CN104121804B - A kind of automatic load system initial failure predicting method merged based on many field information - Google Patents
A kind of automatic load system initial failure predicting method merged based on many field information Download PDFInfo
- Publication number
- CN104121804B CN104121804B CN201410352352.3A CN201410352352A CN104121804B CN 104121804 B CN104121804 B CN 104121804B CN 201410352352 A CN201410352352 A CN 201410352352A CN 104121804 B CN104121804 B CN 104121804B
- Authority
- CN
- China
- Prior art keywords
- information
- load system
- automatic load
- field information
- signal
- 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
Abstract
The present invention relates to a kind of automatic load system initial failure predicting method merged based on many field information, belong to cannon automatic load system fault indication analysis technical field, technical problem to be solved there is provided the multiple physical field information of a kind of employing and carries out fusion treatment to data, antijamming capability is strong, recognition accuracy and the high automatic load system initial failure predicting method merged based on many field information of reliability, the technical scheme adopted is for obtaining multiple physical field primary data information (pdi), the acceleration of Real-time Collection automatic load system, the analog signal that angle parameter and current sensor export, and the signal that puts in place of each proximity switch, angle, the data signal of speed and temporal characteristics, the information of collection is mapped mutually, associates, weighting process and Dimensionality Reduction optimization, set up the model based on D-S evidence and fuzzy set theory, thus carry out many field information and merge and indicate with fault, accuracy of the present invention is high, and the early stage indication that can be widely used in cannon automatic load system fault is analyzed.
Description
Technical field
The present invention relates to a kind of automatic load system initial failure predicting method merged based on many field information, belong to cannon automatic load system fault indication analysis technical field.
Background technology
Large diameter cannon automatic load system is one of core of cannon weapon system, it is the electro-mechanical system of very complicated high-speed cruising, the quality of its service behaviour directly has influence on the performance of combat efficiency of weapon system, it is the high subsystem of reliability requirement, directly affect the firing rate of self-propelled gun, firepower, survival ability, mobility and opportunity of combat to hold, also become the key factor of restriction China armament systems development.
Existing Large diameter cannon automatic load system generally includes: drive motors, rotation or parallel-moving type ammunition cabins, push away bullet (medicine) mechanism, bullet (medicine) for transfer mechanism, put in place sense switch and displacement speed sensor, control flow and software etc.Automatic load system requires each Mechanism motion coordination, for monoblock type ammunition (separated ammunition is similar), its workflow generally:
Rammer structure carries out pushing away springing and does, ammunition is released (sending) from rotation or parallel-moving type ammunition cabins, ammunition is coordinated to swing after knee-joint lives ammunition, when putting identical with gun tube angle (random angle loads), indicated value is provided by angular transducer or proximity switch, control to coordinate arm accurately to locate, complete ammunition feed action; Then, the switching mechanism coordinated on arm rotates, and bullet is put into the rammer cartridge drum of gun tube rear end, and by ammunition relatively fixing (clamping); After cartridge drum receives ammunition, rammer (usually adopting ejection type rammer when the corresponding Height Angle of heavy caliber ammunition loads) driving pushes away bullet dolly and carries out pushing away springing work, and bullet enters defeated bullet passage at a high speed, carries out card thorax (bearing band embedding rifling); Finally carry out pass door bolt percussion, complete a process of transmission bullet automatically.Control device receives the information that respectively puts in place of ammunition and sends instruction, the orderly action of drive motors or hydraulic valve Shi Ge mechanism and continuous fire.The flow process successively action that each mechanism of automatic load system sets according to actuation cycle figure, under the prerequisite that mutual action is not interfered, mechanism action is overlapping, is beneficial to improve burst-firing frequency, realizes High-Speed Automatic filling.
Supply the high-speed motion of Ramming Device and collision to constitute the principal vibration excitation of executing agency in automatic load system, be also that the external interference of control device (comprising sensor) encourages simultaneously.High-speed motion between component and shock make to produce excessive static stress and distortion for Ramming Device, and the response of continual impact shock, cause component plastic deformation or fatigue damage.High-speed motion between component, touch to rub and clash into and also play the load of executing agency by changing transmission, the change of mechanism's active force and loading moment makes the load current of drive motors change, thus the Changing Pattern of load current and the state direct correlation of motor-driven mechanism action.Not smooth, the inharmonious even clamping stagnation of motion for Ramming Device all can change the time flow of control device, and the electromagnetic environment of the igniting launching shock of ammunition in cannon thorax not only destruct limit device, the time difference that during Canon launching, process is entered in the recoil of barrel assembly again yet will affect control flow.This many factors composition cannon automatic load system work unreliable is also the main contributor of automatic load system initial failure.
Cannon automatic load system live load weight, bad environments, moving component acts frequently, the enchancement factor affecting moving component course of action is more, due to the impact of vibration, impact, fretting wear and elastic deformation etc., causes the accuracy of critical piece mechanism action, promptness inadequate, thus make mechanism kinematic form uniformity poor, to move not in place, asynergia, cause system motion to be stagnated time serious, cisco unity malfunction.Due to the difficulty of multiple response message on-the-spot test during complicated automatic load system shooting and the restriction of data processing technique validity, application experimental test means are not had to implement fault indication and the mechanism action reliability consideration work of automatic load system so far.The common fault of cannon automatic load system mainly contains: 1. transmission plays that mechanism kinematic is not smooth, not in place, dynamo-electric exercise not harmony etc. causes and stop penetrating; 2. the crackle that rammer structure high-speed impact produces damages; 3. electromagnetic interference and rush of current cause control system misoperation; 4. component wear clamping stagnation or damping is excessive causes the continuous fire cycle to strengthen and inharmonious etc. with control flow.
Automatic load system initial failure has following characteristics: 1) fault signature is faint; 2) characteristic information time variation is strong; 3) fault features information coupling; 4) feature performance is uncertain; 5) initial failure active development.These features cause Representative Faults Diagnosis method not in the past to be suitable for the indication of automatic load system initial failure, make automatic load system initial failure indicate than conventional equipment fault diagnosis require higher, difficulty is larger.Current Large diameter automatic load system generally can only gather the switching signal of Overstroke, corner and tach signal, be furnished with based on the data acquisition running control, Threshold Alerts and communication function, but its information content is very limited, still initial failure indication can not be carried out to automatic load system.
Summary of the invention
The present invention overcomes the deficiency that prior art exists, technical problem to be solved there is provided and a kind of adopt multiple physical field information and carry out fusion treatment to data, and antijamming capability is strong, recognition accuracy and the high automatic load system initial failure predicting method merged based on many field information of reliability.
For solving the problems of the technologies described above, the technical solution adopted in the present invention is: a kind of automatic load system initial failure predicting method merged based on many field information, carries out according to following steps,
A, acquisition multiple physical field primary data information (pdi), preferred test point in each executing agency of automatic load system, arranges ICP piezoelectric acceleration sensor; Photo-electric angle parametrical sense device is installed near the rotating shaft of each rotating member; Each electric driver installs Hall current sensor; And from control unit, draw the signal that puts in place of each proximity switch, angle and rate signal; Thus draw the real-time characteristic of control flow Zhong Ge mechanism;
The analog signal that b, the acceleration adopting Portable Data-Acquisition System Real-time Collection automatic load system, angle parameter and current sensor export, and the data signal of the signal that puts in place of each proximity switch of control unit extraction, angle, speed and temporal characteristics;
C, gathered each field data is carried out respectively to the pretreatment of stress release treatment interference, again the normalization rule under different shape between initial failure classification is set up to many field information, and the mapping relations between corresponding multidimensional form, space and time variables is utilized three-dimensional motion shape information to be mapped as change continuous time, provide action-time circular chart during each executing agency's actual motion, complete the mutual mapping of information between field, linear transformation, normalized and information association;
D, to through mutually mapping and the time domain acceleration of association process, angle, angular speed and load signal, first do the various Digital Signal Analysis of time domain and extract corresponding temporal signatures, comprising action period, peak value, average, mean-square value, variance, auto-correlation, cross-correlation, amplitude probability distribution, impact energy, High Order Moment.Again action-time circular chart that the feature that time domain is extracted associates out with from control unit is combined and carry out the information fusion of characteristic layer, then the standardization index of many field information of linear transformation structure automatic load system different phase is adopted, by setting up yardstick measuring and calculating and factor of influence structure weight matrix, for many field information sample distributes different weights;
E, employing local linear Embedded algorithm and rough set theory carry out information dimension yojan, restructuring and optimization information content, utilize PSO and evolutionary programming algorithm to optimize characteristic parameter, set up the model based on D-S evidence and fuzzy set theory, thus carry out many field information and merge and indicate with fault.
Preferably, when carrying out data acquisition, the portable 32 circuit-switched data acquisition system SCADAS Mobile SCM05 adopting Belgian LMS company to produce carry out synchronous data collection.
Preferably, when setting up the model based on D-S evidence and fuzzy set theory, adopt and frequency domain, time and frequency zone, small echo, EMD and Local-wave Analysis are done to acceleration, angle, angular speed and load signal, extract energy and the entropy-spectrum feature thereof of frequency domain and other decomposition field; Construction is suitable for the SVM model of small sample identification, carries out discriminator to automatic load system initial failure classification; Thus carry out the fusion of many field information and indicate judging.
The present invention adopts many physical messages gathering automatic load system, comprise the analog signal that the acceleration of automatic load system, angle parameter and current sensor export, and the data signal of the signal that puts in place of each proximity switch of control unit extraction, angle, speed and temporal characteristics; By mutually mapping above-mentioned information, linear transformation, normalized, information association and information characteristics extract and merge, thus construction is suitable for SVMs (SVM) model of small sample identification, carries out discriminator to automatic load system initial failure classification.The optimisation strategy of further formulation dynamic pattern recognition model and Performance Evaluating Indexes, carry out many field information and merge and indication decision-making, improve accuracy and the reliability of automatic load system initial failure indication.
Accompanying drawing explanation
Fig. 1 is the FB(flow block) of overall technological scheme of the present invention.
Detailed description of the invention
Below in conjunction with accompanying drawing, the specific embodiment that the present invention does is described further.
As shown in Figure 1, a kind of automatic load system initial failure predicting method merged based on many field information,
A, multiple physical field primary data information (pdi) obtain
(1) preferred test point in each executing agency of automatic load system, arranges ICP piezoelectric acceleration sensor.Consider that the Hz-KHz of accelerometer is wide, sensitive to mechanism kinematic response, belong to test link additional outside automatic load system, should be fewer but better as far as possible, need to be optimized point position, optimization principles is should the more multimember vibratory response of sensor sensing (near many components joint, in distance mechanism's high velocity impact point and mechanism, troublesome component is a little near), is convenient to reliable installation and cabling again.
(2) opto-electronic conversion formula angle parametrical sense device is installed near the rotating shaft of each rotating member of executing agency, tests rotary defeated cartridge drum, coordinate the electric impulse signal of arm and switching mechanism angular displacement.
(3) on each drive unit of automatic load system, Hall current sensor is installed, the load current of each drive motors is tested.
(4) carry out changing and drawing to the sensing detecting data (comprising the signal that puts in place of each proximity switch, angle and rate signal) in automatic load system control unit.
(5) with reference to the motion priority of automatic load system mechanism action-displacement cycle figure upper member, from the software control flow process of control unit, the real-time characteristic of each mechanism is drawn.
B, the synchronous Real-time Collection of portable many field information
Because above-mentioned 5 kinds of multiple physical field information datas are all using the time as unified abscissa, be thus convenient to carry out real-time data acquisition when medium-and-large-sized artillery shooting in automatic Loading process.The on-the-spot portable 32 circuit-switched data acquisition system SCADAS Mobile SCM05 adopting Belgian LMS company general in the world to produce carry out synchronous data collection, and each interchannel of this system is mutually isolated, and antijamming capability is strong.System has high sample frequency, is convenient to the explication de texte of sampled data.
The mutual mapping of c, many field information, association and process
After the information data of the different fields gathered for data Layer enters computer system, first need to carry out pretreatment respectively to each field data: do base wavelet and high LPF to acceleration responsive signal, stress release treatment disturbs; The electric impulse signal of reflection rotating mechanism variation in angular displacement is changed, maps and be processed into angular displacement and the angular speed rule over time of each rotating mechanism; First pretreatment is done to the current signal of Hall element collection, eliminates the impact of power frequency component, then map load (power or the moment) situation of change to the time of process Chu Ge mechanism; To drawing from control unit checkout gear and the switch collected and the angle parameter signal of telecommunication, the mark of advanced line time axle and process, then carry out association process with the acceleration of each mechanism, angle parameter, load signal; Control flow to be drawn and the time dependent feature of each mechanism gathered joins in the association of many time-domain informations, provide action-time circular chart during each executing agency's actual motion.Complete the mutual mapping of information between field, linear transformation, normalized and information association.Utilize space and time variables three-dimensional motion shape information to be mapped as change continuous time, set up the normalization rule under different shape between initial failure classification, and the mapping relations between corresponding multidimensional form.
The feature extraction of d, many field information and Feature-level fusion
To through mutually mapping and the time domain acceleration of association process, angle, angular speed and load signal, do the various Digital Signal Analysis of time domain and extract corresponding temporal signatures, comprising action period, peak value, average, mean-square value, variance, auto-correlation, cross-correlation, amplitude probability distribution, impact energy, High Order Moment.Analyze the constant interval of each characteristic value and the development and change rule with fault type and degree, utilize parameter comparison and fuzzy clustering method can the preliminary each fault type of discriminance analysis.Combined by action-time circular chart that the feature that time domain is extracted associates out with from control unit and carry out the information fusion of characteristic layer, initial analysis system may produce reason and the position of fault.Adopt the standardization index of many field information of linear transformation structure automatic load system different phase, by setting up yardstick measuring and calculating and factor of influence structure weight matrix, for many field information sample distributes different weights.
After feature fusion, contain much information, outstanding problem that dimension is many, adopt local linear Embedded algorithm and rough set theory to carry out information dimension yojan, restructuring and optimization information content, utilize particle group optimizing (PSO) and evolutionary programming algorithm to optimize characteristic parameter; In the Fault Identification Decision-level fusion stage, front two-graded fusion result can be made full use of, set up the model based on D-S evidence and fuzzy set theory, carry out the many field information fusions of decision-making level and indicate with fault.Also need to do frequency domain, time and frequency zone, small echo, empirical mode decomposition (EMD) and Local-wave Analysis further to acceleration, angle, angular speed and load signal, extract energy and the entropy-spectrum feature thereof of frequency domain and other decomposition field; Construction is suitable for SVMs (SVM) model of small sample identification, carries out discriminator to automatic load system initial failure classification.The optimisation strategy of further formulation dynamic pattern recognition model and Performance Evaluating Indexes, carry out many field information and merge and indication decision-making, improve accuracy and the reliability of automatic load system initial failure indication.
By reference to the accompanying drawings embodiments of the invention are explained in detail above; but the present invention is not limited to above-described embodiment; in the ken that those of ordinary skill in the art possess; the various changes can also made under the prerequisite not departing from present inventive concept, also should be considered as protection scope of the present invention.
Claims (3)
1., based on the automatic load system initial failure predicting method that many field information merge, it is characterized in that: carry out according to following steps,
A, acquisition multiple physical field primary data information (pdi), preferred test point in each executing agency of automatic load system, arranges ICP piezoelectric acceleration sensor; Photo-electric angle parametrical sense device is installed near the rotating shaft of each rotating member; Each electric driver installs Hall current sensor; And from control unit, draw the signal that puts in place of each proximity switch, angle and rate signal; Thus draw the real-time characteristic of control flow Zhong Ge mechanism;
The analog signal that b, the acceleration adopting Portable Data-Acquisition System Real-time Collection automatic load system, angle parameter and current sensor export, and the data signal of the signal that puts in place of each proximity switch of control unit extraction, angle, speed and temporal characteristics;
C, gathered each field data is carried out respectively to the pretreatment of stress release treatment interference, again the normalization rule under different shape between initial failure classification is set up to many field information, and the mapping relations between corresponding multidimensional form, space and time variables is utilized three-dimensional motion shape information to be mapped as change continuous time, provide action-time circular chart during each executing agency's actual motion, complete the mutual mapping of information between field, linear transformation, normalized and information association;
D, to the time domain acceleration through mutual mapping and association process, angle, angular speed and load signal, first do the various Digital Signal Analysis of time domain and extract corresponding temporal signatures, comprise the action period, peak value, average, mean-square value, variance, auto-correlation, cross-correlation, amplitude probability distribution, impact energy, High Order Moment, again action-time circular chart that the feature that time domain is extracted associates out with from control unit is combined and carry out the information fusion of characteristic layer, then the standardization index of many field information of linear transformation structure automatic load system different phase is adopted, by setting up yardstick measuring and calculating and factor of influence structure weight matrix, for many field information sample distributes different weights,
E, employing local linear Embedded algorithm and rough set theory carry out information dimension yojan, restructuring and optimization information content, utilize PSO and genetic algorithm optimization characteristic parameter, set up the model based on D-S evidence and fuzzy set theory, thus carry out many field information and merge and indicate with fault.
2. a kind of automatic load system initial failure predicting method merged based on many field information as claimed in claim 1, it is characterized in that: when carrying out data acquisition, the portable 32 circuit-switched data acquisition system SCADAS Mobile SCM05 adopting Belgian LMS company to produce carry out synchronous data collection.
3. a kind of automatic load system initial failure predicting method merged based on many field information as claimed in claim 1, it is characterized in that: when setting up the model based on D-S evidence and fuzzy set theory, adopt and frequency domain, time and frequency zone, small echo, EMD and Local-wave Analysis are done to acceleration, angle, angular speed and load signal, extract energy and the entropy-spectrum feature thereof of frequency domain and other decomposition field; Construction is suitable for the SVM model of small sample identification, carries out discriminator to automatic load system initial failure classification; Thus carry out the fusion of many field information and indicate judging.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410352352.3A CN104121804B (en) | 2014-07-23 | 2014-07-23 | A kind of automatic load system initial failure predicting method merged based on many field information |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410352352.3A CN104121804B (en) | 2014-07-23 | 2014-07-23 | A kind of automatic load system initial failure predicting method merged based on many field information |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104121804A CN104121804A (en) | 2014-10-29 |
| CN104121804B true CN104121804B (en) | 2015-11-04 |
Family
ID=51767322
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410352352.3A Active CN104121804B (en) | 2014-07-23 | 2014-07-23 | A kind of automatic load system initial failure predicting method merged based on many field information |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104121804B (en) |
Families Citing this family (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104679981A (en) * | 2014-12-25 | 2015-06-03 | 新疆大学 | Vibration signal noise reduction method based on variable-step-length LMS-EEMD |
| CN105758450B (en) * | 2015-12-23 | 2017-11-24 | 西安石油大学 | Met an urgent need based on multisensor the fire-fighting early warning sensory perceptual system construction method of robot |
| CN107024141B (en) * | 2017-05-23 | 2018-07-06 | 中北大学 | Sound and vibration monitoring and defect positioning method for Ramming Device assembling quality |
| CN107943818A (en) * | 2017-10-09 | 2018-04-20 | 中国电子科技集团公司第二十八研究所 | A kind of Urban Data service system and method based on Multi-source Information Fusion |
| CN107942943B (en) * | 2017-12-25 | 2019-12-31 | 北京信息科技大学 | High-end numerical control equipment state identification method based on multi-source information fusion |
| CN108647642B (en) * | 2018-05-10 | 2021-08-31 | 北京航空航天大学 | Multi-sensor crack damage comprehensive diagnosis method based on fuzzy fusion |
| CN108801043B (en) * | 2018-06-22 | 2019-11-22 | 西北工业大学 | A kind of supply automatically plays On-line Fault monitoring and prediction technique |
| CN110044631A (en) * | 2019-03-13 | 2019-07-23 | 中交广州航道局有限公司 | Trend prediction method, device and the computer equipment of ship machine diesel engine |
| CN111982174A (en) * | 2019-05-23 | 2020-11-24 | 中国科学院沈阳自动化研究所 | Force-magnetic-acoustic three-field data fusion industrial equipment damage identification method |
| CN111089512B (en) * | 2019-12-11 | 2022-05-13 | 南京理工大学 | Method for synchronously monitoring internal and external states of ammunition feeding and conveying system and diagnosing faults |
| CN115371490A (en) * | 2022-08-24 | 2022-11-22 | 中国人民解放军陆军工程大学 | General comprehensive electronic information system data acquisition equipment for self-propelled artillery |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101614775A (en) * | 2009-07-15 | 2009-12-30 | 河北科技大学 | Transformer State Assessment system and appraisal procedure thereof based on Multi-source Information Fusion |
| CN103116090A (en) * | 2013-01-21 | 2013-05-22 | 江南大学 | Three-phrase pulse-width modulation (PWM) rectifier fault diagnosis method based on wavelet packet analysis and support vector machine |
| US8825567B2 (en) * | 2012-02-08 | 2014-09-02 | General Electric Company | Fault prediction of monitored assets |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8260486B2 (en) * | 2008-02-01 | 2012-09-04 | GM Global Technology Operations LLC | Method and apparatus for diagnosis of sensors faults using adaptive fuzzy logic |
| US20120166363A1 (en) * | 2010-12-23 | 2012-06-28 | Hongbo He | Neural network fault detection system and associated methods |
-
2014
- 2014-07-23 CN CN201410352352.3A patent/CN104121804B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101614775A (en) * | 2009-07-15 | 2009-12-30 | 河北科技大学 | Transformer State Assessment system and appraisal procedure thereof based on Multi-source Information Fusion |
| US8825567B2 (en) * | 2012-02-08 | 2014-09-02 | General Electric Company | Fault prediction of monitored assets |
| CN103116090A (en) * | 2013-01-21 | 2013-05-22 | 江南大学 | Three-phrase pulse-width modulation (PWM) rectifier fault diagnosis method based on wavelet packet analysis and support vector machine |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104121804A (en) | 2014-10-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104121804B (en) | A kind of automatic load system initial failure predicting method merged based on many field information | |
| CN101680946B (en) | Methods and apparatus for selecting a target from radar tracking data | |
| CN102043158B (en) | Signal detection and judgment method and device in capture of weak satellite navigation signal | |
| CN102507230A (en) | Method for diagnosing fault of automatic ammunition supply and transportation device | |
| CN202442649U (en) | A turntable for semi-physical simulation of a laser terminal guided projectile | |
| CN102410785A (en) | Turntable for semi-physical simulation of laser terminally guided projectile | |
| CN102243133A (en) | High-speed automaton fault diagnosis method based on movement patterns and impact signal analysis | |
| CN106247848B (en) | A kind of complicated automatic Incipient Fault Diagnosis method for supplying bullet system | |
| CN106440948A (en) | Shooting training system and shooting training method | |
| CN205301888U (en) | Confirm to be used for equipment and device and operating parameter generater of operating parameter of pump sending unit of well | |
| CN103017662B (en) | Five-freedom-degree vibration displacement test method for artillery cradle | |
| CN205318142U (en) | Confirm to be used for equipment and device and operating parameter generater of operating parameter of pump sending unit of well | |
| CN106768549A (en) | A kind of high dynamic carrier environment force measuring device | |
| CN111582135B (en) | Excavator hand operation proficiency evaluation method and device based on working stage identification | |
| CN103453799A (en) | Real-time measurement method for quantity of shot bullets of small arm | |
| CN205301858U (en) | Data acquisition device of broken type state of automatic recording test piece based on EPC | |
| Nilsson et al. | Robust driving pattern detection and identification with a wheel loader application | |
| CN113222399B (en) | Comprehensive performance evaluation method for engineering equipment operation guarantee | |
| CN203276260U (en) | Detector of artillery control system computer | |
| CN105910803A (en) | Acoustic-pressure-information-processing-based diagnosis method for feeding system fault | |
| CN101793590A (en) | Structural impact damage diagnostic method based on blackboard cooperation | |
| CN111089512B (en) | Method for synchronously monitoring internal and external states of ammunition feeding and conveying system and diagnosing faults | |
| CN110376579B (en) | Dynamic programming track-before-detect method for maneuvering target | |
| CN115759805A (en) | CUSUM-RHD-based gun anti-recoil device state evaluation method | |
| Nurmi et al. | Detection and isolation of leakage and valve faults in hydraulic systems in varying loading conditions, Part 1: Global Sensitivity Analysis |
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 |