CN107066683A - A kind of Cnc ReliabilityintelligeNetwork Network model modelling approach and system based on energy consumption characters - Google Patents
A kind of Cnc ReliabilityintelligeNetwork Network model modelling approach and system based on energy consumption characters Download PDFInfo
- Publication number
- CN107066683A CN107066683A CN201710080893.9A CN201710080893A CN107066683A CN 107066683 A CN107066683 A CN 107066683A CN 201710080893 A CN201710080893 A CN 201710080893A CN 107066683 A CN107066683 A CN 107066683A
- Authority
- CN
- China
- Prior art keywords
- power
- time
- network model
- energy consumption
- data
- 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
- 238000005265 energy consumption Methods 0.000 title claims abstract description 20
- 238000013459 approach Methods 0.000 title claims abstract description 13
- 239000012634 fragment Substances 0.000 claims abstract description 25
- 238000000034 method Methods 0.000 claims abstract description 18
- 238000003066 decision tree Methods 0.000 claims abstract description 8
- 230000011218 segmentation Effects 0.000 claims abstract description 6
- 238000012546 transfer Methods 0.000 claims abstract description 6
- 238000012549 training Methods 0.000 claims abstract description 5
- 238000004364 calculation method Methods 0.000 claims description 11
- 230000008569 process Effects 0.000 claims description 8
- 238000004458 analytical method Methods 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 5
- 230000008859 change Effects 0.000 claims description 4
- 238000005520 cutting process Methods 0.000 claims description 4
- 238000000605 extraction Methods 0.000 claims description 3
- 230000007257 malfunction Effects 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 230000007704 transition Effects 0.000 description 3
- 238000007514 turning Methods 0.000 description 3
- 238000013480 data collection Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000003754 machining Methods 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000006855 networking Effects 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 230000000737 periodic effect Effects 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- PEDCQBHIVMGVHV-UHFFFAOYSA-N Glycerine Chemical compound OCC(O)CO PEDCQBHIVMGVHV-UHFFFAOYSA-N 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000010304 firing Methods 0.000 description 1
- 230000002045 lasting effect Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000012856 packing Methods 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/30—Circuit design
- G06F30/36—Circuit design at the analogue level
- G06F30/367—Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
-
- 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
- G06F2119/06—Power analysis or power optimisation
Landscapes
- Engineering & Computer Science (AREA)
- Computer Hardware Design (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Microelectronics & Electronic Packaging (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- General Factory Administration (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Numerical Control (AREA)
Abstract
The invention discloses a kind of Cnc ReliabilityintelligeNetwork Network model modelling approach based on energy consumption characters, including step：Power data is gathered, power difference sequence is calculated；Segmentation power difference sequence obtains power fragment；Extract feature database；First string representation conditions of machine tool transfer path；Power data, feature based storehouse training CART decision trees, with decision tree to power segment classification are chosen from power fragment；Sorted power fragment is arranged according to acquisition time order, the second string representation is used；The first character string and the second character string are matched, power sequence is obtained, process-cycle duration is calculated；Time slip-window and threshold value are set, and statistics calculates process-cycle, working time and fault time；Set up model.The invention also discloses the Cnc ReliabilityintelligeNetwork Network model modeling system based on energy consumption characters.The inventive method has higher accuracy, can avoid human error, data not in time, the problem of taking time and effort.
Description
Technical field
The present invention relates to a kind of reliability model modeling method and system, more particularly to a kind of number based on energy consumption characters
Control lathe reliability model modeling method and system.
Background technology
When the reliability model of Digit Control Machine Tool is process-cycle and working time and the failure for describing Digit Control Machine Tool
Between probability density function.The reliability model of Digit Control Machine Tool is management, production line Performance Evaluation and the property to Digit Control Machine Tool
The basis that can optimize.In general, before lathe dispatches from the factory, manufacturer can provide the reference parameter of the lathe in production explanation.To making
For user, staff's early stage provides the nominal value of parameter by modes such as records.But it is due to that machine maintenance personnel can be right
Digit Control Machine Tool is adjusted and is negligent of record, and these adjustment are likely to produce influence to parameter.In the course of time, these change day
The product moon is tired to have ultimately resulted in the situation that nominal value is not inconsistent with actual value.Accordingly, it would be desirable to carry out carefully measurement to them so that ensure can
By the precision of property model.The measurement of these current parameters mostly manually regularly counts to complete, this artificial periodic statistical
Method cause data feedback not in time, easily error and the problems such as labor intensive resource.
The content of the invention
, can it is an object of the invention to provide a kind of Digit Control Machine Tool based on energy consumption characters for above-mentioned the deficiencies in the prior art
By property model modelling approach.It is a further object to provide a kind of Cnc ReliabilityintelligeNetwork Network model based on energy consumption characters
Modeling.
The technical scheme is that such：A kind of Cnc ReliabilityintelligeNetwork Network model modeling side based on energy consumption characters
Method, comprises the following steps,
S01, the power data for gathering lathe, calculate power difference sequence；
S02, by power difference sequence segmentation obtain the power fragment based on conditions of machine tool；
S03, the feature database of its power characteristic can be characterized to each one group of power snippet extraction, the feature database is included and machine
The different running status correlations of bed and some features changed with the change of state；
S04, with the corresponding conditions of machine tool transfer path of the procedure of the first string representation one；
S05, all power fragments are selected to power data in proportion carry out status indicator, based on feature database training
CART decision trees, the classification by the decision tree after training to power fragment；
It S06, will be arranged by the sorted power fragments of step S05 according to acquisition time order, and and use the second character string
Represent；
S07, using the character string of Knuth-Morris-Pratt algorithmic match first and the second character string, obtain power data
Power sequence during middle processing, subtracts initial time by the end time of power sequence when processing and obtains process-cycle duration；
S08, one time slip-window of setting, count process-cycle quantity in the time window, and during all process-cycles
Long, the arithmetic average for calculating all process-cycle durations in the time window is used as the process-cycle of work at present day；It is described to slide
Time window is moved rearwards by a working day, obtains next workaday process-cycle；
S09, given threshold, think working condition more than threshold value by lathe operation time power data, lathe are worked
Moment power data thinks malfunction less than threshold value, and stream time and its quantity are counted in the time slip-window
And calculate arithmetic average, statistics fault time and its quantity and calculate arithmetic mean；The time slip-window is moved rearwards by one
On working day, obtain next workaday working time arithmetic mean and fault time arithmetic mean；
S10, the different operating day for obtaining step S08 process-cycle, the work for the different operating day that step S09 is obtained
Time arithmetic mean and fault time arithmetic mean are used as lathe reliability model parameter.
Further, the conditions of machine tool is divided into standby, startup, four states of zero load and cutting.
Further, the calculating power difference sequence is to use P={ Pi, i=1,2 ..., n } and represent power data, n tables
Show the quantity of power collecting point, PiRepresent i-th of power collecting point, power difference sequence D={ Di, i=1,2 ..., n } table
Show, Di=Pi+1-Pi。
Further, when splitting power difference sequence in the step S02, rule of thumb Bayes's threshold method determines lathe
Power data critical point when state is shifted, the cut-point of power data critical point position power difference sequence.
Further, feature described in the step S03 includes maximum, minimum value, mean absolute deviation, geometric average
With three rank autoregressive coefficients.
Further, the maximum value calculation formula is pmax=max (pi, i=1,2 ..., m), minimum value calculation formula
For pmin=min (pi, i=1,2 ..., m), mean absolute deviation calculation formula is pmad=mediani(|pi-medianj(pj)
|), geometric average calculation formula isThree rank autoregressive coefficient calculation formula areM is power data collection number, p in power fragmentiI-th of power collecting point is represented, y is the work(
The geometric average of rate fragment, ω is intercept, βtIt is regression coefficient,It is noise parameter.
A kind of Cnc ReliabilityintelligeNetwork Network model modeling system based on energy consumption characters, including on-site data gathering module：Adopt
Collect machine power data and pack and upload onto the server；Base module, the spy for setting up lathe running power characteristic
Levy the conditions of machine tool transfer path of storehouse and process；Analysis module, carry out power difference sequence segmentation, the classification of power fragment with
And the calculating of lathe reliability model parameter；Setup module, sets time slip-window and threshold value.
Further, the on-site data gathering module includes some intelligent electric meters composition RS-485 networks.
The advantage of technical scheme provided by the present invention is, machine is realized to machine power data acquisition using intelligent electric meter
The automatic calculating of bed reliability model parameter is handled compared to artificial periodic statistical, and accuracy is higher, can avoid human error, number
According to not in time, the problem of taking time and effort.
Brief description of the drawings
Fig. 1 is the Cnc ReliabilityintelligeNetwork Network model modeling system construction drawing based on energy consumption characters.
Cnc ReliabilityintelligeNetwork Network model modelling approach flow charts of the Fig. 2 based on energy consumption characters.
Fig. 3 is that power when Digit Control Machine Tool processes certain process changes over time curve.
Fig. 4 is the state recognition result of embodiment Digit Control Machine Tool turning process.
Embodiment
With reference to embodiment, the invention will be further described, but not as a limitation of the invention.
Fig. 1 is referred to, the Cnc ReliabilityintelligeNetwork Network model modeling system based on energy consumption characters includes：On-site data gathering mould
Block：Every Digit Control Machine Tool configures an intelligent electric meter, and power data is gathered with certain frequency, and power data converges in data acquisition unit
Always, during then packing uploads onto the server, connected between intelligent electric meter and data acquisition unit using RS485 buses；Knowledge base mould
Block, for setting up the feature database of lathe running power characteristic and the conditions of machine tool transfer path of process；Analysis module, enters
The segmentation of row power difference sequence, the classification of power fragment and the calculating of lathe reliability model parameter；Setup module, sets and slides
Dynamic time window and threshold value.Can be networked three kinds of sides between data acquisition unit and server using wired networking, Wireless Networking and GPRS
Formula.Between server and analysis module, base module and setup module by the way of wired connection.
Incorporated by reference to Fig. 2, by taking a numerically controlled lathe as an example, the Cnc ReliabilityintelligeNetwork Network model modelling approach based on energy consumption characters
It is as follows：
S01, the power data of on-site data gathering module collection lathe are uploaded onto the server, and analysis module is carried from server
Power data is taken to calculate power difference sequence：With P={ Pi, i=1,2 ..., n } and power data is represented, n represents power collecting point
Quantity, PiRepresent i-th of power collecting point, power difference sequence D={ Di, i=1,2 ..., n } represent, Di=Pi+1-Pi。
S02, power difference sequence pass through document " Empirical Bayes Selection of as observation
The Empirical Bayes threshold method that Wavelet Thresholds.Annals of Statistics " (2005) are proposed determines lathe
Power data critical point when state is shifted, so as to split the power fragment obtained based on conditions of machine tool.
S03, base module set up lathe running power features storehouse.According to document " Multi-objective
teaching–learning-based optimization algorithm for reducing carbon emissions
And operation time in turning operations.Engineering Optimization " (2015), by number
Control lathe can be divided into four states such as standby, startup, unloaded and cutting.Lathe can be divided into standby, startup, unloaded and cutting
Etc. state.According to the characteristic of lathe running power, running statuses correlations different from lathe are extracted and with the change of state
And the multiple features changed, form power features storehouse.The feature of its power characteristic can be characterized to every piece of one group of power snippet extraction
Storehouse, including maximum (pmax), minimum value (pmin), mean absolute deviation (pmad), geometric average (pgm), three rank autoregressive coefficients
(β1,β2,β3) etc. seven features.Common m collection point in the power fragment, each collection point pi(i=1,2 ..., m), feature
Calculation formula it is as follows：
pmax=max (pi, i=1,2 ..., m)
pmin=min (pi, i=1,2 ..., m)
pmad=mediani(|pi-medianj(pj)|)
M is power data collection number, p in power fragmentiI-th of power collecting point is represented, y is the several of the power fragment
What is average, and ω is intercept, βtIt is regression coefficient,It is noise parameter.
S04, the processing sequence by process, can obtain its state transition path.Fig. 3 gives the machine tooling one
Power during procedure changes over time curve, its state transition path for it is standby->Start->Unloaded->Cutting->It is unloaded.Four
Individual machining state is indicated with a character respectively, as shown in table 1.Signable state transition path then in Fig. 3 is the first character
String R：“ITACA”
Table 1, machining state and character corresponding table
| State | Character |
| It is standby | I |
| Start | T |
| It is unloaded | A |
| Cutting | C |
S05, all power fragments are selected to certain proportion carry out status indicator, CART decision trees are trained, after training
Decision tree be used for power fragment classification.Fig. 4 provides the state recognition result of certain turning process.
S06, by sorted power fragment according to acquisition time order arrange, with the second character string CpSign.
S07, using Knuth-Morris-Pratt algorithmic match character strings R and Cp, CpThe substring of middle matching is to add
The power sequence in man-hour.The end time of power sequence when processing is subtracted into initial time can obtain the process-cycle.
S08 and S09, setup module, set Cnc ReliabilityintelligeNetwork Network Parameters in Mathematical Model time slip-window length and
Threshold value a.Time slip-window is extracted from setup module, up to the termination time since the initial time of time window, if with R
Match somebody with somebody, then counter values add 1, while the duration for recording the substring is a process-cycle.In this way, can be somebody's turn to do
Quantity (uses n the process-cycle in time window1Represent), and all process-cycle duration τi(i=1,2 ..., n1).Calculate the time
The arithmetic average of all process-cycles in window, as the process-cycle of work at present day.
Similarly, time slip-window is moved rearwards by a working day, it is possible to obtain next workaday process-cycle.
Designated firing duration and repair time：Give a threshold value a, when certain normal work moment power data be more than a, then
Think that lathe is in running order；When power data is less than a, then it is assumed that lathe is in malfunction.Give a sliding time
Window, counts stream time t in the time windowup,iAnd quantity nup, also fault time tdown,iAnd quantity ndown.Meter
Calculate the arithmetic average of working time and fault time in the time window.Similarly, time slip-window is moved rearwards by a working day, and
Next workaday t can be obtainedup, tdown。
S10, the reliability model relevant parameter that can obtain by above step the Digit Control Machine Tool are as shown in table 2.From table
It can be seen that, in less time dimension, the process-cycle is held essentially constant, but average operation time and mean down time are then
In the presence of certain fluctuation.As can be seen here, the industrial plant Cnc ReliabilityintelligeNetwork Network model modelling approach that the present invention is described is to mould
The lasting monitoring of the relevant parameter of type and analyze meaningful to industrial plant production line Performance Evaluation.
Table 2, Cnc ReliabilityintelligeNetwork Network Parameters in Mathematical Model
| Relevant parameter | 1st day | 2nd day | 3rd day | 4th day | 5th day |
| τ(s) | 72 | 73 | 72 | 72 | 72 |
| tup(h) | 38.4 | 39.1 | 40.5 | 39.7 | 41.3 |
| tdown(h) | 4.8 | 4.3 | 4.9 | 5.2 | 4.8 |
Claims (8)
1. a kind of Cnc ReliabilityintelligeNetwork Network model modelling approach based on energy consumption characters, it is characterised in that：Comprise the following steps,
S01, the power data for gathering lathe, calculate power difference sequence；
S02, by power difference sequence segmentation obtain the power fragment based on conditions of machine tool；
S03, the feature database of its power characteristic can be characterized to each one group of power snippet extraction, the feature database is included with lathe not
With some features that running status is related and changes with the change of state；
S04, with the corresponding conditions of machine tool transfer path of the procedure of the first string representation one；
S05, all power fragments are selected to power data in proportion carry out status indicator, CART is trained based on the feature database
Decision tree, the classification by the decision tree after training to power fragment；
It S06, will be arranged by the sorted power fragments of step S05 according to acquisition time order, and and use the second string representation；
S07, using the character string of Knuth-Morris-Pratt algorithmic match first and the second character string, obtain in power data plus
The power sequence in man-hour, subtracts initial time by the end time of power sequence when processing and obtains process-cycle duration；
S08, one time slip-window of setting, count process-cycle quantity in the time window, and all process-cycle durations, count
The arithmetic average for calculating all process-cycle durations in the time window is used as the process-cycle of work at present day；The time slip-window
A working day is moved rearwards by, next workaday process-cycle is obtained；
S09, given threshold, think working condition, by lathe operation time by lathe operation time power data more than threshold value
Power data thinks malfunction less than threshold value, and stream time and its quantity are counted in the time slip-window and is counted
Calculate arithmetic average, statistics fault time and its quantity and calculate arithmetic mean；The time slip-window is moved rearwards by a job
Day, obtain next workaday working time arithmetic mean and fault time arithmetic mean；
S10, the different operating day for obtaining step S08 process-cycle, the working time for the different operating day that step S09 is obtained
Arithmetic mean and fault time arithmetic mean are used as lathe reliability model parameter.
2. the Cnc ReliabilityintelligeNetwork Network model modelling approach according to claim 1 based on energy consumption characters, it is characterised in that
The conditions of machine tool is divided into standby, startup, unloaded and four states of cutting.
3. the Cnc ReliabilityintelligeNetwork Network model modelling approach according to claim 1 based on energy consumption characters, it is characterised in that
The calculating power difference sequence is to use P={ Pi, i=1,2 ..., n } and power data is represented, n represents the number of power collecting point
Amount, PiRepresent i-th of power collecting point, power difference sequence D={ Di, i=1,2 ..., n } represent, Di=Pi+1-Pi。
4. the Cnc ReliabilityintelligeNetwork Network model modelling approach according to claim 1 based on energy consumption characters, it is characterised in that
When splitting power difference sequence in the step S02, rule of thumb Bayes's threshold method determines power when conditions of machine tool is shifted
Data critical point, the power data critical point is the cut-point of power difference sequence.
5. the Cnc ReliabilityintelligeNetwork Network model modelling approach according to claim 1 based on energy consumption characters, it is characterised in that
Feature described in the step S03 includes maximum, minimum value, mean absolute deviation, geometric average and three rank autoregressive coefficients.
6. the Cnc ReliabilityintelligeNetwork Network model modelling approach according to claim 1 based on energy consumption characters, it is characterised in that
The maximum value calculation formula is pmax=max (pi, i=1,2 ..., m), minimum value calculation formula is pmin=min (pi, i=1,
2 ..., m), mean absolute deviation calculation formula is pmad=mediani(|pi-medianj(pj) |), geometric average calculation formula
ForThree rank autoregressive coefficient calculation formula areM is in power fragment
Power data gathers number, piI-th of power collecting point is represented, y is the geometric average of the power fragment data, and ω is intercept, βt
It is regression coefficient,It is noise parameter.
7. a kind of Cnc ReliabilityintelligeNetwork Network model modeling system based on energy consumption characters, it is characterised in that adopted including field data
Collect module：Gather lathe power data and pack and upload onto the server；Base module, for setting up lathe running power
The feature database of characteristic and the conditions of machine tool transfer path of process；Analysis module, carries out the segmentation of power difference sequence, power fragment
Classification and lathe reliability model parameter calculating；Setup module, sets time slip-window and threshold value.
8. Cnc ReliabilityintelligeNetwork Network model modeling system according to claim 7, it is characterised in that the field data is adopted
Collecting module includes some intelligent electric meters composition RS-485 networks.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710080893.9A CN107066683B (en) | 2017-02-15 | 2017-02-15 | Numerical control machine tool reliability model modeling method and system based on energy consumption characteristics |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710080893.9A CN107066683B (en) | 2017-02-15 | 2017-02-15 | Numerical control machine tool reliability model modeling method and system based on energy consumption characteristics |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107066683A true CN107066683A (en) | 2017-08-18 |
| CN107066683B CN107066683B (en) | 2020-06-16 |
Family
ID=59599277
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710080893.9A Expired - Fee Related CN107066683B (en) | 2017-02-15 | 2017-02-15 | Numerical control machine tool reliability model modeling method and system based on energy consumption characteristics |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107066683B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115661919A (en) * | 2022-09-26 | 2023-01-31 | 珠海视熙科技有限公司 | Repeated action cycle statistical method and device, fitness equipment and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103235555A (en) * | 2013-04-18 | 2013-08-07 | 重庆大学 | On-line machine tool equipment utilization state monitoring method and device based on power information |
| KR101329996B1 (en) * | 2012-08-08 | 2013-11-15 | 한밭대학교 산학협력단 | Manufacturing information extraction method and equipment, machine tool monitoring method and system using the same |
| CN104794492A (en) * | 2015-04-27 | 2015-07-22 | 重庆大学 | Online machine tool equipment machining and running state recognizing method based on power feature models |
| CN104808587A (en) * | 2015-04-27 | 2015-07-29 | 重庆大学 | Utilization statistical approach based on operation states of machining equipment |
-
2017
- 2017-02-15 CN CN201710080893.9A patent/CN107066683B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101329996B1 (en) * | 2012-08-08 | 2013-11-15 | 한밭대학교 산학협력단 | Manufacturing information extraction method and equipment, machine tool monitoring method and system using the same |
| CN103235555A (en) * | 2013-04-18 | 2013-08-07 | 重庆大学 | On-line machine tool equipment utilization state monitoring method and device based on power information |
| CN104794492A (en) * | 2015-04-27 | 2015-07-22 | 重庆大学 | Online machine tool equipment machining and running state recognizing method based on power feature models |
| CN104808587A (en) * | 2015-04-27 | 2015-07-29 | 重庆大学 | Utilization statistical approach based on operation states of machining equipment |
Non-Patent Citations (5)
| Title |
|---|
| JIHONG PANG ET.AL: ""ELECTRE I Decision Model of Reliability Design Scheme for Computer Numerical Control Machine"", 《JOURNAL OF SOFTWARE》 * |
| 张勇 等: ""一种新的信号控制干道行程时间实时估计模型"", 《自动化学报》 * |
| 张根保 等: ""数控机床可靠性概述"", 《制造技术与机床》 * |
| 熊尧: ""面向重型数控机床的服役可靠性评估方法及增长技术研究"", 《中国博士学位论文全文数据库工程科技辑》 * |
| 赵敏: ""数控机床智能化状态监测与故障诊断系统"", 《中国优秀硕士学位论文全文数据库工程科技辑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115661919A (en) * | 2022-09-26 | 2023-01-31 | 珠海视熙科技有限公司 | Repeated action cycle statistical method and device, fitness equipment and storage medium |
| CN115661919B (en) * | 2022-09-26 | 2023-08-29 | 珠海视熙科技有限公司 | Repeated action period statistics method and device, body-building equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107066683B (en) | 2020-06-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109674080B (en) | Tobacco leaf conditioning water adding amount prediction method, storage medium and terminal equipment | |
| TWI543102B (en) | Method and system of cause analysis and correction for manufacturing data | |
| CN111475921A (en) | Tool residual life prediction method based on edge calculation and L STM network | |
| CN111680820B (en) | Distributed photovoltaic power station fault diagnosis method and device | |
| CN107220734A (en) | CNC Lathe Turning process Energy Consumption Prediction System based on decision tree | |
| CN104808587B (en) | A kind of mobility statistical method based on machining apparatus running status | |
| CN107516135A (en) | A kind of automation monitoring learning method for supporting multi-source data | |
| CN110531722B (en) | Process parameter recommendation system and method based on data acquisition | |
| CN110109431B (en) | Intelligent acquiring system for OEE information of die casting machine | |
| CN114418177B (en) | New product material distribution prediction method based on digital twin workshops for generating countermeasure network | |
| CN108198268B (en) | Production equipment data calibration method | |
| CN116186624A (en) | Boiler assessment method and system based on artificial intelligence | |
| CN114253242B (en) | VPN-based cloud equipment data acquisition system for Internet of things | |
| CN117849700B (en) | Modular electric energy metering system capable of controlling measurement | |
| CN107066683A (en) | A kind of Cnc ReliabilityintelligeNetwork Network model modelling approach and system based on energy consumption characters | |
| CN104834787B (en) | Perception and analysis method for energy consumption of bearing grinding machine production workshop | |
| CN108563201B (en) | DMAIC-driven small-batch part machining process quality improvement method | |
| CN117559443A (en) | Ordered power utilization control method for large industrial user cluster under peak load | |
| CN111724203B (en) | Product processing price estimation method and system based on multiple regression model | |
| CN103268329B (en) | Plasma panel manufacturing process data digging system | |
| CN116562599A (en) | Efficient intelligent factory scheduling system based on deep learning | |
| CN115392663A (en) | Data acquisition and processing method based on big data | |
| CN116360339A (en) | Numerical control machine tool reliability optimization method based on digital twin model of meta-action unit | |
| CN106959668A (en) | A kind of mechanical production devices cutting state identification and data processing method method | |
| CN110083804B (en) | Wind power plant SCADA data missing intelligent repairing method based on condition distribution regression |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20190107 Address after: 210000 2nd Floor, Block C, Gupinggang 4, Gulou District, Nanjing City, Jiangsu Province Applicant after: Nanjing Sichen Information Technology Co.,Ltd. Address before: 215500 Tongji Science and Technology Plaza, 333 Hushan Road, Changshu High-tech Industrial Development Zone, Suzhou City, Jiangsu Province, 308 Applicant before: SUZHOU WENHAI IOT TECHNOLOGY Co.,Ltd. |
|
| TA01 | Transfer of patent application right | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20200619 Address after: Room 1428, unit 1, unit 1, building 10 (original 3), Guanggu Chuangye street, Dongxin Road, Donghu New Technology Development Zone, Wuhan, Hubei Province 430070 Patentee after: Wuhan Mouse Technology Co.,Ltd. Address before: 210000 2nd Floor, Block C, Gupinggang 4, Gulou District, Nanjing City, Jiangsu Province Patentee before: Nanjing Sichen Information Technology Co.,Ltd. |
|
| TR01 | Transfer of patent right | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20200616 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |