CN103792087B - Test run Fault monitoring and diagnosis method in parallel - Google Patents
Test run Fault monitoring and diagnosis method in parallel Download PDFInfo
- Publication number
- CN103792087B CN103792087B CN201410035097.XA CN201410035097A CN103792087B CN 103792087 B CN103792087 B CN 103792087B CN 201410035097 A CN201410035097 A CN 201410035097A CN 103792087 B CN103792087 B CN 103792087B
- Authority
- CN
- China
- Prior art keywords
- parameter
- rule base
- engine
- test run
- diagnosis
- 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
Landscapes
- Testing And Monitoring For Control Systems (AREA)
Abstract
A kind of test run Fault monitoring and diagnosis method in parallel of the present invention, comprise 1] actual value getparms, 2] parameter grouping, 3] parameter detecting, 4] sensor failure detects, 5] step such as abnormal parameters judgement, solve "or" relation between four engines, the problem of "AND" relation between separate unit engine parameters, improve the precision of Fault monitoring and diagnosis system algorithm, phenomenon is failed to report in the wrong report decreased due to the generation of the reason such as algorithm, sensor failure, can carry out monitoring and diagnosis to the different parameters of different engine simultaneously.
Description
Technical field
The present invention relates to a kind of Fault monitoring and diagnosis technology, be specifically related to a kind of test run Fault monitoring and diagnosis method in parallel.
Background technology
Space mission all will carry out various test before launching each time; ground level heat test run test simulation state of flight can directly obtain various parameter and performance; decide quality even success or failure when rocket body is launched; be necessary to introduce and adopt Fault monitoring and diagnosis technology to reduce its empirical risk, protection product and test bay safety.Oxygen kerosene engine failure monitoring and fault diagnosis system designs for separate unit engine, there is following problem: first, does not possess the ability of simultaneously monitoring four engine operating states; Secondly, when wherein one or a few break down time can accurately locate; Finally, must ensure false alarm can not occur or do not report to the police for the test run in parallel of four machines.Oxygen kerosene engine four machine test run Fault monitoring and diagnosis in parallel technology is domestic Fault monitoring and diagnosis technology being applied to first in the heat run in parallel of four engines.
Summary of the invention
For solving the current technical matters not being suitable for multiple stage engine failure monitoring and diagnosis in parallel for separate unit breakdown in the motor monitoring and diagnosis method, the invention provides a kind of test run Fault monitoring and diagnosis method in parallel.
Technical solution of the present invention is as follows:
A kind of test run Fault monitoring and diagnosis method in parallel, its special character is: comprise the following steps:
1] actual value getparms
Obtain the digital quantity value of the malfunction monitoring point parameter required for Fault monitoring and diagnosis system, and digital quantity value be synchronously converted to analog quantity, i.e. the actual value of malfunction monitoring point parameter,
The digital quantity value of described malfunction monitoring point parameter by being arranged on the sensor collection carried out on multiple engines of test run, and obtains after data processing,
2] parameter grouping
Parameter is classified according to engine, the parameter of same engine is divided into one group, the different parameters of same engine is classified by the attribute information according to parameter, and concludes under having the rule base of same alike result with this parameter, and the rule base of different engine arranges not identical; Described rule base comprises multi-class data Ku Biaoge, every class database table for storing the threshold value calculation method of a class parameter in different time points,
3] parameter detecting
Parallel detection step 1] multiple breakdown in the motor monitoring points parameter of carrying out test run of obtaining, concrete detecting step is as follows:
Identification parameter attribute, the database table of the rule base corresponded is inquired about according to parameter attribute, the threshold value calculation method recorded in database table is utilized to calculate the threshold value of this parameter at this time point, by the actual value of this parameter compared with threshold value, if the actual value of parameter is in threshold range, then this parameter running status is normal, if the actual value of parameter exceeds threshold range, then carry out step 4]
4] sensor failure detects
Whether detecting sensor is in open circuit, short circuit, or the abnormality of numerical value oscillation on large scale, if so, this parameter is rejected, and is not re-used as malfunction monitoring point parameter; Otherwise, carry out next step;
5] abnormal parameters judges
If the actual value continuous several times of malfunction monitoring point parameter exceeds threshold range, then this abnormal parameters, carries out early warning.
In step 5] after also comprise: 6] breakdown judge, when an engine has the actual value continuous several times of multiple malfunction monitoring point parameter to exceed threshold range simultaneously, confirm that this test run is broken down and reports to the police.
The threshold value calculation method that in above-mentioned rule base, database table stores comprises two kinds, and one is ATA algorithm, another kind of red line shutdown algorithm,
At engine startup, adopt red line shutdown algorithm, after engine enters stable section, adopt ATA algorithm.
In step 1] actual value getparms time, the digital quantity value of malfunction monitoring point parameter needs carry out data backup by data acquisition backup computer and be sent to Fault monitoring and diagnosis computing machine, after actual value getparms, by Fault monitoring and diagnosis computing machine completing steps 1] to step 5].
For improving reliability of the present invention, in step 1] before, also comprise the step whether judgment rule storehouse is correct, concrete grammar is as follows:
5.1] according to same model engine test run True Data in the past, it is modified and makes emulated data file, malfunction monitoring point parameter required when described emulated data file comprises malfunction monitoring and judges, malfunction monitoring point parameter can be arranged to normal, abnormal two states
5.2] the malfunction monitoring point parameter in emulated data file is read,
5.3] parameter grouping
Same engine different parameters is classified by the attribute information according to parameter, and concludes under the rule base with same alike result, and the rule base of different engine arranges not identical; Described rule base comprises multi-class data Ku Biaoge, every class database table for storing the threshold value calculation method of a class parameter in different time points,
5.4] parameter detecting
Identify the parameter attribute in emulated data file, the database table of the rule base corresponded is inquired about according to parameter attribute, the threshold value calculation method recorded in database table is utilized to calculate the threshold value of this parameter at this time point, by the actual value of this parameter compared with calculated value, judge that whether this parameter is abnormal
5.5] by step 5.4] show that the set condition of this parameter in parameter detecting result and simulation document compares, if both are consistent, then the rule base data form of this test run is arranged correctly, otherwise, rule base form arranges exception, re-starts rule base form and arranges.
During in order to ensure diagnosis, the connection reliability of system, in step 1] before, also comprise the whether normal step of interface transmission judged between data acquisition backup computer and Fault monitoring and diagnosis computing machine, concrete grammar is as follows:
6.1] according to same model engine test run True Data in the past, it is modified and makes emulated data file, malfunction monitoring point parameter required when described emulated data file comprises malfunction monitoring and judges, malfunction monitoring point parameter can be arranged to normal, abnormal two states
6.2] playback emulated data file in data acquisition backup computer, and malfunction monitoring point parameter each in emulated data is sent to Fault monitoring and diagnosis computing machine, if Fault monitoring and diagnosis computing machine can receive the data that data acquisition backup computer is transmitted, then this firing test data interface gathered between backup computer and Fault monitoring and diagnosis computing machine transmits normal.
If testing result 6.2] is normal, proceed the step whether judgment rule storehouse is correct, concrete grammar is as follows:
6.3] classified by same engine different parameters according to the attribute information of parameter, and conclude under the rule base with same alike result, the rule base of different engine arranges not identical; Described rule base comprises multi-class data Ku Biaoge, every class database table for storing the threshold value calculation method of a class parameter in different time points,
6.4] parameter attribute in emulated data file is identified, the database table of the rule base corresponded is inquired about according to parameter attribute, the threshold value calculation method recorded in database table is utilized to calculate the threshold value of this parameter at this time point, by the actual value of this parameter compared with calculated value, judge that whether this parameter is abnormal
6.5] by step 6.4] show that the set condition of this parameter in parameter detecting result and simulation document compares, if both are consistent, then the rule base data form of this test run is arranged correctly, otherwise, rule base form arranges exception, re-starts rule base form and arranges.
The present invention compared with prior art, has the following advantages:
1, the oxygen kerosene engine failure monitoring and fault diagnosis system that the invention solves for separate unit engine does not possess the ability of simultaneously monitoring four engine operating states, can accurate fault location, false alarm does not occur or does not report to the police.
2, the present invention's test run Fault monitoring and diagnosis in parallel method needs to diagnose the different parameters of different engine simultaneously, for avoiding mutually disturbing between each group of parameter, affect accuracy of judgement degree, adopt many group parameter independence discrimination technologies, utilize fault diagnosis mathematical algorithm, database technology, transducer range judgment technology, solve "or" relation between four engines, the problem of "AND" relation between separate unit engine parameters, improve the precision of fault diagnosis system algorithm, decrease due to algorithm, phenomenon is failed to report in the wrong report that the reasons such as sensor failure produce.
3, the present invention is when parameter threshold calculates, and have employed the method that ATA algorithm and red line shutdown algorithm combine, and at engine startup, adopts red line shutdown algorithm, can ensure the reliability of Fault monitoring and diagnosis method; After engine enters stable section, adopt ATA algorithm can improve the method running precision.
4, the present invention's test run Fault monitoring and diagnosis in parallel method is before parameter detecting, has carried out the inspection that between rule base and device, whether transmission interface is correct, has improve the reliability of system monitoring and diagnosis.
5, data acquisition alternate device gathers multiple signals simultaneously in the present invention, synchronized transmission is to Fault monitoring and diagnosis system, Packet Generation, judgment criterion interpretation time are all several times of separate unit engine, adopt multiparameter fault rapid discrimination technology, realize the multimachine multiparameter interpretation time with separate unit engine interpretation time consistency in the past, adopt Fault monitoring and diagnosis technology to send alerting signal and send shutdown command time delay to control system from monitoring fault origination point and be better than 50ms simultaneously.
Accompanying drawing explanation
Fig. 1 fault detection and diagnosis block diagram of system.
Embodiment
For four machines test run Fault monitoring and diagnosis in parallel system, the present invention is described in detail below.
Figure 1 shows that Fault monitoring and diagnosis system architecture diagram, system comprises sensor that engine carries, gathers leader cable, data switch cabinet, data acquisition equipment, data acquisition and backup computer, fault detection and diagnosis computing machine, acoustic-optic alarm etc.In the block diagram of system as shown in the figure, engine is arranged on the important and main portions of designer's care from belt sensor, in order to obtain performance parameter before test engine dispatches from the factory, has installed from belt sensor at main portions.Data switch cabinet completes Signal transmissions between foreground sensor image data and back-end data collecting device, and engine is connected with data switch cabinet by leader cable from belt sensor, each connection leader cable is provided with mark, can maintains easily and overhaul leader cable.Data acquisition equipment mainly realizes the functions such as parameter acquisition, filtering, amplification, transmission.Data acquisition and backup computer have two aspect effects, one, be responsible for engine important parameter answer work of recording workpoints, secondly, send engine failure monitoring and diagnosis parameter; Fault detection and diagnosis computing machine completes parameter acquiring, parameter grouping, parameter detecting, abnormal parameters judgement, sensor failure detection, breakdown judge, sends alerting signal, sends shutdown command function.
In illustrated system architecture, and if only if meet fault emergency cutoff rule time, send sound and light alarm signal and send instruction to control system simultaneously, judged by control system and whether shut down.
Four machines test run Fault monitoring and diagnosis in parallel method, mainly comprises the following steps:
1] actual value getparms
Obtain the digital quantity value of the malfunction monitoring point parameter required for Fault monitoring and diagnosis system, and digital quantity value be synchronously converted to analog quantity, i.e. the actual value of malfunction monitoring point parameter,
The digital quantity value of malfunction monitoring point parameter by being arranged on the sensor collection carried out on multiple engines of test run, and obtains after data processing,
2] parameter grouping
For the test run in parallel of this four machine, what first will solve is the grouping problem of engine parameter.When data acquisition and backup computer by all types of parameters that obtain from motor head (as pressure, temperature, vibration, water attack, pulsation, rotating speed etc.) digital quantity value when packing in real time and be sent to Fault monitoring and diagnosis computing machine by network, software is used to abandon non-faulting monitoring point parameter, obtained parameter is classified according to engine, the parameter of same engine is divided into one group, the dissimilar parameter of same engine gathers under same rule base, rule base comprises multi-class data Ku Biaoge, every class database table is for storing the threshold value calculation method of a class parameter in different time points, the rule base of different engine arranges not identical, this completes the grouping work of engine parameter.
3] parameter detecting
Parallel detection step 1] the malfunction monitoring point parameter that obtains, concrete detecting step is as follows:
Identification parameter attribute, the database table of the rule base corresponded is inquired about according to parameter attribute, the threshold value calculation method recorded in database table is utilized to calculate the threshold value of this parameter at this time point, by the actual value of this parameter compared with calculated value, if the actual value of parameter is in threshold range, then this parameter running status is normal, if the actual value of parameter exceeds threshold range, then enters step 4];
4] sensor failure rule
Sensor failure rule is independent of Fault monitoring and diagnosis rule base, when Fault monitoring and diagnosis computing machine receives data, first carry out failure detection, comprise open sensor, short circuit sensor, and sensor values oscillation on large scale etc., all think sensor failure when this takes place, rejected, do not entered within the scope of Fault monitoring and diagnosis DBD database diagnostics, avoid false alarm occurring and not reporting to the police, otherwise, carry out next step;
5] abnormal parameters judges
If malfunction monitoring point continuous parameters repeatedly exceeds threshold range, then this abnormal parameters, carries out early warning.
6] breakdown judge
When detecting extremely, start parameter and judge, between each monitoring parameter of separate unit engine interior, logical relation is "AND", and namely any multiple abnormal parameters of engine interior shows this engine abnormity.
Logical relation between four engines is "or", namely has an engine abnormity, and Fault monitoring and diagnosis system shows four machines test run results abnormity in parallel.
Each malfunction monitoring point parameter has logical relation in group, and between group, do not have logical relation, the method improves the precision of Fault monitoring and diagnosis system.
Fault location function main manifestations can provide accurate time of failure when being fault generation, which platform engine there occurs fault, have which parameter, fault that position etc. occurs.This is preliminary localization of fault result, the later stage with the continuous accumulation of firing test data and fault test run data and collection, then through network training, can to the reason occurred that is out of order.
Fault location function is mainly manifested in, and when the test runs in parallel of four machines are broken down, Fault monitoring and diagnosis system can show fault origination point in real time, is that problem has appearred in which platform engine, and can the position even reason that occurs of Primary Location fault.
Based on the optimization to above basic technical scheme, for improving the accuracy of the present invention's diagnosis further, the present invention has also made the improvement of the following aspects:
1] when have in separate unit engine four acquisition parameters three continuous exceed threshold value three times time, thinking that this engine has fault, is "or" relation between four engines, and now fault diagnosis system sends fault off signal to control system.For solving the logical relation between separate unit engine four parameters, between four engines, adopt database technology building database form, the inquiry of each parameter threshold is carried out with lookup table mode, time inquiring, Adjustable calculation is inquired about, software is contrasted by accessing database form and actual measurement sensor signal, thus carries out interpretation.
(2) the present invention is when parameter threshold calculates, and have employed the method that ATA algorithm and red line shutdown algorithm combine, and at engine startup, adopts red line shutdown algorithm, can ensure the reliability of Fault monitoring and diagnosis method; After engine enters stable section, adopt ATA algorithm can improve the method running precision.
At engine startup, adopt red line shutdown algorithm, the method is effectively the quickest.After entering stable section, adopt ATA algorithm, ATA algorithm and adaptive threshold thresholding algorithm, by calculating the threshold value obtaining fault diagnosis parameter in real time, namely the average of a bit of measured data and given bound setting range matching is utilized to draw a stable section threshold value, and every the set time, again adopt same procedure matching to draw the threshold value of subsequent time period.When engine working conditions change, bound setting range also can respective change.
(3) can application and trouble mode simulation technology in four machine Parallel tests, the unit that this technology can be used for Fault monitoring and diagnosis computing machine detects, and the object that unit detects is whether the rule base that failure judgement monitoring and diagnosis computing machine is applied when carrying out parameter detecting is correct; Also can be used for the On-line measurement of data acquisition backup computer and Fault monitoring and diagnosis computing machine, On-line measurement both can judge that the interface transmission between data acquisition backup computer and Fault monitoring and diagnosis computing machine was whether normal, also can the rule base applied when carrying out parameter detecting of failure judgement monitoring and diagnosis computing machine whether correct.
Unit detects and carried out before test run, and concrete step is as follows:
4.1] according to same model engine in the past test run True Data modify and make emulated data file, malfunction monitoring point parameter required when described emulated data file comprises malfunction monitoring and judges, malfunction monitoring point parameter can be arranged to normal, abnormal two states
4.2] the malfunction monitoring point parameter in emulated data file is read,
4.3] parameter grouping
Obtained parameter is classified according to engine, the parameter of same engine is divided into one group, same engine different parameters is classified by the attribute information according to parameter, and concludes under the rule base with same alike result, and the rule base of different engine arranges not identical; Described rule base comprises multi-class data Ku Biaoge, every class database table for storing the threshold value calculation method of a class parameter in different time points,
4.4] parameter detecting
Identify the parameter attribute in emulated data file, the database table of the rule base corresponded is inquired about according to parameter attribute, the threshold value calculation method recorded in database table is utilized to calculate the threshold value of this parameter at this time point, by the actual value of this parameter compared with calculated value, judge that whether this parameter is abnormal
4.5] by step 4.4] show that the set condition of this parameter in parameter detecting result and simulation document compares, if both are consistent, then the rule base data form of this test run is arranged correctly, otherwise, rule base form arranges exception, re-starts rule base form and arranges.
On-line measurement also carried out before test run, and concrete step is as follows:
6.1] according to same model engine in the past test run True Data modify and make emulated data file, malfunction monitoring point parameter required when described emulated data file comprises malfunction monitoring and judges, malfunction monitoring point parameter can be arranged to normal, abnormal two states
6.2] playback emulated data file in data acquisition backup computer, and malfunction monitoring point parameter quantities each in emulated data is sent to Fault monitoring and diagnosis computing machine, if Fault monitoring and diagnosis computing machine can receive the data that data acquisition backup computer is transmitted, then the interface transmission between the data acquisition backup computer of this test run and Fault monitoring and diagnosis computing machine is normal.
In actual applications, can interface transmission between data acquisition backup computer and Fault monitoring and diagnosis computing machine normal after, then utilize data acquisition backup computer and Fault monitoring and diagnosis computing machine to carry out the whether correct step in judgment rule storehouse, concrete grammar is as follows:
6.3] classified by same engine different parameters according to the attribute information of parameter, and conclude under the rule base with same alike result, the rule base of different engine arranges not identical; Described rule base comprises multi-class data Ku Biaoge, every class database table for storing the threshold value calculation method of a class parameter in different time points,
6.4] parameter attribute in emulated data file is identified, the database table of the rule base corresponded is inquired about according to parameter attribute, the threshold value calculation method recorded in database table is utilized to calculate the threshold value of this parameter at this time point, by the actual value of this parameter compared with calculated value, judge that whether this parameter is abnormal
6.5] by step 6.4] show that the set condition of this parameter in parameter detecting result and simulation document compares, if both are consistent, then the rule base data form of this test run is arranged correctly, otherwise, rule base form arranges exception, re-starts rule base form and arranges.
Claims (7)
1. a test run Fault monitoring and diagnosis method in parallel, is characterized in that: comprise the following steps:
1] actual value getparms
Obtain the digital quantity value of the malfunction monitoring point parameter required for Fault monitoring and diagnosis system, and digital quantity value be synchronously converted to analog quantity, i.e. the actual value of malfunction monitoring point parameter,
The digital quantity value of described malfunction monitoring point parameter by being arranged on the sensor collection carried out on multiple engines of test run, and obtains after data processing,
2] parameter grouping
Parameter is classified according to engine, the parameter of same engine is divided into one group, the different parameters of same engine is classified by the attribute information according to parameter, and concludes under having the rule base of same alike result with this parameter, and the rule base of different engine arranges not identical; Described rule base comprises multi-class data Ku Biaoge, every class database table for storing the threshold value calculation method of a class parameter in different time points,
3] parameter detecting
Parallel detection step 1] multiple breakdown in the motor monitoring points parameter of carrying out test run of obtaining, concrete detecting step is as follows:
Identification parameter attribute, the database table of the rule base corresponded is inquired about according to parameter attribute, the threshold value calculation method recorded in database table is utilized to calculate the threshold value of this parameter at this time point, by the actual value of this parameter compared with threshold value, if the actual value of parameter is in threshold range, then this parameter running status is normal, if the actual value of parameter exceeds threshold range, then carry out step 4]
4] sensor failure detects
Whether detecting sensor is in open circuit, short circuit, or the abnormality of numerical value oscillation on large scale, if so, this parameter is rejected, and is not re-used as malfunction monitoring point parameter; Otherwise, carry out next step;
5] abnormal parameters judges
If the actual value continuous several times of malfunction monitoring point parameter exceeds threshold range, then this abnormal parameters, carries out early warning.
2. test run Fault monitoring and diagnosis method in parallel according to claim 1, it is characterized in that: in step 5] after also comprise: 6] breakdown judge, when an engine has the actual value continuous several times of multiple malfunction monitoring point parameter to exceed threshold range simultaneously, confirm that this test run is broken down and reports to the police.
3. test run Fault monitoring and diagnosis method in parallel according to claim 1 and 2, is characterized in that: the threshold value calculation method that in described rule base, database table stores comprises two kinds, and one is ATA algorithm, another kind of red line shutdown algorithm,
At engine startup, adopt red line shutdown algorithm, after engine enters stable section, adopt ATA algorithm.
4. test run Fault monitoring and diagnosis method in parallel according to claim 1 and 2, it is characterized in that: in step 1] actual value getparms time, the digital quantity value of malfunction monitoring point parameter needs carry out data backup by data acquisition backup computer and be sent to Fault monitoring and diagnosis computing machine, after actual value getparms, by Fault monitoring and diagnosis computing machine completing steps 1] to step 5].
5. test run Fault monitoring and diagnosis method in parallel according to claim 4, is characterized in that: in step 1] before, also comprise the step whether judgment rule storehouse is correct, concrete grammar is as follows:
5.1] according to same model engine test run True Data in the past, it is modified and makes emulated data file, malfunction monitoring point parameter required when described emulated data file comprises malfunction monitoring and judges, malfunction monitoring point parameter can be arranged to normal, abnormal two states
5.2] the malfunction monitoring point parameter in emulated data file is read,
5.3] parameter grouping
Same engine different parameters is classified by the attribute information according to parameter, and concludes under the rule base with same alike result, and the rule base of different engine arranges not identical; Described rule base comprises multi-class data Ku Biaoge, every class database table for storing the threshold value calculation method of a class parameter in different time points,
5.4] parameter detecting
Identify the parameter attribute in emulated data file, the database table of the rule base corresponded is inquired about according to parameter attribute, the threshold value calculation method recorded in database table is utilized to calculate the threshold value of this parameter at this time point, by the actual value of this parameter compared with calculated value, judge that whether this parameter is abnormal
5.5] by step 5.4] show that the set condition of this parameter in parameter detecting result and simulation document compares, if both are consistent, then the rule base data form of this test run is arranged correctly, otherwise, rule base form arranges exception, re-starts rule base form and arranges.
6. test run Fault monitoring and diagnosis method in parallel according to claim 4, it is characterized in that: in step 1] before, also comprise the whether normal step of interface transmission judged between data acquisition backup computer and Fault monitoring and diagnosis computing machine, concrete grammar is as follows:
6.1] according to same model engine test run True Data in the past, it is modified and makes emulated data file, malfunction monitoring point parameter required when described emulated data file comprises malfunction monitoring and judges, malfunction monitoring point parameter can be arranged to normal, abnormal two states
6.2] playback emulated data file in data acquisition backup computer, and malfunction monitoring point parameter each in emulated data is sent to Fault monitoring and diagnosis computing machine, if Fault monitoring and diagnosis computing machine can receive the data that data acquisition backup computer is transmitted, then this firing test data interface gathered between backup computer and Fault monitoring and diagnosis computing machine transmits normal.
7. test run Fault monitoring and diagnosis method in parallel according to claim 6, is characterized in that: if 6.2] testing result normal, proceed the step whether judgment rule storehouse correct, concrete grammar is as follows:
6.3] classified by same engine different parameters according to the attribute information of parameter, and conclude under the rule base with same alike result, the rule base of different engine arranges not identical; Described rule base comprises multi-class data Ku Biaoge, every class database table for storing the threshold value calculation method of a class parameter in different time points,
6.4] parameter attribute in emulated data file is identified, the database table of the rule base corresponded is inquired about according to parameter attribute, the threshold value calculation method recorded in database table is utilized to calculate the threshold value of this parameter at this time point, by the actual value of this parameter compared with calculated value, judge that whether this parameter is abnormal
6.5] by step 6.4] show that the set condition of this parameter in parameter detecting result and simulation document compares, if both are consistent, then the rule base data form of this test run is arranged correctly, otherwise, rule base form arranges exception, re-starts rule base form and arranges.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410035097.XA CN103792087B (en) | 2014-01-24 | 2014-01-24 | Test run Fault monitoring and diagnosis method in parallel |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410035097.XA CN103792087B (en) | 2014-01-24 | 2014-01-24 | Test run Fault monitoring and diagnosis method in parallel |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103792087A CN103792087A (en) | 2014-05-14 |
| CN103792087B true CN103792087B (en) | 2016-03-16 |
Family
ID=50667965
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410035097.XA Active CN103792087B (en) | 2014-01-24 | 2014-01-24 | Test run Fault monitoring and diagnosis method in parallel |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103792087B (en) |
Families Citing this family (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105335599B (en) * | 2014-08-07 | 2018-03-27 | 上海通用汽车有限公司 | A kind of detection method and system of automobile failure diagnosis rate |
| CN104238501B (en) * | 2014-08-22 | 2017-03-08 | 中国石油天然气股份有限公司 | Alarm data processing method and device for refining system |
| CN105571865B (en) * | 2014-11-03 | 2018-07-10 | 中国飞行试验研究院 | Middle engine behavior of taking a flight test monitors method |
| CN105278521B (en) * | 2015-10-15 | 2018-01-26 | 珠海格力电器股份有限公司 | Method and device for diagnosing fault cause of unit and air conditioning unit |
| CN105606205B (en) * | 2015-12-23 | 2018-06-08 | 南京南瑞继保电气有限公司 | A kind of out-of-limit discriminating gear of the vibration of slewing and method |
| CN105680931A (en) * | 2016-02-26 | 2016-06-15 | 浪潮通用软件有限公司 | Message transmission method based on UDP protocol |
| CN108109319A (en) * | 2017-12-11 | 2018-06-01 | 山西省交通科学研究院 | It is a kind of by distributed optical fiber temperature measurement data integration in the method for tunnel comprehensive monitoring system |
| CN108238538B (en) * | 2017-12-28 | 2020-09-04 | 北京市地铁运营有限公司地铁运营技术研发中心 | Fault monitoring method and fault monitoring system of escalator |
| CN108710411A (en) * | 2018-05-08 | 2018-10-26 | 南京思甲宁新材料科技有限公司 | A method of the hardware electronic lock system with cooling element |
| CN108917823B (en) * | 2018-06-06 | 2020-11-27 | 国网山东省电力公司博兴县供电公司 | Online monitoring method and system for automatically setting monitoring parameters of switch cabinet |
| CN108829088B (en) * | 2018-07-20 | 2021-11-05 | 深圳市道通科技股份有限公司 | Automobile diagnosis method and device and storage medium |
| CN110398264A (en) * | 2019-07-31 | 2019-11-01 | 联想(北京)有限公司 | A kind of equipment condition monitoring method and system |
| CN110879152A (en) * | 2019-12-02 | 2020-03-13 | 北京航天试验技术研究所 | Liquid rocket engine test real-time data diagnosis strategy |
| CN111751111A (en) * | 2020-07-08 | 2020-10-09 | 中国第一汽车股份有限公司 | Engine parameter monitoring device and method and vehicle |
| CN111947702B (en) * | 2020-07-16 | 2022-03-18 | 中广核核电运营有限公司 | Sensor cross validation fault diagnosis method and device and computer equipment |
| CN112134942B (en) * | 2020-09-18 | 2023-12-19 | 北京市生态环境监测中心 | Heavy vehicle remote monitoring platform data quality detection device and method |
| CN112555050B (en) * | 2020-12-02 | 2022-03-11 | 西安航天动力试验技术研究所 | Method for monitoring running state and scramming of distributed supersonic airflow generation device |
| CN114459762B (en) * | 2020-12-25 | 2024-02-23 | 北京天兵科技有限公司 | Rocket engine health state diagnosis system and method |
| CN113757223B (en) * | 2021-09-06 | 2023-11-03 | 江苏徐工工程机械研究院有限公司 | Hydraulic component reliability analysis method and system, computer device, and storage medium |
| CN115326400B (en) * | 2022-10-13 | 2023-04-07 | 中国航发四川燃气涡轮研究院 | Fault diagnosis method of aircraft engine surge detection system and electronic equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201322686Y (en) * | 2008-12-26 | 2009-10-07 | 中国兵器装备集团摩托车检测技术研究所 | Engine pedestal long-distance fault diagnosis system |
| CN202836971U (en) * | 2012-08-29 | 2013-03-27 | 桂林金铭和智控科技有限公司 | Engine state monitoring and fault diagnosis module |
| CN103217291A (en) * | 2013-01-06 | 2013-07-24 | 国电联合动力技术有限公司 | Wind generating set fault diagnosis method and system |
| CN103245508A (en) * | 2013-04-11 | 2013-08-14 | 西安航天动力试验技术研究所 | Synchronous and dynamic early warning method and early warning system in multiple-engine test |
| CN103383312A (en) * | 2013-06-27 | 2013-11-06 | 西安航天动力试验技术研究所 | On-line monitoring system and method of engine test data |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR2939924B1 (en) * | 2008-12-15 | 2012-10-12 | Snecma | IDENTIFICATION OF FAILURES IN AN AIRCRAFT ENGINE |
-
2014
- 2014-01-24 CN CN201410035097.XA patent/CN103792087B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201322686Y (en) * | 2008-12-26 | 2009-10-07 | 中国兵器装备集团摩托车检测技术研究所 | Engine pedestal long-distance fault diagnosis system |
| CN202836971U (en) * | 2012-08-29 | 2013-03-27 | 桂林金铭和智控科技有限公司 | Engine state monitoring and fault diagnosis module |
| CN103217291A (en) * | 2013-01-06 | 2013-07-24 | 国电联合动力技术有限公司 | Wind generating set fault diagnosis method and system |
| CN103245508A (en) * | 2013-04-11 | 2013-08-14 | 西安航天动力试验技术研究所 | Synchronous and dynamic early warning method and early warning system in multiple-engine test |
| CN103383312A (en) * | 2013-06-27 | 2013-11-06 | 西安航天动力试验技术研究所 | On-line monitoring system and method of engine test data |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103792087A (en) | 2014-05-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103792087B (en) | Test run Fault monitoring and diagnosis method in parallel | |
| US20180107196A1 (en) | Method of Detecting Home Appliance Bus Control System | |
| US5919267A (en) | Neural network fault diagnostics systems and related method | |
| US6456928B1 (en) | Prognostics monitor for systems that are subject to failure | |
| CN107831422B (en) | GIS equipment partial discharge diagnosis method and system | |
| WO2020132060A1 (en) | A mechanical failure detection system and method | |
| CN110085005B (en) | Ship generator monitoring method, device and system and storage medium | |
| JP2006226135A (en) | Method and device for failure diagnosis of manifold pressure sensor | |
| CN110701137A (en) | Intelligent online detection and diagnosis device and method for hydraulic system of heading machine | |
| CN114255784A (en) | Substation equipment fault diagnosis method based on voiceprint recognition and related device | |
| CN117851956B (en) | Electromechanical equipment fault diagnosis method, system and terminal based on data analysis | |
| CN110334844A (en) | Utilize the ship optimized use method of the main promotion diesel engine operating status of ship | |
| CN113027607B (en) | Fault detection method for oil-gas separator and related device | |
| US11836057B2 (en) | Fault location in a redundant acquisition system | |
| CN114353869A (en) | Online monitoring method and system for mobile equipment and readable storage medium | |
| CN116490933A (en) | Device and method for tracking abnormal state judgment basis by using neural network model | |
| CN113485898A (en) | Vibration measuring point display method, device, equipment and storage medium | |
| CN109488473B (en) | Online prejudgment system and online prejudgment method of engine | |
| KR101823626B1 (en) | System and method for detecting trouble of apparatus with signal analysis | |
| CN116558835A (en) | Fault diagnosis system based on measured data | |
| CN105090000A (en) | Fault diagnosis method for rough vacuum pumping system of space environment simulator | |
| CN115144668A (en) | Fault detection method, signal simulator and fault detection system | |
| CN104533882B (en) | Diagnosis method and system for leakage fault of oil cylinders | |
| CN109828175B (en) | Built-in test method for electronic sequential assembly | |
| CN113203531A (en) | Method for monitoring sealing degree of transformer bushing |
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 |