CN101428735B - Load moment limiting device self-adaption accuracy calibrating method based on artificial neural network algorithm - Google Patents
Load moment limiting device self-adaption accuracy calibrating method based on artificial neural network algorithm Download PDFInfo
- Publication number
- CN101428735B CN101428735B CN 200710166216 CN200710166216A CN101428735B CN 101428735 B CN101428735 B CN 101428735B CN 200710166216 CN200710166216 CN 200710166216 CN 200710166216 A CN200710166216 A CN 200710166216A CN 101428735 B CN101428735 B CN 101428735B
- Authority
- CN
- China
- Prior art keywords
- abnormal nodes
- data
- node
- crane
- hoisting crane
- 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.)
- Expired - Fee Related
Links
Landscapes
- Jib Cranes (AREA)
Abstract
A precision calibration method of a load moment limiter which timely measures and displays crane working condition parameters as the weight, the amplitude, the length, the angles and the like. The method comprises the following steps: adopting the self-adapting calibration technology based on artificial neural net algorithm; through transforming a circuit, a single chip microcomputer and a memory to automatically collect and store the working condition parameters in different motion states (upward and downward or stillness) and timely operating and treating data, obtaining precision value of the weight and the amplitude of the crane in the working condition; having no need for operating and treating data additionally; effectively excluding the errors due to man-made interference; and greatly improving the efficiency and the precision of the load moment limiter. The method can cause the load moment limiter to precisely calibrate according to the practical use condition, improve the precision and the debugging efficiency of the load moment limiter, effectively guarantee the operating safety of the crane, and is widely applicable to the precision calibration of load moment limiters of mobile cranes (an automobile crane, a crawler crane and a tyre crane) and non-mobile cranes.
Description
1, technical field
The present invention is the accuracy calibrating method of hoisting crane operating mode parameters such as the real-time weight of measuring and showing of a kind of load moment limiter, amplitude, length, angle.
2, background technology
Load moment limiter (is measured length by position transduser; signals such as angle) and the power sensor (measure pulling force; signals such as pressure) obtain the real-time working condition parameter signal; the accuracy calibrating method that adopts is to measure hoisting crane sensor signal during different lift heavy under various operating modes; obtain parameter translation operation formula by specific algorithm; write micro controller system then and handle computing; obtain actual hoisting capacity and amplitude and compare judgement with maximum torque; under precarious position, export control signal; avoid hoisting crane to move to precarious position, thereby play the safety guard-safeguard effect.But, load moment limiter this accuracy calibrating method that generally adopts need expend lot of manpower and material resources and time and hoisting crane is carried out duty parameter measure, easily because the accidentalia in the measurement process or human error produce mistake, and the duty parameter data in the crane arm motion process are difficult to measure gathers, cause the load moment limiter precision not enough, produce false judgment.
3, summary of the invention
Long in order to overcome the existing load moment limiter precision alignment time, efficient is low, be easy to generate the problem of error, the present invention adopts the adaptive calibration technology based on artificial neural network algorithm, pass through change-over circuit, micro controller system and memory device are gathered the storage crane arm at the different motion state (upwards automatically, downward or static) the duty parameter data and carry out calculation process immediately, obtain this hoisting crane carries out weight and the amplitude of work under this operating mode accurate numerical value, not needing to carry out in addition data operation handles, effectively get rid of the error that artificial interference produces, improve efficient and the accuracy rate of the calibration of load moment limiter precision greatly.
The technology used in the present invention solution is: the operating mode of hoisting crane is reasonably divided, and is that coordinate makes up data acquisition neural network system of axes with the angle between brachium and crane arm and the level; By the sensor signal of the automatic acquisition process of change-over circuit storage crane arm at different motion state (upwards, downward or static), and the input micro controller system carries out calculation process, according to certain rule with the automatic real-time storage of transformation result to the memory device designated area; The parameter translation operation formula that obtains by specific algorithm is finished and is adopted artificial neural network algorithm that the data of gathering are judged, identify unusual node of network, the abnormal nodes data are rejected, and generated the overall process of effective node data according to the adjacent node data automatically; The limiter of moment main frame is identified crane operating status according to position transduser when hoisting crane is worked, obtain the hoisting crane force-bearing situation by the power sensor, automatically call the data of storing and carry out calculation process, thereby real-time working condition such as the weight of obtaining, amplitude data, show in man-machine interface, and export control signal according to the result with the maximum torque judgement.
The present invention can make limiter of moment carry out the precision calibration according to the actual use state of hoisting crane, improve precision and the debugging efficiency of load moment limiter, effectively guarantee the job safety of hoisting crane, be widely used in the limiter of moment precision calibration of runabout crane (car hosit, crawler crane, wheel-mounted crane) and non-flowing type hoisting crane.
4, the specific embodiment
The present invention need specifically implement by the human-machine operation flow process of core algorithm and software realization, hardware design and regulation.
4.1 core algorithm and software are realized
The present invention adopts the neural network adaptive technique as the basis of core algorithm, comprises with the lower part:
4.1.1 the artificial neural net (ANN) system of axes makes up
The present invention is divided into hoisting crane retractable and can not freely stretches two major types according to the operating characteristic of hoisting crane, makes up system of axes with the angle between boom length and crane arm and the level as coordinate axle.For the hoisting crane (as car hosit) of retractable, the length section of indicating according to its lifting property list is as the foundation of brachium coordinate node division; For the hoisting crane that can not freely stretch (as crawler crane), then according to himself different brachiums as brachium coordinate node division foundation.In view of hoisting crane descends angle between crane arm and the level generally between the 20-80 degree in working order, so angle coordinate is start node with 22 degree, every three degree as node coordinate, till 79 degree, by making up the neural network system of axes with upper type.
4.1.2 data storage and access rule
After making up the neural network system of axes, according to the state of kinematic motion of crane jib the data of required storage are divided into upwards and downward two spaces, each space is made up of the matrix of m individual 5 * 20, and wherein m is that brachium is divided the node number, and each matrix is the data of this brachium node.It is as follows that the ranks of matrix represent meaning:
Wherein: self registering angle signal conversion value when the a0-hoisting crane moves under light condition;
Self registering angle signal conversion value when the a1-hoisting crane moves under having hung constant weight counterweight state;
Self registering force sensor signals conversion value when the F0-hoisting crane moves under light condition;
Self registering force sensor signals conversion value when the F1-hoisting crane moves under having hung constant weight counterweight state;
The weight of used counterweight when the W1-hoisting crane is calibrated at this brachium node.
After calibration was finished, micro controller system stored data in the memory device designated area by above-mentioned storage rule automatically.
When hoisting crane is worked, micro controller system is according to the corresponding matrix of visiting when forearm length and state of kinematic motion in the corresponding space, judge according to angle value then and call the corresponding one group of data of respective angles node, use these data to carry out computing and obtain lifted weight Weight Calculation result, and show at the assigned address of man-machine interface.
4.1.3 abnormal nodes is identification and repairing automatically
Carry out hoisting crane when calibration, because the influence of the service conditions restriction of hoisting crane own or manual operation factor, may cause mis-calculate when the hoisting crane real work at the correct record data of some node.For avoiding such mistake, adopt identifier the data of each node to be judged whether the correct data that records when calibrating for hoisting crane.If identifier shows the incorrect record data of certain node, then this node is abnormal nodes.After judging that certain node is abnormal nodes, from this node adjacent node is judged, if adjacent node still is abnormal nodes, then continue next adjacent node is judged, till finding non-abnormal nodes.After searching out non-abnormal nodes, neighbouring relations according to abnormal nodes and non-abnormal nodes are carried out interpolation arithmetic, its rule is: when abnormal nodes falls between two non-abnormal nodes, carry out inside interpolation calculation by two non-abnormal nodes data, deposit the gained data in former abnormal nodes position, identifier is revised as non-abnormal nodes; In the time of outside abnormal nodes falls within two non-abnormal nodes, carry out outside interpolation calculation by two non-abnormal nodes data, deposit the gained data in former abnormal nodes position, identifier is revised as non-abnormal nodes.So carry out automatic abnormal nodes identification and repairing by rule, till all back end all are non-abnormal nodes.
4.1.4 software is realized
Core algorithm of the present invention can be realized by certain machine language such as C language, assembly language etc.
4.2 hardware design
In order to implement method of the present invention, the hardware design of load moment limiter must comprise with the lower part:
4.2.1 signal converter amplifier circuit
In the invention process process, need real-time pick-up transducers signal, must be according to the range modelled signal converter amplifier circuit of sensor signal in the limiter of moment hardware design, by this circuit the sensor signal that collects is converted to the respective value that micro controller system can calculation process.
4.2.2 memory device
Selecting high capacity highly effective rate memory device for use is the concrete gordian technique route of implementing of the present invention.The applicant selects the FRAM FeRAM memory chip of Ramtron company for use, can read and write data expeditiously in a large number, the writing data into memory of gathering in the calibration process is preserved, and in hoisting crane work process, read institute's deposit data in the memory device according to different operating mode visits, carry out calculation process in real time.
4.2.3 micro controller system and peripheral circuit
Limiter of moment adopts micro controller system as CPU, realizes functions such as incoming signal conversion process, computing, control signal output, man-machine interface demonstration by CPU and peripheral circuit.
4.3 human-machine operation flow process
Be ready to the counterweight of known accurate weight, the brachium operating mode that affirmation need be calibrated: car hosit uses the length in the rated load weight table to calibrate; Crawler crane is calibrated by the real work brachium.Hoisting crane is parked on the solid level ground and is in normal working; Length, the angle of checking the moment telltale equate that with actual value error is less than ± 1% with the amplitude displayed value.
4.3.1 empty hook calibration
Carry out calibration operation according to the following steps:
A. by menu prompt the demonstration gravimetric value is modified as current suspension hook weight.
B. the downward luffing to 20 of principal arm is spent position, the left and right sides.
Press ← key when c. principal arm begins at the uniform velocity slowly to make progress luffing, enter automatic quickly calibrated menu.
Make slowly the luffing action into luffing downwards when d. principal arm makes progress luffing to 79 °, up to reference position, the weight calibration finishes, and returns previous menu.
4.3.2 counterweight weight calibration
Carry out calibration operation according to the following steps:
A. by menu prompt the demonstration gravimetric value is modified as the total weight of current counterweight+suspension hook.
B. the downward luffing of principal arm is to the position that shows that weight equates with rated load weight.
Press ← key when c. principal arm begins at the uniform velocity slowly to make progress luffing, enter automatic quickly calibrated menu.
Make at the uniform velocity slowly the luffing action into luffing downwards when d. principal arm makes progress luffing to 79 °, up to reference position, the weight calibration finishes, and returns previous menu.
Claims (1)
1. a load moment limiter is measured and the accuracy calibrating method of the weight that shows, amplitude, length, angle hoisting crane operating mode parameter in real time, employing is based on the adaptive calibration technology of artificial neural network algorithm, automatically gather the storage crane arm in the duty parameter data of different motion state and carry out calculation process immediately by change-over circuit, micro controller system and memory device, obtain this hoisting crane carries out weight and the amplitude of work under this operating mode accurate numerical value, described state of kinematic motion comprises upwards, downward or static; Described accuracy calibrating method is specially, the operating mode of hoisting crane is reasonably divided, be that coordinate makes up data acquisition neural network system of axes with the angle between brachium and crane arm and the level, hoisting crane for retractable, the length section of indicating according to its lifting property list is as the foundation of brachium coordinate node division, for the hoisting crane that can not freely stretch, then according to himself different brachiums as brachium coordinate node division foundation, angle coordinate is start node with 22 degree, as node coordinate, till 79 degree, make up the neural network system of axes every three degree; After making up the neural network system of axes, according to the state of kinematic motion of crane jib the data of required storage are divided into upwards and downward two spaces, each space is made up of the matrix of m individual 5 * 20, and wherein m is that brachium is divided the node number, and each matrix is the data of this brachium node; By the sensor signal of the automatic acquisition process storage of change-over circuit crane arm at the different motion state, and the input micro controller system carries out calculation process, according to certain rule the automatic real-time storage of transformation result is arrived the memory device designated area, described memory device is the FRAM FeRAM memory chip; Adopt artificial neural network algorithm that the data of gathering are judged, identify unusual node of network, the abnormal nodes data are rejected, and generate effective node data automatically according to the adjacent node data; The limiter of moment main frame is identified crane operating status according to position transduser when hoisting crane is worked, obtain the hoisting crane force-bearing situation by the power sensor, automatically call the data of storing and carry out calculation process, thereby real-time working condition such as the weight of obtaining, amplitude data, show in man-machine interface, and export control signal according to the result with the maximum torque judgement;
Described method also comprises automatically identification and the step of repairing of abnormal nodes: adopt identifier the data of each node to be judged whether the correct data that records when calibrating for hoisting crane; If identifier shows the incorrect record data of certain node, then this node is abnormal nodes; After judging that certain node is abnormal nodes, from this node adjacent node is judged, if adjacent node still is abnormal nodes, then continue next adjacent node is judged, till finding non-abnormal nodes; After searching out non-abnormal nodes, neighbouring relations according to abnormal nodes and non-abnormal nodes are carried out interpolation arithmetic, its rule is: when abnormal nodes falls between two non-abnormal nodes, carry out inside interpolation calculation by two non-abnormal nodes data, deposit the gained data in former abnormal nodes position, identifier is revised as non-abnormal nodes; In the time of outside abnormal nodes falls within two non-abnormal nodes, carry out outside interpolation calculation by two non-abnormal nodes data, deposit the gained data in former abnormal nodes position, identifier is revised as non-abnormal nodes; So carry out automatic abnormal nodes identification and repairing by rule, till all back end all are non-abnormal nodes.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 200710166216 CN101428735B (en) | 2007-11-08 | 2007-11-08 | Load moment limiting device self-adaption accuracy calibrating method based on artificial neural network algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 200710166216 CN101428735B (en) | 2007-11-08 | 2007-11-08 | Load moment limiting device self-adaption accuracy calibrating method based on artificial neural network algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN101428735A CN101428735A (en) | 2009-05-13 |
| CN101428735B true CN101428735B (en) | 2013-08-21 |
Family
ID=40644419
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN 200710166216 Expired - Fee Related CN101428735B (en) | 2007-11-08 | 2007-11-08 | Load moment limiting device self-adaption accuracy calibrating method based on artificial neural network algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN101428735B (en) |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101665217B (en) * | 2009-09-14 | 2011-08-17 | 长沙中联重工科技发展股份有限公司 | Method for detecting stability of crane and device thereof |
| CN102336362A (en) * | 2011-09-08 | 2012-02-01 | 中联重科股份有限公司 | Method for measuring lifting torque of tower crane, device thereof and monitoring system thereof |
| WO2013033904A1 (en) * | 2011-09-08 | 2013-03-14 | 长沙中联重工科技发展股份有限公司 | Method and device for measuring lifting moment of tower crane and monitoring system |
| CN105460793B (en) * | 2015-12-31 | 2017-07-18 | 浙江三一装备有限公司 | The apparatus and method of test moment limiter |
| CN108387266A (en) * | 2018-02-10 | 2018-08-10 | 深圳万智联合科技有限公司 | Bridge structure safe intelligent monitor system |
| CN108401237A (en) * | 2018-02-10 | 2018-08-14 | 深圳大图科创技术开发有限公司 | A kind of intelligent monitor system of the cruiseway Simulations of Water Waves Due To Landslides based on big data processing |
| CN109998513B (en) * | 2019-01-25 | 2022-04-19 | 芯海科技（深圳）股份有限公司 | Error processing method for calculating HRV signal in human body impedance signal |
| CN111591893A (en) * | 2020-05-27 | 2020-08-28 | 太原科技大学 | Method for measuring hoisting load of automobile crane based on neural network |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN2592616Y (en) * | 2002-12-31 | 2003-12-17 | 三一重工股份有限公司 | Towercrane operation monitoring apparatus |
-
2007
- 2007-11-08 CN CN 200710166216 patent/CN101428735B/en not_active Expired - Fee Related
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN2592616Y (en) * | 2002-12-31 | 2003-12-17 | 三一重工股份有限公司 | Towercrane operation monitoring apparatus |
Non-Patent Citations (2)
| Title |
|---|
| 谷立臣.基于函数型连接神经网络的塔机起重力矩测量方法.《建筑机械》.2005,(第05期),说明书第3－4节. * |
| 贾永峰 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN101428735A (en) | 2009-05-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101428735B (en) | Load moment limiting device self-adaption accuracy calibrating method based on artificial neural network algorithm | |
| CN101077756B (en) | Erecting equipment remote actual time safety monitoring system and its control method | |
| CN102556866B (en) | Stability monitoring system and stability monitoring method of lifting equipment | |
| CN103303802B (en) | Crane control method, crane controller, crane and many crane systems | |
| CN101780926B (en) | Crawler crane moment limiting system | |
| CN201217613Y (en) | Security monitoring management system for tower crane | |
| CN201762043U (en) | Safety monitoring device of tower crane with two horizontal arms | |
| CN201686411U (en) | Crane lifting monitoring system | |
| CN102963828B (en) | Method and device for health diagnosis and safety monitoring of lifting appliance | |
| CN106395630B (en) | A kind of variable amplitude rope weighing algorithm | |
| WO2022222393A1 (en) | Excavator automatic weighing calibration method and system and excavator | |
| CN106957020A (en) | Safety of tower crane management system and management method | |
| CN104555820A (en) | Flexible arm forklift and control system and control method thereof | |
| CN105731279B (en) | The recognition methods of crane parameter type, response error control method and system | |
| CN102653380A (en) | Torque limiting system of folding-arm type lorry-mounted crane | |
| CN201614243U (en) | Moment limiting system of caterpillar crane | |
| CN204359477U (en) | Based on the bucket-wheel stacker reclaimer cantilever shalving ground connection power on-line monitoring system of PLC | |
| CN104495658B (en) | The adjustment method of the limiter of moment of crane, apparatus and system | |
| CN110759281B (en) | Weighing method of telescopic arm structure, equipment and storage medium thereof | |
| CN105069182A (en) | Method monitoring fatigue service life of crane beam | |
| CN202575792U (en) | Folding arm type lorry-mounted crane torque limit device | |
| CN105129637B (en) | Tower crane state parameter measuring method, equipment, system and engineering machinery | |
| CN115258951A (en) | Method and system for detecting actual load of crane | |
| CN210340012U (en) | Intelligent tension protection device for multi-rope hoisting steel wire rope | |
| CN210193236U (en) | Large-scale machinery cooperation hoist and mount data early warning device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| C56 | Change in the name or address of the patentee |
Owner name: PRECISETECH NETWORKS (BEIJING), INC. Free format text: FORMER NAME: PRECISE TECHNOLOGY (BEIJING) CO., LTD. |
|
| CP01 | Change in the name or title of a patent holder |
Address after: 100098 Beijing, Zhichun Road, Haidian District, No. 56, West seven Patentee after: Beijing Purui Seth joint Polytron Technologies Inc Patentee after: Huang Zhengqing Address before: 100098 Beijing, Zhichun Road, Haidian District, No. 56, West seven Patentee before: Precise Technology (Beijing) Co., Ltd. Patentee before: Huang Zhengqing |
|
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20130821 Termination date: 20171108 |