CN101753364B - Equipment state analyzing and predicting and source distributing method and system - Google Patents
Equipment state analyzing and predicting and source distributing method and system Download PDFInfo
- Publication number
- CN101753364B CN101753364B CN2008101851655A CN200810185165A CN101753364B CN 101753364 B CN101753364 B CN 101753364B CN 2008101851655 A CN2008101851655 A CN 2008101851655A CN 200810185165 A CN200810185165 A CN 200810185165A CN 101753364 B CN101753364 B CN 101753364B
- Authority
- CN
- China
- Prior art keywords
- equipment
- prediction
- maintenance
- sensing signal
- resource allocation
- 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.)
- Active
Links
Images
Abstract
The present invention provides equipment state analyzing and predicting and source distributing method and system. Long-range equipment states can be monitored timely, the life period can be diagnosed and predicted according to monitoring information, and further, a proper maintenance scheduling plan is arranged. By the confluence analysis and source plan, problems possibly generated by equipment can be discovered in advance to timely arrange a preventative maintenance scheduling plan, and further, the service life of the equipment is improved to ensure the safety of the equipment and personal.
Description
Technical field
The present invention relates to a kind of method and system, relate in particular to a kind of for equipment state analyses and prediction and resource allocation methods and system for equipment control.
Background technology
In the face of the epoch of globalization competition, no matter be that goods producer or ISP must effectively utilize own resource, keep its competitive advantage.The manpower that resource general reference enterprise can utilize in the operation process, equipment, fund, assets etc., the how running between each resource of effective coordination, it is more competitive that company is operated, and is the present stage informationized society one big problem of competing.
Generally speaking, the operating system of enterprise comprises direct equipment and indirect support equipment.Directly the device systems general reference has the device resource that directly contacts with production or service goal thing, indirect back-up system then be not with production or the direct contactee of service goal thing.In the plant equipment operation process; No matter directly or indirectly equipment all might be safeguarded because of failure to maintain; Or the factor of human negligence causes part damage, causes to produce or the service activity accident is stopped, and makes enterprise receive greatest loss; Especially concern operation and user of service's life safety relevant device, as use the benefit universal mechanization parking apparatus that becomes in the society that urbanizes.
Because parking apparatus belongs to the equipment of the structural design of complicacy,,, possibly cause the danger of life or property in case break down if therefore do not have appropriate managerial and monitoring.In addition, other also relies on effectively monitoring gradually and keeps in repair scheduling so that equipment is effectively managed in the monitoring management and the consideration of keeping in repair along with the huge and security effectiveness of system of factory or electric mechanical equipment.
In the prior art, for example: U.S. Pat .Pat.No.5, the system of 210, the 704 a kind of equipment fault previsions that provided and diagnosis and loss monitoring and life cycle prediction.It is to utilize the mode of expert system and database integration to carry out fault prevision and measures of effectiveness by the interdependence between the data that captured.And U.S. Pat .Pat.No.5,402,475 and 5; 432; 508 administrative skills that a kind of Parking Stall is provided, but in this technology, to how the technology contents to equipment fault prevision and diagnosis and loss monitoring and life cycle prediction does not propose concrete technical approach.In addition; U.S. Pat .Pat.No.5; 445,347 also provide the system of a kind of monitoring with prevision, and it is that direct mode of operation to vehicle produces sensing signal; And through judging back decision mode of operation and prediction to should the contingent problem of mode of operation, and arrange the maintenance scheduling that this device is keeped in repair.Moreover like U.S. Pat .Pat.No.6,748,341 also provide a kind of monitoring and Forecasting Methodology, and it is that the sensing that carries out for equipment produces a series of sensing signal, and by the contingent fault of PMA prediction of output equipment.
Summary of the invention
The present invention provides a kind of equipment state analyses and prediction and resource allocation methods and system; It is the information that captures from real-time detection technique; And then carry out diagnosis and prediction; And can further carry out the arrangement of preventive maintenance, effectively integrate manufacturing system and make back-up system, to promote the usefulness of manufacturing system.
The present invention provides a kind of equipment state analyses and prediction and resource allocation methods and system; It is applied to the mechanical parking equipment control; For the important motive force of development is injected in the logistic support supply system automation of mechanical parking industry; And expection can drive in good time, the logistic support system on right ground, not only can improve the dealer's of parking lot system normal service efficient, and can significantly promote logistic support system dealer's service accuracy rate; Reduce manpower and waste of material, the raising personnel use the fail safe of plant equipment.
The present invention provides a kind of equipment state analyses and prediction and resource allocation methods and system, and it promptly is that specialized application is in distributed environment by the many agents system that imports; Replace the people to carry out communication and coordination; Under given authority, cooperation reaches the set goal, and (target comprises transfer of data, diagnosis; Prediction, and maintenance scheduling arrangement etc.).
In one embodiment, the present invention provides a kind of equipment state analyzing and predicting method, includes the following step: detect a sensing signal that produces about a remote equipment and reach a central management unit via network; Central management unit receives this sensing signal and diagnoses to produce a diagnostic message; Central management unit is according to the life cycle of this diagnostic message prediction about this equipment; And central management unit judges whether and will keep in repair this equipment according to the result of this prediction life cycle, if keep in repair, then seeks suitable maintaining unit with a maintenance decision-making process this equipment is keeped in repair.Equipment state analyzing and predicting method as claimed in claim 1, wherein this equipment is a mechanical parking equipment.
In another embodiment; The present invention provides a kind of parking apparatus state analysis prediction and resource allocation methods; Include the following step: detect and be arranged at the sensing signal that a long-range parking apparatus is produced, and this sensing signal is sent to a central management unit via network; Central management unit receives this sensing signal and diagnoses to produce a diagnostic message; Central management unit is according to the life cycle of this diagnostic message prediction about this equipment; Central management unit judges whether and will keep in repair this equipment according to the result of this prediction life cycle, if keep in repair, then seeks suitable maintaining unit with a maintenance decision-making process this equipment is keeped in repair; And in the process that this maintenance is carried out, carry out the Parking Stall resource allocation management.
In another embodiment, the present invention provides a kind of equipment state analyses and prediction and resource allocation system, comprising: a database; One monitoring module, it connects with an equipment mutually by chance, and the state of this monitoring module monitoring and this equipment of detection is to produce a sensing signal; One central management unit; It is connected with this monitoring module and this database telecommunication; This central management unit receives this sensing signal and this sensing signal is recorded in this database; Also have in this central management unit: a diagnostic module, it is diagnosed to produce a diagnostic message and to predict the life cycle about this equipment according to this diagnostic message according to this sensing signal; And a maintenance arranging module, its result according to this prediction life cycle judges whether and will keep in repair this equipment, if keep in repair, then with the suitable maintaining unit of a maintenance decision-making process searching this equipment keeped in repair.
Describe the present invention below in conjunction with accompanying drawing and specific embodiment, but not as to qualification of the present invention.
Description of drawings
Fig. 1 is prediction of present device state analysis and resource allocation methods embodiment schematic flow sheet;
The schematic flow sheet that Fig. 2 keeps in repair for maintaining unit of the present invention;
Fig. 3 is prediction of present device state analysis and resource allocation methods embodiment sketch map;
Fig. 4 integrates the parking stall resource allocation schematic flow sheet of maintenance scheduling for the present invention;
Fig. 5 A is equipment state analyses and prediction of the present invention and resource allocation system sketch map;
Fig. 5 B is the mechanical parking position sketch map in the mechanical parking equipment of the present invention;
Fig. 6 is a central management unit internal module embodiment sketch map.
Wherein, Reference numeral
2-equipment state analyzing and predicting method
20～23-step
Analyses and prediction of 3-equipment state and resource allocation methods
300～319-step
40～42-database
The 43-monitoring module
Analyses and prediction of 5-equipment state and resource allocation system
The 50-database
The 51-monitoring module
The 52-central management unit
520-computing judge module
The 521-diagnostic module
522-life cycle prediction module
523-keeps in repair arranging module
53-equipment
531-parking assignment module
The 532-parking stall
533,534-transducer
54-supplier
Embodiment
For characteristic of the present invention, purpose and function there being cognition and understanding further, the hereinafter spy describes the relevant thin bilge construction of device of the present invention and the theory reason of design, specifies statement as follows:
See also shown in Figure 1ly, this figure is prediction of present device state analysis and resource allocation methods embodiment schematic flow sheet.In the present embodiment, this method 2 mainly contains the following step: at first carry out step 20, detect a sensing signal that produces about at least one remote equipment and reach a central management unit via network.This remote equipment can be any machinery or electronic motor equipment, and its quantity does not have specific limited, as long as at least more than one.This central management unit can be the device that computer, server or work station etc. have operational capability.In addition; This sensing signal can be vitals on this remote equipment; For example: about the sensing signal of associated voltage, electric current, temperature, pressure, humidity or the tension force of motor, chain, belt or hydraulic package etc. type etc., can have multiplely, might not have only a kind of.
The position is set can be dispersed in different zones of this remote equipment is connected with this central management unit by network (cable network or wireless network) then.This central management unit is connected with a database, can this sensing signal be write down to become a historical information.In the present embodiment; This remote equipment can be mechanical parking equipment, sensing signal that should mechanical parking equipment can be then lift in the mechanical parking equipment moves up and down rate signal, horizontal transfer rate signal, vehicle dimension and weight signal, stops the power supply state signal, personnel pass through sensing signal, temperature sensing signal, hydraulic system pressure signal, cable tension sensing signal, running noise frequency and amplitude signal or vibration signal.
Then carry out step 21, central management unit receives this sensing signal and diagnoses to produce a diagnostic message.In the present embodiment, have the module of diagnosis in the central management unit, its can according in the reading database at any time about the historical information of this sensing signal, then this historical information and new sensing signal are performed calculations and obtain a diagnostic message.The method that wherein produces this diagnostic message also includes the following step: record sensing signal each time is to form a historical information; Summarize the caution value of one of this remote equipment according to historical information; And when receiving new sense data, promptly compare to obtain this diagnostic message with this caution value.For example, be example with the motor running, when motor normally used, according to historical data in the past, the rotary vibration signal frequency of summarizing this motor was the caution value at 60Hz.If but become 59Hz at some time points, on behalf of motor, this might wear out, so this central management unit can be sent alert news according to such diagnostic result.
Next, this central management unit can also step 22 be predicted the life cycle about this equipment according to this diagnostic message.The method of wherein predicting life cycle also includes the following step: record sensing signal each time is to form a historical information; And when receiving new sense data, promptly perform calculations to obtain this prediction life cycle with this historical information.In the present embodiment, the mode of this calculation is chosen as bar row method, decision tree method, genetic algorithm or neural network method.For example: the rotary vibration signal lowest limit that motor system may use is 55Hz; Suppose that detected value is about 60Hz in the historical data; Detected motor running frequency is 59Hz when a time point, and is 58Hz at next time point, as when present detected operating frequency is 57Hz; Possibly only be left 55Hz in the time of then can utilizing step 22 to infer the 5th day, that is in step 22, can infer the motor residual life only remaining 2 days.If the caution time about life cycle of this method is two days, central management unit can be sent alert news when the detection frequency is 57Hz so.At last, central management unit carry out step 23, judges whether and will keep in repair this equipment according to the result of this prediction life cycle, if keep in repair, then seeks suitable maintaining unit with a maintenance decision-making process this equipment is keeped in repair.
The mode of this maintenance decision program mainly is to carry out suggestion through the relevant ability that maintaining unit had that database is set up.In database, can set up the time that maintenance service can be provided, place, route, equipment, personnel and the parts library storage of different maintaining units.At this moment, this central management unit is rubbed with the hands and suitable maintaining unit about the data based screening rule of maintaining unit according to database foundation.This screening rule can be according to priority picks and weight; For example: First Come First Served, SPT, long process time, earliest due date, least residue time, minimum operation number, minimum when floating, minimum when floating/the remaining deadline, crucial ratio, customer relationship or rule such as selection at random; Aforesaid rule is to belong to the rule that prior art is used always, does not give unnecessary details at this.
As shown in Figure 2, this figure is the schematic flow sheet that maintaining unit of the present invention keeps in repair.In the present embodiment, after the step 23 of Fig. 1 determines the unit of maintenance via this maintenance decision program, can maintenance requirements (including relevant informations such as plant component in need of maintenance kind, required manpower and time) be sent to maintaining unit.In step 24, after maintaining unit receives maintenance requirements, can prepare the required material of maintenance and arrange the maintenance stroke according to maintenance requirements.And then get the raw materials ready and arrange to keep in repair scheduling with step 25.Maintaining unit can have the supplier of different material and spare part; In step 24, receive the notice of maintenance requirements when maintaining unit after; Material and the spare part required according to maintenance requirements confirm whether material and spare part be enough, if not enough; Maintaining unit by in the step 25 to each supplier obtain must material and spare part, before accomplishing maintenance, prepare.In addition, maintaining unit also can be arranged needed maintenance manpower according to maintenance requirements, to accomplish the preparation of manpower.At last, carry out step 26 again and carry out maintenance activity.
See also shown in Figure 3ly, this figure is prediction of present device state analysis and resource allocation methods embodiment sketch map.In the present embodiment; One side is diagnosed for the situation of equipment and is predicted outside the life cycle; Can also be when equipment be keeped in repair, the use information of this equipment state is planned again and regulated, make that maintenance of equipment and use are able to carry out simultaneously.This method 3 is at first monitored the state of a mechanical parking equipment in real time with step 300.Then carry out step 301, detect and be arranged at the sensing signal that a long-range parking apparatus is produced.This sensing signal can be that lift in the mechanical parking equipment moves up and down rate signal, horizontal transfer rate signal, vehicle dimension and weight signal, stops the power supply state signal, personnel pass through sensing signal, temperature sensing signal, hydraulic system pressure signal, cable tension sensing signal, running noise frequency and amplitude signal or vibration signal.Then with step 302 this sensing signal being sent to a central management unit via network receives.Then carry out step 303, central management unit receives this sensing signal and this sensing signal is reached a diagnostic module to diagnose to produce a diagnostic message.The execution mode of step 303 is not given unnecessary details at this as the program of aforesaid step 21.In step 304, this diagnostic module is passed this diagnostic message back this central management unit to carry out follow-up judgement.
Then carry out step 305, this central management unit judges whether to predict the life cycle about the equipment or the assembly of this sensing signal according to this diagnostic message.If predict life cycle, then send the demand of prediction life cycle with step 306.In step 307, a life period forecasting module receives this demand then, promptly with step 308 prediction life cycle.The mode of prediction life cycle is not given unnecessary details at this as the program of abovementioned steps 22.This life cycle prediction module predicts the outcome to this central management unit reception with step 309 repayment after having predicted life cycle.
This central management unit receives after this predicts the outcome, and can judge whether and will keep in repair this equipment with the result of step 310 according to this prediction life cycle.If keep in repair, then send maintenance requirements to a maintenance arranging module with step 311.This maintenance arranging module receives the demand of maintenance in step 312 after, then seek suitable maintaining unit this equipment is keeped in repair with step 313 a maintenance decision-making process.This maintenance decision program mainly is to seek out suitable maintenance factory according to the data that database is set up, and its step is said as aforesaid step 23, does not give unnecessary details at this.Subsequently, the maintenance arranging module is passed to the processor that is arranged at maintenance factory with maintenance requirements through central management unit.This processor is the equipment that computer, server or work station etc. have the calculation process ability, but not as limit.
This processor receives in step 314 after the maintenance requirements that meeting is got the raw materials ready with step 315 and arranged to keep in repair scheduling.In this step, processor can be judged the quantity of needed material of maintenance requirements and spare part earlier, and maintenance personal's number.Then, confirm according to the material in the database and spare part inventory status and maintenance personal's stroke arrangement with step 316.Then send the notice that replenishes to the material or the spare part supplier of correspondence if find under the not enough situation of material or spare part with step 317.After the flow process of confirming and replenishing, accomplished the program that maintenance is prepared.Then carry out step 318, carry material and spare part keeps in repair to this mechanical parking instrument factory in the maintenance personal in time who arranges.In the process of maintenance; The central management server of mechanical parking instrument factory; Can also step 319 carry out the Parking Stall resource allocation management, separated with parking stall and the utilizable parking space information district that will keep in repair, let mechanical parking equipment under the state of maintenance, still keep normal operation.In addition, but also receiving remote terminating machine online, inquiry or the change scheduling arrangement of the processor of this maintaining unit.
See also shown in Figure 4, this figure be the present invention integrate the maintenance scheduling parking stall resource allocation schematic flow sheet.In this flow process, mainly can assign the parking stall according to the period.In the present embodiment, spike period or from the identification mode of peak period, mainly can use historical datas distinguish according to database 40 stored parking stalls.In addition, because general maintenance scheduling more can not be arranged at the period that spike uses the parking stall, therefore the central control computer in the parking lot can be assigned rule according to the parking stall and sent a car in peak hour.In case the parking stall is designated or use, then can central control computer change database 41 in to attribute that should the parking stall, and then be stored in the database 41.Terminal computer equipment can be connected to database and change the show state (room and the situation of using the parking stall) that uses the parking stall with the state in the storehouse of obtaining each parking stall in the parking apparatus in real time then.
If in the stage from the peak; When central control computer distributes the parking stall, can further confirm to have or not maintenance activity to carry out, if maintenance activity is arranged; Then can avoid assigning the parking stall to give the vehicle of warehouse-in then according to the maintenance scheduling plan of being write down in the database 42 in the parking stall of maintenance.Likewise, in case the parking stall is designated or use, then can central control computer change database 41 in to attribute that should the parking stall, and then be stored in the database.Terminal computer equipment can be connected to database to obtain the state in the storehouse of each parking stall in the parking apparatus in real time then.On the other hand, be arranged on monitoring module 43 in the parking lot and can utilize logic controller assemblies such as (PLC) to collect the pilot signal of each mechanism in the parking lot or parts, and be pooled in the database 41.And then, information is passed to long-range central management unit by central control computer.The follow-up disposal that central management unit is done after receiving sensing signal reaches as shown in the flow process of Fig. 1 and Fig. 3.
See also shown in Fig. 5 A, this figure is equipment state analyses and prediction of the present invention and resource allocation system sketch map.This system can carry out the flow process of aforesaid Fig. 1 and Fig. 3.In the present embodiment, this system includes: a database 50, a plurality of monitoring module 51 and a central management unit 52.Each monitoring module 51 connects with an equipment 53 is even mutually, and the state of these monitoring module 51 monitoring and this equipment 53 of detection is to produce a sensing signal.Equipment 53 in the present embodiment can be a mechanical parking equipment, but not as limit.Shown in Fig. 5 B, this figure is the mechanical parking position sketch map in the mechanical parking equipment of the present invention.Have a lot of assemblies in each mechanical parking position 532, for example: motor, chain or lifting assembly etc.Each assembly all is connected with transducer 533 and 534, and monitoring module 51 can capture the sensing signal that each transducer 533 and 534 is produced.Because monitoring module 51 is the state of the mechanical parking apparatus of long-time monitoring; Therefore in order to reduce data flow; The monitoring module 51 of present embodiment can be compared the sensing signal that receives with previous sensing signal; If change in a scope then to be regarded as signal identical, therefore just do not transmit toward central management unit 52.Otherwise,, then signal is transferred to this central management unit 52 by the internet if when signal changes.
Return shown in Fig. 5 A, this central management unit 52, it is connected with these a plurality of monitoring modules 51 and 50 telecommunications of this database, and this central management unit 52 receives this sensing signal and this sensing signal is recorded in this database 50.As shown in Figure 6, this figure is a central management unit internal module embodiment sketch map.In the present embodiment, have a computing judge module 520, a diagnostic module 521, a life period forecasting module 522 and a maintenance arranging module 523 in this central management unit 52.This diagnostic module 521, it is diagnosed to produce a diagnostic message according to this sensing signal and gives this computing judge module 520.Computing judge module 520 judges whether to carry out the life cycle prediction according to this diagnostic message, if carry out the life cycle prediction, then sends a signal and gives this life cycle prediction module 522.This life cycle prediction module 522 can judge doping the device that should sensing signal or the life cycle of assembly according to this sensing signal and before historical data, and this computing judge module 520 is given in repayment when receiving signal.
This computing judge module 520 is according to diagnostic message and predict the outcome and judged whether and will keep in repair, and will keep in repair then again this life cycle money preface is passed to this maintenance arranging module 523 if having.This keeps in repair arranging module 523, and its result and diagnostic message according to this prediction life cycle judges whether and will keep in repair this equipment, if keep in repair, then seeks suitable maintaining unit with a maintenance decision-making process this equipment is keeped in repair.Return shown in Fig. 5 A, this central control unit 52 also is connected with a plurality of maintaining units 54 interior server or computers, to receive the maintainability information of repaying with this server of management at any time.This maintainability information also include maintaining unit manpower, with distance, part inventory information or the emergency interconnection information of equipment.
And each maintaining unit 54 can corresponding at least one article supplier of 55.Each article supplier of 55 can provide different material and spare parts, dispatches for this maintaining unit.Because the relevant informations such as maintainability of each maintaining unit 54 all can be passed to the database 50 that this central management unit 52 is connected, so the maintenance arranging module 523 in the central management unit 52 can be calculated needed time of scheduling and manpower and determine suitable maintaining unit 54 according to the ability of each interior maintaining unit 54 of database 50.After determining suitable maintaining unit 54; This maintenance arranging module 523 can be given computing judge module 520 with the information report of required manpower, time and maintaining unit, and this computing judge module 520 can be passed to corresponding maintaining unit 54 with this information then.
Maintaining unit 54 is after receiving maintenance instructions; Has the inventory management module of getting the raw materials ready in the server that has in the maintaining unit 54; They can be according to the stored data of database in the server, and whether material and the spare part of judging the stock be enough in response to maintenance requirements.If not enough, then can list required material or spare part demand inventory information, and this information circle is reached corresponding supplier by network, by the supplier required materials and parts are provided.In addition, maintaining unit 54 also can be arranged suitable maintenance personal according to manpower and required time.In the middle control computer of mechanical parking equipment 53, also have a parking assignment module 531, can carry out the flow process of earlier figures 4,, can not carry out and hindered along with the scheduling of parking stall maintenance to let the running of mechanical parking ground.
Comprehensively above-mentioned; Equipment state analyses and prediction provided by the invention and resource allocation methods and system; Owing to have confluence analysis and MRP; Make the issuable problem of equipment be able to find in advance and arrange preventative maintenance scheduling in real time, and then improve the service life of equipment and guarantee equipment and personnel's safety.
Certainly; The present invention also can have other various embodiments; Under the situation that does not deviate from spirit of the present invention and essence thereof; Those of ordinary skill in the art work as can make various corresponding changes and distortion according to the present invention, but these corresponding changes and distortion all should belong to the protection range of the appended claim of the present invention.
Claims (29)
1. an equipment state analyzing and predicting method is characterized in that, includes the following step:
Detect a sensing signal that produces about a remote equipment and reach a central management unit via network, this remote equipment is a mechanical parking equipment;
Central management unit receives this sensing signal and diagnoses to produce a diagnostic message;
Central management unit is according to the life cycle of this diagnostic message prediction about this remote equipment; And
Central management unit judges whether and will keep in repair this remote equipment according to the result of this prediction life cycle, if keep in repair, then seeks suitable maintaining unit with a maintenance decision-making process this remote equipment is keeped in repair.
2. equipment state analyzing and predicting method according to claim 1; It is characterized in that, this sensing signal for the lift in this mechanical parking equipment moves up and down rate signal, horizontal transfer rate signal, vehicle dimension and weight signal, stops the power supply state signal, personnel pass through sensing signal, temperature sensing signal, hydraulic system pressure signal, cable tension sensing signal, running noise frequency and amplitude signal and vibration signal one of them.
3. equipment state analyzing and predicting method according to claim 1 is characterized in that, this maintenance decision program can also determine a maintenance scheduling suggestion.
4. equipment state analyzing and predicting method according to claim 3; It is characterized in that this maintenance scheduling suggestion is offered suggestions according to the information of maintenance service time, place, route, equipment, personnel and parts library storage that this suitable maintaining unit can provide.
5. equipment state analyzing and predicting method according to claim 1 is characterized in that, also includes the following step:
Maintaining unit receives maintenance requirements;
Prepare the required material of maintenance and arrange the maintenance stroke; And
Carry out maintenance activity.
6. equipment state analyzing and predicting method according to claim 1 is characterized in that, the method that produces this diagnostic message also includes the following step:
Record sensing signal each time is to form a historical information;
Summarize the caution value of one of this remote equipment according to historical information; And
When receiving new sense data, promptly compare to obtain this diagnostic message with this caution value.
7. equipment state analyzing and predicting method according to claim 1 is characterized in that, the method for prediction life cycle also includes the following step:
Record sensing signal each time is to form a historical information; And
When receiving new sense data, promptly perform calculations to obtain this prediction life cycle with this historical information.
8. equipment state analyzing and predicting method according to claim 7 is characterized in that, the mode of this calculation is chosen as bar row method, decision tree method, genetic algorithm or neural network method.
9. a parking apparatus state analysis is predicted and resource allocation methods, it is characterized in that, includes the following step:
Detect and be arranged at the sensing signal that a long-range parking apparatus is produced, and this sensing signal is sent to a central management unit via network;
Central management unit receives this sensing signal and diagnoses to produce a diagnostic message;
Central management unit is according to the life cycle of this diagnostic message prediction about this equipment;
Central management unit judges whether and will keep in repair this equipment according to the result of this prediction life cycle, if keep in repair, then seeks suitable maintaining unit with a maintenance decision-making process this equipment is keeped in repair; And
In the process that this maintenance is carried out, carry out the Parking Stall resource allocation management.
10. parking apparatus state analysis prediction according to claim 9 and resource allocation methods; It is characterized in that, this sensing signal for the lift in this parking apparatus moves up and down rate signal, horizontal transfer rate signal, vehicle dimension and weight signal, stops the power supply state signal, personnel pass through sensing signal, temperature sensing signal, hydraulic system pressure signal, cable tension sensing signal, running noise frequency and amplitude signal and vibration signal one of them.
11. parking apparatus state analysis prediction according to claim 9 and resource allocation methods is characterized in that, this maintenance decision program can also determine a maintenance scheduling suggestion.
12. parking apparatus state analysis prediction according to claim 11 and resource allocation methods; It is characterized in that this maintenance scheduling suggestion is offered suggestions according to the information of maintenance service time, place, route, equipment, personnel and parts library storage that these a plurality of maintaining units can provide.
13. parking apparatus state analysis prediction according to claim 9 and resource allocation methods is characterized in that, also include the following step:
Maintaining unit receives this maintenance requirements;
Prepare the required material of maintenance and arrange the maintenance scheduling; And
Carry out maintenance activity.
14. parking apparatus state analysis prediction according to claim 13 and resource allocation methods is characterized in that this Parking Stall resource allocation management also includes the following step:
Determine available parking stall according to this maintenance scheduling; And
Send according to this available parking stall and to put distribution.
15. parking apparatus state analysis prediction according to claim 9 and resource allocation methods is characterized in that the method that produces this diagnostic message also includes the following step:
Record sensing signal each time is to form a historical information;
Summarize the caution value of one of this remote equipment according to historical information; And
When receiving new sense data, promptly compare to obtain this diagnostic message with this caution value.
16. parking apparatus state analysis prediction according to claim 9 and resource allocation methods is characterized in that, the method for prediction life cycle also includes the following step:
Record sensing signal each time is to form a historical information; And
When receiving new sense data, promptly perform calculations to obtain this prediction life cycle with this historical information.
17. parking apparatus state analysis prediction according to claim 16 and resource allocation methods is characterized in that the mode of this calculation is chosen as bar row method, decision tree method, genetic algorithm or neural network method.
18. parking apparatus state analysis prediction according to claim 16 and resource allocation methods is characterized in that, this Parking Stall resource allocation management can carry out the parking stall distribution with cooperate the maintenance scheduling from peak time according to the use spike of this parking apparatus.
19. equipment state analyses and prediction and resource allocation system is characterized in that, comprising:
One database;
One monitoring module, it connects with an equipment mutually by chance, and the state of this monitoring module monitoring and this equipment of detection is to produce a sensing signal, and this equipment is a parking apparatus; And
One central management unit, it is connected with this monitoring module and this database telecommunication, and this central management unit receives this sensing signal and this sensing signal is recorded in this database, also has in this central management unit:
One diagnostic module, it is diagnosed to produce a diagnostic message according to this sensing signal;
One life period forecasting module, it is according to the life cycle of this diagnostic message prediction about this equipment; And
One maintenance arranging module, its result according to this prediction life cycle judge whether and will keep in repair this equipment, if keep in repair, then seek suitable maintaining unit with a maintenance decision-making process this equipment is keeped in repair.
20. equipment state analyses and prediction according to claim 19 and resource allocation system is characterized in that, this monitoring module by network with this sensing signal transmission to this central management unit.
21. equipment state analyses and prediction according to claim 19 and resource allocation system is characterized in that, also have the parking management module, rule is assigned on the parking stall in the decision parking apparatus.
22. equipment state analyses and prediction according to claim 19 and resource allocation system is characterized in that, this central management unit also with a plurality of maintaining units in server be connected, to receive the maintainability information with this server repayment of management at any time.
23. equipment state analyses and prediction according to claim 22 and resource allocation system is characterized in that, have the inventory management module of getting the raw materials ready in each server to carry out the management of material and spare part.
24. equipment state analyses and prediction according to claim 19 and resource allocation system is characterized in that, this maintainability information also include maintaining unit manpower, with distance, part inventory information or the emergency interconnection information of equipment.
25. equipment state analyses and prediction according to claim 19 and resource allocation system; It is characterized in that; It receives this diagnostic module, life cycle prediction module and the handled result of maintenance arranging module also to have a computing judge module in this central management unit, to judge.
26. equipment state analyses and prediction according to claim 19 and resource allocation system; It is characterized in that; This central management unit can write down each time sensing signal in this database forming a historical information, and summarize the caution value of one of this equipment according to historical information.
27. equipment state analyses and prediction according to claim 26 and resource allocation system is characterized in that, this diagnosis unit compares to obtain this diagnostic message according to received new sense data and this caution value.
28. equipment state analyses and prediction according to claim 19 and resource allocation system is characterized in that, this central management unit record sensing signal each time in this database to form a historical information.
29. equipment state analyses and prediction according to claim 28 and resource allocation system is characterized in that, this life cycle prediction module is to perform calculations with the life cycle of prediction about this equipment according to receiving new sense data and this historical information.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2008101851655A CN101753364B (en) | 2008-12-11 | 2008-12-11 | Equipment state analyzing and predicting and source distributing method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2008101851655A CN101753364B (en) | 2008-12-11 | 2008-12-11 | Equipment state analyzing and predicting and source distributing method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN101753364A CN101753364A (en) | 2010-06-23 |
| CN101753364B true CN101753364B (en) | 2012-08-22 |
Family
ID=42479803
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2008101851655A Active CN101753364B (en) | 2008-12-11 | 2008-12-11 | Equipment state analyzing and predicting and source distributing method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN101753364B (en) |
Families Citing this family (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102385724A (en) * | 2010-08-27 | 2012-03-21 | 上海财经大学 | Spare part assembling demand forecasting information processing method applied to inventory management |
| CN102592437A (en) * | 2011-01-17 | 2012-07-18 | 新光国际航太科技股份有限公司 | Vehicle scheduling system and scheduling method |
| CN103123697A (en) * | 2011-11-18 | 2013-05-29 | 丰联资讯股份有限公司 | Vehicle information integration platform |
| US9568911B2 (en) | 2012-11-30 | 2017-02-14 | Tennant Company | Dynamic maintenance scheduling system for surface cleaning machines |
| US9230208B2 (en) * | 2013-12-18 | 2016-01-05 | International Business Machines Corporation | Haptic-based artificial neural network training |
| CN107533733B (en) * | 2015-05-21 | 2018-12-28 | 三菱电机株式会社 | Long-Range Surveillance Unit and method, long-range monitoring maintenance system and recording medium |
| US10789335B2 (en) * | 2015-12-08 | 2020-09-29 | Dartpoint Tech. Co., Ltd. | Remote diagnosis management system and method for operating the same |
| CN108241343A (en) * | 2016-12-24 | 2018-07-03 | 青岛海尔模具有限公司 | A kind of intelligent plant management platform system |
| TWI632441B (en) * | 2017-01-20 | 2018-08-11 | 財團法人工業技術研究院 | Prognostics method and device for processing apparatus |
| CN109034381B (en) * | 2017-06-09 | 2022-01-28 | 宏达国际电子股份有限公司 | Training task optimization system, method and non-transitory computer readable medium thereof |
| CN107729405A (en) * | 2017-09-25 | 2018-02-23 | 中国航空工业集团公司西安飞机设计研究所 | A kind of calibration cycle generation method of ATE |
| CN108174400B (en) * | 2017-12-27 | 2020-12-22 | 瑞斯康达科技发展股份有限公司 | Data processing method, system and equipment of terminal equipment |
| CN108414526A (en) * | 2018-02-02 | 2018-08-17 | 江苏方建质量鉴定检测有限公司 | A kind of sloughing of exterior wall detecting system |
| CN109034192B (en) * | 2018-06-20 | 2022-03-08 | 西南交通大学 | Track-vehicle body vibration state prediction method based on deep learning |
| CN109443026B (en) * | 2018-10-31 | 2020-07-14 | 广东兴发铝业有限公司 | Oxidation furnace state prediction analysis system based on industrial big data |
| JP7156975B2 (en) * | 2019-02-26 | 2022-10-19 | 三菱重工業株式会社 | Management evaluation device, management evaluation method, and program |
| CN111681444A (en) * | 2020-05-12 | 2020-09-18 | 武汉恒鑫创赢科技有限公司 | Parking management method and device based on big data analysis |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5210704A (en) * | 1990-10-02 | 1993-05-11 | Technology International Incorporated | System for prognosis and diagnostics of failure and wearout monitoring and for prediction of life expectancy of helicopter gearboxes and other rotating equipment |
| US5402475A (en) * | 1993-03-31 | 1995-03-28 | Schlumberger Technologies, Inc. | Monitoring and control of parking management system by remote |
| US5432508A (en) * | 1992-09-17 | 1995-07-11 | Jackson; Wayne B. | Technique for facilitating and monitoring vehicle parking |
| US5445347A (en) * | 1993-05-13 | 1995-08-29 | Hughes Aircraft Company | Automated wireless preventive maintenance monitoring system for magnetic levitation (MAGLEV) trains and other vehicles |
-
2008
- 2008-12-11 CN CN2008101851655A patent/CN101753364B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5210704A (en) * | 1990-10-02 | 1993-05-11 | Technology International Incorporated | System for prognosis and diagnostics of failure and wearout monitoring and for prediction of life expectancy of helicopter gearboxes and other rotating equipment |
| US5432508A (en) * | 1992-09-17 | 1995-07-11 | Jackson; Wayne B. | Technique for facilitating and monitoring vehicle parking |
| US5402475A (en) * | 1993-03-31 | 1995-03-28 | Schlumberger Technologies, Inc. | Monitoring and control of parking management system by remote |
| US5445347A (en) * | 1993-05-13 | 1995-08-29 | Hughes Aircraft Company | Automated wireless preventive maintenance monitoring system for magnetic levitation (MAGLEV) trains and other vehicles |
Also Published As
| Publication number | Publication date |
|---|---|
| CN101753364A (en) | 2010-06-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101753364B (en) | Equipment state analyzing and predicting and source distributing method and system | |
| Basri et al. | Preventive maintenance (PM) planning: a review | |
| Luxhøj et al. | Trends and perspectives in industrial maintenance management | |
| US9760068B2 (en) | Predictive maintenance method and system | |
| US7496475B2 (en) | Maintenance management of a machine | |
| Jin et al. | Present status and future growth of advanced maintenance technology and strategy in US manufacturing | |
| US20020161600A1 (en) | Technical support program | |
| Tu et al. | An integrated maintenance management system for an advanced manufacturing company | |
| CN103098044A (en) | Cycle decomposition analysis for remote machine monitoring | |
| CN116862199A (en) | Building construction optimizing system based on big data and cloud computing | |
| KR101278428B1 (en) | Real-time collaborated enterprise asset management system based on condition-based maintenance and method thereof | |
| KR20230062463A (en) | AI IoT-based Elevator Predictive Maintenance System Project | |
| Ramesh et al. | Implementation of predictive maintenance systems in remotely located process plants under industry 4.0 scenario | |
| TWI380226B (en) | System and method of prognostic analysis and resource planning for apparatu | |
| zu Wickern | Challenges and reliability of predictive maintenance | |
| Einabadi et al. | Dynamic predictive and preventive maintenance planning with failure risk and opportunistic grouping considerations: A case study in the automotive industry | |
| KR101853485B1 (en) | Condition based maintenance system for lng fpso | |
| JP3213929U (en) | Offshore plant preventive maintenance system | |
| Nwadinobi et al. | Review of maintenance scheduling and optimization models | |
| Ge et al. | Improving periodic maintenance performance: a grouping and heuristic approach | |
| Ip et al. | SCADA in an integrated maintenance management system | |
| KR20200072069A (en) | Robot state information providing system based on motor information | |
| Singh et al. | Advanced Maintenance and Reliability | |
| Pradhan et al. | Trends and perspectives in industrial maintenance management | |
| Jayasuriya | Intelligent maintenance management model for critical machines in a solid tire manufacturing factory |
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 |