47 etc..
When the running status of crane is judged as failure in data analysis and model layer 7, data analysis and model layer 7
Parameter in normal moving model is transmitted to crane.Additionally, data application layer 8 is also preset with the operational factor threshold of crane
Value and key position threshold value.Data application layer 8 according to the operational factor and sensor signal in a period of time, during next section of prediction
Interior operational factor and sensor signal, and the operational factor and sensor signal and operational factor threshold value and key that will be predicted
Position threshold value compares, so as to generate fault pre-alarming 38.
The present invention realizes its commercial value and significance in the foundation and the analysis of big data of big data model.Can be with
Following service and set up following service system for harbour user provides：Crane remote monitoring system 42, crane fault diagnosis
With Predict analysis system 43, Mechanism of crane monitoring and evaluation system 44, steel construction health monitoring and evaluation system 45 and crackle
Remotely monitor and automatic recognition system 46.
Said system structure can be largely classified into following 3 partial function block：Crane front end data acquisition 50, remote monitoring
The hardware device construction and crane inline diagnosis at center and preventive maintenance system related software.
50 points of crane front end data acquisition is structure monitoring, mechanism's monitoring and crack monitoring, and it mainly includes following
Data：Crane real-time running data 51；Fault data 52；Crane essential information parameter 53；Video monitoring data 54；Lifting
Machine basic operating conditions 55；Crane operating status parameter 56；Crane does case statistics 57；Operator's operation statistics 58；Shut down
Fault parameter 59；Real time fail monitoring 60；Driver operation monitoring 61；Working site monitoring 62；Key equipment operation monitoring 63.
Structure monitoring by experiment crane on structural key point monitoring modular system energy integrated high-sensitivity stress,
Strain testing instrument and dynamic signal testing instrument, realize to physical characteristic data realities such as key position stress, strain and structural vibrations
When gather；
Mechanism monitors the vibration and temperature for gathering motor 10 and reduction box, by the threshold supervision to vibration and temperature
Realize the assessment to jack machinism.Main various sensors, the change by equipment and structure of the hardware system of on-line monitoring system
Device, signal condition collector 23, rack and various communication cables is sent to constitute.
Structural crack remotely monitor mainly uses image recognition technology with automatic recognition system 46, and steel construction is carried out automatically
Change identification.Monitor that key point installs camera additional by experiment crane crackle, the equipment such as NVR are installed additional in electrical house, to lifting
Machine crackle key point carries out real-time online monitoring.The system can analyze steel construction crackle index, and real-time Transmission analysis result is substituted
Workman patrols and examines, and can remote inquiry to steel construction real-time architecture, improve workman's operating efficiency.
Crane front end data is all transferred to industrial computer on machine, and is carried out with the original hoist crane management system of the machine (RCMS)
Data summarization is as shown in Figure 2.The industry control machine information of all cranes continues to be transferred to harbour home server 27, is converged for data
Total and data are uploaded to high in the clouds.
Crane remote Surveillance center is proposed to be located at distal end general headquarters, mainly realizes to global crane facility running status number
According to real-time collecting with storage, be to reach to enter crane facility by analyzing the historical data of crane facility running status
The important hardware foundation of row preventive maintenance.The main hardware equipment of Surveillance center includes large-screen display, server 27, net
Network interchanger, monitor workstation and ups power etc..The server 27 at corporation monitoring center be used for local software service operation,
Database (at least preserving project information, access user information), knowledge base etc.；The multiple work stations of deployment, support for each work post
The work station of personnel；A set of video conference device is equipped with, video conference is carried out for live project team and user.
Main software systems have crane facility remote monitoring center integrated management software systems, crane steel structure to be good for
Health monitoring and evaluation system 45, Mechanism of crane monitoring and evaluation system 44, crane steel structure crackle remotely monitor with it is automatic
Identifying system 46, the crane accident analysis based on machine learning and big data and Prediction System and based on distributed structure/architecture
Real time historical database and industrial monitoring and control software.
Crane facility remote monitoring center integrated management software systems are be deployed in monitoring central server 27
Set crane facility long distance control system software.Main function includes that crane operating data collection 50, Information Security is protected
Barrier, crane remote in-service monitoring, remote online preventive maintenance analysis and remote service management platform etc..
A) crane data acquisition 50
Wharf crane data acquisition 50 gathers 50 equipment and harbour Data Collection transmission equipment by crane exclusive data
Complete, to realize the data acquisition 50 on crane facility, pretreatment, storage and transmit.Real time data, the system by pre-processing
Count and history buffer data can be sent to deployment system beyond the clouds according to IOT forms.Concrete function is as follows：
1) crane exclusive data gathers the collection requirement that 50 equipment disclosure satisfy that the local various data of crane, including from
The data that serial ports, fieldbus and Ethernet interface can be collected into, also including the reading of discrete signal point.
2) crane exclusive data gathers 50 equipment has data prediction ability, can write logic according to user, enters
Capable some statistical functions, form statistics or staqtistical data base record.
3) the special collecting device of crane data has certain data buffer storage requirement.
4) the special collecting device of crane data, disclosure satisfy that the requirement that data write back.Can be according to the communication of controller
Agreement, according to order write back data, or can export discrete I/O signal.(this function should have strict control measure, and
And this function can be closed by hardware)
5) harbour Data Collection transmission equipment, can simultaneously connect the crane exclusive data on different cranes in harbour
50 equipment are gathered, 100 equipment can be at most connected.
6) harbour Data Collection transmission equipment, as needed, can preserve the historical data of at least 2 years.
7) each crane exclusive data gathers 50 equipment, and harbour Data Collection transmission equipment, is respectively provided with its exclusive
ID, so that high in the clouds deployment program can bind with specific crane facility.
B) the security guarantee of crane monitoring data
The security of crane monitoring data mainly includes：The transmission safety of monitoring data and the storage peace of high in the clouds data
Entirely.Crane data there may be loss of data, data delay and leaking data equivalent risk in transmitting procedure.Base can be used
Data transmission credibility is improved in the transmission means of the multipoint parallel of Transmission Control Protocol, determining for IP network architecture is redesigned
Inhibition and generation network service, improves the adaptivity of network transmission, and ensures the security of data using various access control policies, from
And the common-denominator target such as realize real-time data transmission, arrive safe and sound.
The main research of monitoring data transmission safety includes the integrity problem to data transfer in cloud computing platform, using one
ICP/IP protocol multipoint parallel transmission means is planted, long data block is cut, network is then reduced using parallel mode transmission
Delay；Build open network SRM technology (NDN), design NDN access vector caches strategy, request of data plan
Slightly, the strategic layer of data forwarding strategy and guarantee data integrity, the safe floor of reliability；With access control policy to cloud meter
Calculate network data and be controlled access, data are improved from networking access control, the control of authority of network, directory level security control etc.
Security.
Further, since crane supervising data storage is beyond the clouds in server 27, inevitably exist by internal staff
Or the risk that developer steals, it is therefore desirable to monitoring data is encrypted, is combined with asymmetric encryption using symmetric cryptography
Thinking solve the problems, such as data safety storage in cloud computing.
C) crane remote in-service monitoring
Different from traditional RCMS, crane remote in-service monitoring is to dispose crane facility management system beyond the clouds.
Crane remote in-service monitoring has different door entrances for intra-company personnel and harbour user.For corporate user, can
By global crane in-service monitoring portal accesses, immediate data is derived from the integrated system in PaaS, and is aided with related increment clothes
Business.For harbour user, accessed by single harbour in-service monitoring door (RCMS functions can be substituted), data concentrate on comprehensive
Environmental data in syzygy system, such as monitoring of mechanism status, suspender state and Third party system.
D) the online preventive maintenance analysis of crane remote
The system will be disposed beyond the clouds, can provide preventive maintenance service for the crane facility of all harbours.Prevention
Property safeguard refer to equipment failure before to equipment complex using various possible methods, means, technology improve equipment
Can, to realize the due function of equipment.Preventative maintenance has certain perspective, can regard the current state of production system,
Reasonably plan preventive maintenance schedule.
System will set up threshold values to some key indexs, according to and will receive data be compared with threshold values it is right to determine
The response strategy answered, such as notifies related system.
System is by the way that based on the big data system on cloud, machine learning comes from the magnanimity number of large quantities of cranes to these
According to (data such as the warning of the main stress value including mechanical structure, the shock value of principal organ, and machine at this stage, failure)
Be analyzed, can anticipation mechanical structure problem that may be present, especially after various serious natural weathers, in time analysis,
Potential problems are found ahead of time.For main travelling mechanism, such as motor 10, the mechanism such as reductor, by data analysis, also may be used
To carry out anticipation, potential problems are found in time.Preventative maintenance can the effective low maintenance cost of sinking, reduce product cost, and
Enable users to prepare corresponding spare part in advance or repaired before problem represents, so that when farthest reducing service disconnection
Between, improve enterprise competitiveness.
To realize that the online preventive maintenance analysis of crane remote sets, it is necessary to install special signal acquisition on crane
Standby, including the signal transducer such as vibrative mechanism stress, vibrations, the signal transducer such as vibrations, temperature of mechanism is special
Signal acquisition supervision equipment etc..
The interface of the system and analysis result, will be presented by global crane in-service monitoring door.
E) remote service management system
Technical support personnel can by the equipment of remote service management system docking access customer carry out remote monitoring, troubleshooting and
Technological guidance and with the work such as user's online exchange.Additionally, the system also has maintenance of information and audit function.Maintenance of information
Main Back ground Information including equipment, knowledge base, the management of accessing user etc..Audit function is mainly to behaviour important on platform
Recorded, and for inquiring about afterwards.
Crane steel structure health monitoring and evaluation system 45 can be realized entering the key point stress abnormality of crane structure 11
Emergency situations are given a warning by row analysis, judgement, are reminded and are checked in time.Data correction crane structure can be used according to true
11 actual lives estimated the theoretical value of 35 modules, the assessment that the actual life of crane structure 11 estimates 35 modules is more met currently
The actually used situation of bridge crane.
The vertical vibration of the Main Analysis motor 10 of Mechanism of crane monitoring and evaluation system 44, horizontal vibration, amplitude have
Valid value and motor 10 and the temperature of reductor, the alarm to Mechanism of crane is realized by the monitoring to thresholding.
Crane steel structure crackle Long-Range Surveillance System can allow staff to pass through the key of system investigation crane
Point photo, patrols and examines in hgher efficiency than manual site, the problems such as the personnel in real work that can solve patrol and examine not in place.Remotely monitor work(
Can be mainly by disposing a set of remotely monitor recognition software system in monitoring central server 27, automatically regularly from scene
Each key point image photograph of acquisition crane on NVR, and carry out keeping records according to harbour, crane and time.
The Identification of Cracks of view-based access control model is main by image processing techniques, and automatic identification steel construction cracking plays effective
Remind and prevention effect.And field condition can be reaffirmed by long-range connection, direct access image.Automatic recognition system
Starting point, friendly interactive interface, even if in the case where that cannot automatically confirm that, it is also possible to by regarding are automatically recognized as with slight crack
Frequency picture helps remote personnel to confirm field condition, reaches identification exhaustive, so as to mitigate the working strength of operating personnel significantly.
Whole system can be divided into five parts：Picture collection, image procossing, pattern match, data are compared, result treatment.
The crane fault diagnosis and Predict analysis system analyzed based on machine learning and big data are referred in crane facility
Failure is accurately predicted before occurring, and the trouble saving generation that can adopt an effective measure.By crane steel knot
Structure key position important parameter, such as run time of crane motor 10, overlond running (rated load 100%) time, high capacity
When operation (rated load 120%) time, frequency converter run total time (having output), conduction time (rectification side operation), are powered
Between the associated monitoring parameter such as point (rectification side operation) be monitored, crane steel structure health evaluating is set up based on history big data
39 models, the crane to current operation carries out health state evaluation.Abnormal information is typically to cause zero in Mechanism of crane
The principal element that part is damaged.By to crane important spare part such as speed changer, encoder, brake 12, Diesel engine, steel
Cord etc. carries out status monitoring, collects history big data and simultaneously analyzes, the method based on machine learning to history anomaly and its
Corresponding key parameter collection modeling.The probabilistic model that each Parameters variation is damaged with parts is described.Additionally, can also be by the probability mould
Type is compared with current multi-parameter Probability State space, reaches realization and judges the current health status of component with trend point
Analysis.Rational suggestion is given to harbour operation, for example：Reasonable arrangement maintenance plan, acknowledgment copy procurement plan etc..Based on distribution
The real time historical database and industrial monitoring and control software of framework are mainly with cloud service platform with PaaS (Platform as a
Service) service form is presented.With computing capability is extended on demand, without buying and safeguarding hardware, virtualization can be made full use of
Various services the features such as.Real time historical database and industrial monitoring and control software based on distributed structure/architecture can be realized based on Hadoop
The real time historical database of distributed structure/architecture, it is possible to achieve following functions：1) big data quantity history disposal ability, based on distribution
Clustered deploy(ment), can million grades of rapid saving data point, and ensure the reliability of data.2) file system of efficient redundancy,
With efficient disk IO read-write ability and highly reliable data backup function.3) powerful data analysis capabilities, using point
The parallel computation of cloth, the statistics and analysis that can carry out big data quantity are calculated, there is provided valuable various analyze datas.
Those of ordinary skill in the art it should be appreciated that the embodiment of the above be intended merely to explanation the present invention,
And be not used as being limitation of the invention, as long as in spirit of the invention, the change to embodiment described above
Change, modification will all fall in the range of claims of the present invention.
Claims (6)
1. a kind of container crane inline diagnosis and preventative maintenance system based on big data, it is characterised in that including：
Crane facility layer, sensor layer, Internet, data service layer, wherein the data service layer is further divided into data
Analysis and model layer, data application layer, business system layer；
The operational factor of the crane facility layer reception crane；
The sensor layer receives the sensor signal of crane key position；
The Internet transmits to the data service layer operational factor and sensor signal；
The data analysis and the model layer default normal moving model of crane and fault model, operational factor and sensor are believed
Number compared with the model, so as to judge the running status of crane；
Fortune of the data application layer according to the operational factor and sensor signal prediction crane within following a period of time
Row state, and fault pre-alarming is generated with this；
Business system layer setting external data interface, and by the data analysis and model layer, the data of data application layer
Exported to outside.
2. container crane inline diagnosis and preventative maintenance system based on big data as claimed in claim 1, it is special
Levy and be：
When the running status of crane is judged as failure in data analysis and model layer, the data analysis and model layer will
Parameter in normal moving model is transmitted to the crane.
3. container crane inline diagnosis and preventative maintenance system based on big data as claimed in claim 2, it is special
Levy and be：
The operational factor threshold value and key position threshold value of the default crane of the data application layer；
The data application layer is according to the operational factor and sensor signal in a period of time, the operation under prediction in a period of time
Parameter and sensor signal, and by the operational factor and sensor signal of the prediction and operational factor threshold value and key position threshold
Value compares, so as to generate fault pre-alarming.
4. container crane inline diagnosis and preventative maintenance system based on big data as claimed in claim 1, it is special
Levy and be：
The operational factor of the crane includes the operation ginseng of motor, crane structure, brake, dolly, decelerator, gearbox
Number.
5. container crane inline diagnosis and preventative maintenance system based on big data as claimed in claim 1, it is special
Levy and be：
The sensor of the key position include grating strain transducer, displacement transducer, vibrating sensor, temperature sensor,
Pick up sensor, monitoring camera.
6. container crane inline diagnosis and preventative maintenance system based on big data as claimed in claim 1, it is special
Levy and be：
Data analysis and model layer carry out load analysis of spectrum and stress analysis of spectrum, failure mould according to operational factor and sensor signal
Type, life prediction, preventative maintenance analysis, audio analysis.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611088589.0A CN106698197B (en) | 2016-12-01 | 2016-12-01 | Container crane inline diagnosis and preventative maintenance system based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611088589.0A CN106698197B (en) | 2016-12-01 | 2016-12-01 | Container crane inline diagnosis and preventative maintenance system based on big data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106698197A true CN106698197A (en) | 2017-05-24 |
| CN106698197B CN106698197B (en) | 2018-01-16 |
Family
ID=58934391
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611088589.0A Active CN106698197B (en) | 2016-12-01 | 2016-12-01 | Container crane inline diagnosis and preventative maintenance system based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106698197B (en) |
Cited By (26)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107399672A (en) * | 2017-09-11 | 2017-11-28 | 深圳市航天华拓科技有限公司 | crane health monitoring system and method |
| CN107463146A (en) * | 2017-08-18 | 2017-12-12 | 大连美恒时代科技有限公司 | A kind of method that crane failure is diagnosed by MHCIMS |
| CN108190745A (en) * | 2017-12-27 | 2018-06-22 | 上海沪东集装箱码头有限公司 | A kind of tyre crane distributed data acquisition system and method |
| CN108229541A (en) * | 2017-12-11 | 2018-06-29 | 上海海事大学 | Bar stress data classification method in a kind of gantry crane based on K nearest neighbor algorithms |
| CN108227617A (en) * | 2018-02-13 | 2018-06-29 | 上海振华重工(集团)股份有限公司 | A kind of tyre crane remote detection and assessment system |
| CN108928744A (en) * | 2018-08-02 | 2018-12-04 | 深圳汇通智能化科技有限公司 | A kind of container crane inline diagnosis and maintenance system based on big data |
| CN109019349A (en) * | 2018-06-19 | 2018-12-18 | 上海振华重工(集团)股份有限公司 | fault detection method, detector, computer storage medium and crane |
| CN109374044A (en) * | 2018-09-30 | 2019-02-22 | 北京英视睿达科技有限公司 | A kind of mostly ginseng enviromental monitoring equipment intelligence self-repairing method and device |
| CN109894875A (en) * | 2017-11-29 | 2019-06-18 | 林肯环球股份有限公司 | Support predictive and preventive maintenance system and method |
| CN110807250A (en) * | 2019-10-22 | 2020-02-18 | 扬州大学 | Crane health monitoring method based on sensitivity vector compromise and competition relationship |
| CN111002328A (en) * | 2019-12-05 | 2020-04-14 | 广州赛特智能科技有限公司 | Wheeled robot checking system and method |
| CN111044869A (en) * | 2020-01-20 | 2020-04-21 | 上海杰臻电气技术有限公司 | Port wharf insulation online detection system |
| CN111458129A (en) * | 2020-04-29 | 2020-07-28 | 江苏省特种设备安全监督检验研究院 | High-precision online detection system for cantilever beam of crane |
| CN111562125A (en) * | 2020-05-15 | 2020-08-21 | 海南省锅炉压力容器与特种设备检验所 | Crane detection system and method based on vision and vibration |
| CN112012695A (en) * | 2020-09-27 | 2020-12-01 | 中油国家油气钻井装备工程技术研究中心有限公司 | Petroleum drilling machine auxiliary guiding device based on edge calculation and guiding method thereof |
| CN112061988A (en) * | 2020-09-22 | 2020-12-11 | 上海振华重工(集团)股份有限公司 | Crane distributed network monitoring and single machine monitoring management system and method |
| CN112357771A (en) * | 2020-11-19 | 2021-02-12 | 中船重工(青岛)海洋装备研究院有限责任公司 | Ship-shore integrated equipment state monitoring system and method |
| CN113009904A (en) * | 2021-02-23 | 2021-06-22 | 包头钢铁（集团）有限责任公司 | Intelligent monitoring system for cyclone well travelling crane |
| CN113086852A (en) * | 2021-03-31 | 2021-07-09 | 赤湾集装箱码头有限公司 | Container spreader management system based on Internet of things |
| CN113301381A (en) * | 2021-05-17 | 2021-08-24 | 上海振华重工(集团)股份有限公司 | Multi-source data playback system |
| CN113291980A (en) * | 2021-05-14 | 2021-08-24 | 上海振华重工(集团)股份有限公司 | Multisource fault alarm system suitable for automatic crane |
| CN113620191A (en) * | 2020-05-09 | 2021-11-09 | 徐州重型机械有限公司 | Crane operation protection method, device and system and crane |
| CN113788405A (en) * | 2021-08-03 | 2021-12-14 | 杭州大杰智能传动科技有限公司 | Visual image processing system and method for monitoring operation condition of intelligent tower crane |
| CN114488801A (en) * | 2022-01-18 | 2022-05-13 | 无锡安起科技有限公司 | Bridge crane model prediction control method based on data driving |
| CN114715806A (en) * | 2022-06-08 | 2022-07-08 | 杭州未名信科科技有限公司 | Emergency control method, device and medium for abnormal state of tower crane and tower crane |
| CN115293443A (en) * | 2022-08-17 | 2022-11-04 | 上海国际港务（集团）股份有限公司 | Method, system and medium for predicting loading and unloading operation time of bridge crane and container ship |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101893510A (en) * | 2009-05-18 | 2010-11-24 | 中国石化集团南京化学工业有限公司 | Device-based one-machine-one-file on-line monitoring and fault diagnosis system |
| CN102495615A (en) * | 2011-12-26 | 2012-06-13 | 中铁科工集团有限公司 | Remote monitoring management system of large tonnage box girder transport and erection equipment group safety construction |
| CN102830691A (en) * | 2012-07-20 | 2012-12-19 | 南京航空航天大学 | Automatic detecting and fault diagnosing method of unmanned aerial vehicle based on cloud computing |
| CN103241658A (en) * | 2013-04-27 | 2013-08-14 | 广州市特种机电设备检测研究院 | Internet of Things-based health monitoring and security prewarning system of crane metal structure |
| CN203237942U (en) * | 2013-04-18 | 2013-10-16 | 鞍钢股份有限公司 | Crane operation status monitoring and information management system |
| CN105467949A (en) * | 2015-05-19 | 2016-04-06 | 上海谷德软件工程有限公司 | Crane remote monitoring and intelligent maintenance system based on IOT and DSP |
| CN105967063A (en) * | 2016-05-16 | 2016-09-28 | 上海振华重工电气有限公司 | Failure analyzing and handling system and method of maintenance platform |
-
2016
- 2016-12-01 CN CN201611088589.0A patent/CN106698197B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101893510A (en) * | 2009-05-18 | 2010-11-24 | 中国石化集团南京化学工业有限公司 | Device-based one-machine-one-file on-line monitoring and fault diagnosis system |
| CN102495615A (en) * | 2011-12-26 | 2012-06-13 | 中铁科工集团有限公司 | Remote monitoring management system of large tonnage box girder transport and erection equipment group safety construction |
| CN102830691A (en) * | 2012-07-20 | 2012-12-19 | 南京航空航天大学 | Automatic detecting and fault diagnosing method of unmanned aerial vehicle based on cloud computing |
| CN203237942U (en) * | 2013-04-18 | 2013-10-16 | 鞍钢股份有限公司 | Crane operation status monitoring and information management system |
| CN103241658A (en) * | 2013-04-27 | 2013-08-14 | 广州市特种机电设备检测研究院 | Internet of Things-based health monitoring and security prewarning system of crane metal structure |
| CN105467949A (en) * | 2015-05-19 | 2016-04-06 | 上海谷德软件工程有限公司 | Crane remote monitoring and intelligent maintenance system based on IOT and DSP |
| CN105967063A (en) * | 2016-05-16 | 2016-09-28 | 上海振华重工电气有限公司 | Failure analyzing and handling system and method of maintenance platform |
Cited By (35)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107463146A (en) * | 2017-08-18 | 2017-12-12 | 大连美恒时代科技有限公司 | A kind of method that crane failure is diagnosed by MHCIMS |
| CN107399672A (en) * | 2017-09-11 | 2017-11-28 | 深圳市航天华拓科技有限公司 | crane health monitoring system and method |
| CN109894875B (en) * | 2017-11-29 | 2022-04-05 | 林肯环球股份有限公司 | System and method for supporting predictive and preventative maintenance |
| CN109894875A (en) * | 2017-11-29 | 2019-06-18 | 林肯环球股份有限公司 | Support predictive and preventive maintenance system and method |
| CN108229541B (en) * | 2017-12-11 | 2021-09-28 | 上海海事大学 | Shore bridge middle pull rod stress data classification method based on K nearest neighbor algorithm |
| CN108229541A (en) * | 2017-12-11 | 2018-06-29 | 上海海事大学 | Bar stress data classification method in a kind of gantry crane based on K nearest neighbor algorithms |
| CN108190745A (en) * | 2017-12-27 | 2018-06-22 | 上海沪东集装箱码头有限公司 | A kind of tyre crane distributed data acquisition system and method |
| CN108190745B (en) * | 2017-12-27 | 2020-02-18 | 上海沪东集装箱码头有限公司 | Distributed data acquisition system and method for tyre crane |
| CN108227617A (en) * | 2018-02-13 | 2018-06-29 | 上海振华重工(集团)股份有限公司 | A kind of tyre crane remote detection and assessment system |
| CN109019349A (en) * | 2018-06-19 | 2018-12-18 | 上海振华重工(集团)股份有限公司 | fault detection method, detector, computer storage medium and crane |
| CN108928744A (en) * | 2018-08-02 | 2018-12-04 | 深圳汇通智能化科技有限公司 | A kind of container crane inline diagnosis and maintenance system based on big data |
| CN109374044B (en) * | 2018-09-30 | 2023-11-10 | 国际商业机器（中国)投资有限公司 | Intelligent automatic restoration method and device for multi-parameter environment monitoring equipment |
| CN109374044A (en) * | 2018-09-30 | 2019-02-22 | 北京英视睿达科技有限公司 | A kind of mostly ginseng enviromental monitoring equipment intelligence self-repairing method and device |
| CN110807250A (en) * | 2019-10-22 | 2020-02-18 | 扬州大学 | Crane health monitoring method based on sensitivity vector compromise and competition relationship |
| CN110807250B (en) * | 2019-10-22 | 2023-08-22 | 扬州大学 | Crane health monitoring method based on sensitivity vector compromise and competition relationship |
| CN111002328A (en) * | 2019-12-05 | 2020-04-14 | 广州赛特智能科技有限公司 | Wheeled robot checking system and method |
| CN111044869A (en) * | 2020-01-20 | 2020-04-21 | 上海杰臻电气技术有限公司 | Port wharf insulation online detection system |
| CN111458129A (en) * | 2020-04-29 | 2020-07-28 | 江苏省特种设备安全监督检验研究院 | High-precision online detection system for cantilever beam of crane |
| CN113620191A (en) * | 2020-05-09 | 2021-11-09 | 徐州重型机械有限公司 | Crane operation protection method, device and system and crane |
| CN111562125A (en) * | 2020-05-15 | 2020-08-21 | 海南省锅炉压力容器与特种设备检验所 | Crane detection system and method based on vision and vibration |
| CN112061988A (en) * | 2020-09-22 | 2020-12-11 | 上海振华重工(集团)股份有限公司 | Crane distributed network monitoring and single machine monitoring management system and method |
| CN112012695A (en) * | 2020-09-27 | 2020-12-01 | 中油国家油气钻井装备工程技术研究中心有限公司 | Petroleum drilling machine auxiliary guiding device based on edge calculation and guiding method thereof |
| CN112357771A (en) * | 2020-11-19 | 2021-02-12 | 中船重工(青岛)海洋装备研究院有限责任公司 | Ship-shore integrated equipment state monitoring system and method |
| CN113009904A (en) * | 2021-02-23 | 2021-06-22 | 包头钢铁（集团）有限责任公司 | Intelligent monitoring system for cyclone well travelling crane |
| CN113086852A (en) * | 2021-03-31 | 2021-07-09 | 赤湾集装箱码头有限公司 | Container spreader management system based on Internet of things |
| CN113086852B (en) * | 2021-03-31 | 2024-03-22 | 赤湾集装箱码头有限公司 | Container sling management system based on Internet of things |
| CN113291980A (en) * | 2021-05-14 | 2021-08-24 | 上海振华重工(集团)股份有限公司 | Multisource fault alarm system suitable for automatic crane |
| CN113301381A (en) * | 2021-05-17 | 2021-08-24 | 上海振华重工(集团)股份有限公司 | Multi-source data playback system |
| CN113788405A (en) * | 2021-08-03 | 2021-12-14 | 杭州大杰智能传动科技有限公司 | Visual image processing system and method for monitoring operation condition of intelligent tower crane |
| CN113788405B (en) * | 2021-08-03 | 2023-06-02 | 杭州大杰智能传动科技有限公司 | Visual image processing system and method for monitoring operation condition of intelligent tower crane |
| CN114488801B (en) * | 2022-01-18 | 2023-12-22 | 无锡安起科技有限公司 | Bridge crane model prediction control method based on data driving |
| CN114488801A (en) * | 2022-01-18 | 2022-05-13 | 无锡安起科技有限公司 | Bridge crane model prediction control method based on data driving |
| CN114715806A (en) * | 2022-06-08 | 2022-07-08 | 杭州未名信科科技有限公司 | Emergency control method, device and medium for abnormal state of tower crane and tower crane |
| CN115293443A (en) * | 2022-08-17 | 2022-11-04 | 上海国际港务（集团）股份有限公司 | Method, system and medium for predicting loading and unloading operation time of bridge crane and container ship |
| CN115293443B (en) * | 2022-08-17 | 2024-03-22 | 上海国际港务（集团）股份有限公司 | Bridge crane and container ship loading and unloading operation time prediction method, system and medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106698197B (en) | 2018-01-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106698197B (en) | Container crane inline diagnosis and preventative maintenance system based on big data | |
| CN206115248U (en) | Numerical control fault diagnostic based on internet | |
| CN106655522A (en) | Master station system suitable for operation and maintenance management of secondary equipment of power grid | |
| CN108928744B (en) | Container crane online diagnosis and maintenance system based on big data | |
| CN108388950A (en) | Intelligent transformer O＆M method and system based on big data | |
| CN105515180A (en) | Intelligent substation communication network dynamic monitoring system and monitoring method thereof | |
| CN113283344A (en) | Mining conveying belt deviation detection method based on semantic segmentation network | |
| CN101224854A (en) | Crane integrated observation system and method | |
| CN114881808B (en) | Big data-based accurate identification method for electric power larceny and electric power larceny prevention system | |
| CN116862442A (en) | Intelligent equipment management method and system based on big data analysis | |
| CN115566804B (en) | Electric power monitoring system based on distributed optical fiber sensing technology | |
| CN108227617A (en) | A kind of tyre crane remote detection and assessment system | |
| CN111092492A (en) | High-voltage cable line operation monitoring system | |
| CN109051104A (en) | A kind of bottle placer operating status intelligent Detection and method | |
| CN109768971A (en) | A method of based on network flow real-time detection industrial control host state | |
| CN111755969A (en) | High protection modularization block terminal | |
| CN113763667A (en) | Fire early warning and state monitoring device and method based on 5G edge calculation | |
| CN112363029A (en) | Equipment corona discharge online monitoring system and monitoring method thereof | |
| CN112180865A (en) | Intelligent distribution room and equipment monitoring system | |
| CN115131505A (en) | Multi-system fusion's of transformer substation panorama perception system | |
| CN109285331B (en) | Power cable temperature early warning system based on data analysis and temperature prediction | |
| CN112285631A (en) | Judgment method and system for active early warning of current transformer | |
| CN117235443A (en) | Electric power operation safety monitoring method and system based on edge AI | |
| CN115246609A (en) | Elevator safety prevention and control cloud platform and operation state evaluation and processing method | |
| CN109978192A (en) | A kind of huge hydroelectric power plant's state-maintenance model building method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |