CN202183018U - Intelligent neural network moonlet fault diagnosis device based on DSP (Digital Signal Processor) - Google Patents
Intelligent neural network moonlet fault diagnosis device based on DSP (Digital Signal Processor) Download PDFInfo
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- CN202183018U CN202183018U CN2011202718901U CN201120271890U CN202183018U CN 202183018 U CN202183018 U CN 202183018U CN 2011202718901 U CN2011202718901 U CN 2011202718901U CN 201120271890 U CN201120271890 U CN 201120271890U CN 202183018 U CN202183018 U CN 202183018U
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Abstract
An intelligent neural network moonlet fault diagnosis device based on a DSP (Digital Signal Processor) comprises the DSP, peripheral equipment of the DSP, a CPLD (Complex Programmable Logic Device), a storage unit, a bus transceiver and a serial port communication module. The fault diagnosis device can detect telemetry parameters of each stand-alone by a CAN bus on a moonlet, and if abnormal parameters are monitored, a preset four-layer fault diagnosis BP neural network is utilized to diagnose the stand-along with faults and reasons, to display results and to conduct early warning and promotion for the faults. Simultaneously, the fault diagnosis device can conduct early warning and promotion for the faults by collecting land detection serial signals of the stand-alone and analogue quantity signals. The fault diagnosis device can monitor the various parameters of multiple stand-alones of each subsystem of the moonlet simultaneously, is applicable to positioning complicated system faults, can improve the instantaneity and accuracy of fault diagnosis in the testing process of the moonlet, and is applicable to the testes of the whole moonlet with different configurations and applications and the union testes of the various subsystems.
Description
Technical field
The utility model relates to a kind of satellite failure diagnostic device.
Background technology
Lead time is short, production cost is low is one of significant advantage of moonlet, and moonlet has become the necessary complement of large satellite, in field of space technology, has occupied more and more important position.
At present; When carrying out the moonlet ground test; Utilize the working status parameter of each subsystem, each unit on many computer acquisition stars mostly, then carry out manual work according to the design considerations file or area of computer aided is differentiated respectively, this mode has expended plenty of time and human and material resources cost; Especially the method for diagnosing faults in testing; The problem that has the following aspects: (1) is because each subsystem unit of moonlet is of a great variety, parameter is different, and the too decoupling zero of adopting at present of method of discrimination, therefore is difficult to efficiently the system-level malfunction through the many signs of the special differentiation of one-parameter; (2) existing method for diagnosing faults can only real-time diagnosis go out to lack part simple fault reason, after complex failure takes place, is difficult to simulation reproduction fault the scene takes place, and causes failure cause to be difficult to accurate diagnosis; (3) existing method for diagnosing faults lacks self-learning capability, can not adapt to the configuration differenceization of platform and useful load in the different application; (4) because present huge, the complicated operation of ground testing system, the subsystem joint-trial before the whole star test is difficult to utilize the ground testing system resource, the trouble diagnosibility when being badly in need of the small light trouble-shooter and improving important subsystem joint-trial.
The utility model content
The technology of the utility model is dealt with problems and is: overcome the deficiency of prior art, a kind of neural network moonlet intelligent trouble diagnosis device based on DSP is provided, be applicable to the fault diagnosis and the early warning of satellite ground test phases at different levels.
The technical solution of the utility model is: a kind of neural network moonlet intelligent trouble diagnosis device based on DSP comprises digital signal processor DSP, programmable logic device CPLD, memory cell, bus transceiver, serial communication modular; The threshold value of storage failure diagnosis BP neural network algorithm and Diagnostic parameters in the memory cell; Serial communication modular is gathered on-board equipment ground inspection output end signal and is delivered to digital signal processor DSP; Bus transceiver is gathered the parameter source code of being monitored on the satellite CAN bus and is delivered to digital signal processor DSP; Digital signal processor DSP converts analog quantity on the star into digital quantity signal through ADC; Arrange according to data layout; Obtain the parameter value that comprises in said digital quantity signal, parameter source code or the on-board equipment ground inspection output end signal; And with memory cell in the threshold value of the corresponding parameter of storing compare; When parameter value exceeds corresponding threshold, send fault pre-alarming through the I/O mouth, call simultaneously that fault diagnosis BP neural network algorithm in the memory cell obtains corresponding trouble spot and fault type is delivered to programmable logic device CPLD by digital signal processor DSP; Programmable logic device CPLD starts display, makes display export current trouble spot and fault type; When needs selected other subsystems to carry out fault diagnosis, programmable logic device CPLD started keyboard and keyboard input information is read in, and is uploaded to the failure diagnostic process that digital signal processor DSP carries out new argument.
The utility model advantage compared with prior art is:
(1) the utility model trouble-shooter utilizes the learning ability of neural network to the nonlinear model of multiple-input and multiple-output, can monitor simultaneously the various parameters of the many units of moonlet, is easy to be coupled judge and the complicated system-level malfunction of location performance;
(2) utilize the robustness of neural network and fault-tolerance can make fault diagnosis result more reliable, it can improve the speed of fault diagnosis to the parallel processing capability of multiple faults phenomenon, satisfies the requirement of real-time;
(3) neural network has extremely strong self-learning function, through simple study and training process, is easy to flexible Application in the moonlet test process of different application and configuration;
(4) made full use of that the DSP volume is little, advantages such as interface function is abundant, fast operation, be applicable on Ethernet, the star various media such as CAN bus communication net, be applicable to test occasions such as each subsystem test, whole star joint test simultaneously; The built-in AD modular converter of dsp chip can be used for expansion and gather the star analog signals, as the important references information of intelligent trouble diagnosis device, improves diagnosis real-time and spreadability.
Description of drawings
Fig. 1 is the structural drawing of the utility model trouble-shooter;
Fig. 2 is the workflow diagram of the utility model trouble-shooter.
Embodiment
As shown in Figure 1, the utility model trouble-shooter mainly comprises high speed digital signal processor DSP and peripherals thereof, programmable logic device CPLD, memory cell, bus transceiver, serial communication modular from the hardware.
1) core cell of this hardware is high speed digital signal processor DSP; DSP selects the TMS320F2812 of TI company for use; This DSP has adopted the Harvard structure of multibus; The bus operation sequential is divided into instruction fetch, instruction decode, fetch operand and the parallel processing of execution command four-stage, has greatly accelerated processing speed.This chip adopts the memory bank of program and data-carrier store unified addressing to organize form, and same storage space both can be mapped as the program space and also can be mapped as data space, for user's allocate memory provides very big dirigibility.It also provides external memory interface, can expand the many external memory storages of 1.5M.
In addition, the peripheral hardware resource of this DSP is also very abundant: two task manager module EVA and EVB, can handle time-event relevant with the time and external break events.This DSP also has 12 ADC (the minimum transition time is 60ns), serial communication interface (SCI) of 3 independent CPUs timers, local area network (LAN) CAN 2.0B bus controller, 16 passages or the like.IDE Code Composer Studio 2.2 that the exploitation of this DSP can utilize TI company to provide is for the user provides C/C++ program compiler, assembly routine, linker and based on the debugged program of Windows, used very convenient.
Simultaneously, constitute the minimum system of DSP jointly by 30Mhz crystal oscillator and power supply.Power supply chip is selected TPS73HD301 for use, is the power supply chip that TI company designs for DSP specially, takes into account the problem that solves 2812 chip internal electric sequences, has improved system stability.
2) complicated programmable logic device CPLD, CPLD selects the CPLDMAX7128 of altera corp for use, is mainly used in the chip select address decoding of each outside extended function module of realization and the input and output of logic function.Simultaneously, for alleviating the burden of core cell DSP, CPLD also accomplishes the control to Man Machine Interfaces such as LCDs, touch operation keyboards.
3) for realizing the extensibility of this trouble-shooter; To possess the ability that self-adaptation is used under variant satellite subsystem; Satisfy programming and upgrade requirement; Increase outer extension memory unit 256K RAM and 512K FLASH, be respectively the IS61LV25616 chip of Integrated Circuit Solution company and the MBM29LV800BA chip of FUJITSU company.
4) the main CAN bus that adopts of communication on the small satellite satellite, this trouble-shooter when participating in satellite test, will be connected on the star communication network with real-time reception star on each unit parameter of each subsystem, to be used for the real-time diagnosis fault.Differential transmission and receiving function for the CAN bus that realizes this device; Select CAN controller and the physical bus interface of the SN65HVD230 bus transceiver of PHILIPS Co. for use as DSP; Be connected with its built-in CAN controller module; This bus transceiver all has good transmitting-receiving ability for the CAN transmission of various speed, has stronger anti-wide region common mode interference, the ability of electromagnetic interference (EMI).
5) the moonlet on-board equipment has the ground inspection of having disposed the serial ports form greatly; Be convenient this trouble-shooter and the on-board equipment ground inspection port communications of using; The MAX232 or the MAX489E that select MAXIM for use are as serial communication modular; Directly link to each other with the built-in SCI serial communication modular of DSP2812, can realize the serial communication mode that baud rate is adjustable, utilize RS232 or RS422 communication standard and compunication.
6) DSP 2812 built-in 12 ADC; Can be used for gathering each the unit simulating signal that passes under the star catalogue; Like the gyro motor electric current of rail control subsystem etc., the ADC input front end is provided with signal conditioning circuits such as relevant isolation, filtering, to adapt to the voltage range of 2812ADC input end 0～3V.
System software partly comprises data processor, BP neural metwork training program, BP neural networks application programs, CAN capture program, serial ports capture program, analog acquisition program, early warning interrupt routine and keyboard and display routine.At the beginning of each program of operation, at first move initialization submodule separately, reading and saving according to this profile information, communicates detection and state-detection to each module at the configuration file of this locality, produces system operational parameters.
Handle the priori storehouse, extract the key parameter of each subsystem of moonlet and each unit in rail moonlet quality problems knowledge base and each subsystem and unit development and production knowledge base according to the moonlet fault countermeasure.BP neural metwork training program is the neural network input signal with each subsystem key parameter abnormal occurrence promptly; Fault type with the location is a neural network output signal; Set up four layers of fault diagnosis BP neural network (Fault Diagnosis BP Neural Network; FD-BPNN), carry out multisample study and training.FD-BPNN comprises input layer, the latent layer of ground floor, the latent layer of the second layer and output layer: wherein; Input layer is selected the log-sigmoid function for use with the transition function of latent layer; The output layer transition function is selected linear function for use, and training obtains suitable weights, accomplishes the foundation to fault model.
The weights and the input-output function of training the FD-BPNN that accomplishes are cured as neural networks application programs.This program after carrying out the debugging emulation success on the simulation test platform CCS of DSP, is write in the FLASH module of this device dsp system.
After device software starts; The CAN capture program is gathered the parameter source code of being monitored on the CAN bus through bus transceiver SN65HVD230; Data processor is arranged, reads the parameter value in the source code and is converted agreement physical unit dimension into according to data layout; Simultaneously, data processor reads corresponding parameters normality threshold table from FLASH, relatively back output abnormality parameter type and numerical value.
The unusual parameter information that neural networks application programs collects data processor utilizes the phenomenon of the failure of the FD-BPNN output interpretation of trained as the neural network input information.Get into the early warning interrupt routine this moment, start signal output I/O mouth makes the flicker of fault pre-alarming pilot lamp.
When test needs to gather simulating signal; Start the analog acquisition program; 12 ADCs built-in through DSP convert analog quantity on the star into digital quantity signal; The analog acquisition program converts digital signal into agreement physical unit dimension, from FLASH, reads analog parameter normality threshold table simultaneously, relatively back output abnormality parameter type and numerical value.The unusual parameter information that neural networks application programs collects data processor utilizes the phenomenon of the failure of the FD-BPNN output interpretation of trained as the neural network input information.Get into the early warning interrupt routine this moment, start signal output I/O mouth makes the flicker of fault pre-alarming pilot lamp.
In the time of when test, need gathering on-board equipment ground inspection output end signal; Start the serial ports capture program; Through MAX232 or MAX489E (selecting) according to the on-board equipment serial port protocol; Obtain parameter information on the star, from FLASH, read parameter normality threshold table simultaneously, relatively back output abnormality parameter type and numerical value.The unusual parameter information that neural networks application programs collects data processor utilizes the phenomenon of the failure of the FD-BPNN output interpretation of trained as the neural network input information.Get into the early warning interrupt routine this moment, start signal output I/O mouth makes the flicker of fault pre-alarming pilot lamp.
After fault took place, CPLDMAX7128 started keyboard and display routine, makes display export current fault unit and subsystem.Simultaneously, when needs select other subsystems to carry out fault diagnosis, carry out keyboard operation through display prompts; CPLDMAX7128 starts keyboard and display routine, and keyboard input information is read in, and is uploaded to DSP and generates configuration file and preservation automatically; Move relevant subsystem program according to the parameter that is provided with; Select to be provided with thereby accomplish subsystem, simultaneously, with the relevant painted demonstration of subsystem on the display.
Be that example is introduced the use of the utility model with the fault diagnosis of rail control subsystem, power subsystem, Star Service subsystem, camera subsystem respectively below.
1) rail control subsystem fault diagnosis:
A) hardware setting: at first, utilize keyboard operation to select the subsystem of rail control subsystem as current diagnosis, the reading displayed screen is confirmed to select successfully, and this moment, the rail control subsystem should be painted demonstration.Secondly, this intelligent trouble diagnosis device is inserted CAN bus network on the star, obtain the state telemetry parameter of rail control subsystem through the CAN bus.
When test needs to gather rail control subsystem simulating signal; The analog signal output of rail control subsystem central computer is connected with the ADC input end of intelligent trouble diagnosis device, to obtain the simulating signal of parts such as rail control subsystem gyro, infrared earth sensor.
In the time of when carrying out subsystem test, need gathering unit ground inspection output end signal,, mouth is examined on the ground of rail control subsystem unit be connected, to obtain momenttum wheel ground inspection signal with the MAX232 or the MAX489E interface of intelligent trouble diagnosis device like momenttum wheel.
B) software setting: with rail control subsystem key parameter abnormal occurrence is the neural network input signal; Fault type with the location is a neural network output signal; Set up FD-BPNN to the rail control subsystem, be cured as rail control subsystem neural networks application programs after the completion training.This program after carrying out the debugging emulation success on the simulation test platform CCS of DSP, is write among the FLASH of this device dsp system, accomplish software setting.
C) diagnostic procedure: rail control subsystem gyro powers up in the course of work; Gather the CAN bus through bus transceiver SN65HVD230 and attend institute's monitoring gyro motor telemetering of current value; Simultaneously, from FLASH, read motor current normality threshold in the normality threshold table, when this parameter exceeds setting threshold; Start I/O mouth output signal and make the fault pre-alarming pilot lamp glimmer, on liquid crystal display, show fault parameter corresponding equipment gyro and rail control subsystem through CPLD simultaneously.DSP is at this moment according to faulty equipment and subsystem information; Start rail control subsystem neural networks application programs; As the FD-BPNN input information, neural networks application programs is calculated through the FD-BPNN multilayer with gyro motor telemetering of current value, and gyro stall fault is orientated in output as.Thereafter DSP reads prestore among the FLASH suggestion processing scheme code and demonstration.According to the diagnostic result of intelligent trouble diagnosis device, the gyro head is detected, can verify diagnostic result.As be applied to other model moonlet rail control subsystems, and need reconfigure test parameter, can reinitialize setting to the intelligent trouble diagnosis device.
2) power subsystem fault diagnosis:
A) hardware setting: at first, utilize keyboard operation to select the subsystem of power subsystem as current diagnosis, the reading displayed screen is confirmed to select successfully, and this moment, power subsystem should be painted demonstration.Secondly, this intelligent trouble diagnosis device is inserted CAN bus network on the star, obtain the state telemetry parameter of power subsystem through the CAN bus.
When the power subsystem simulating signal need is gathered in test, the analog signal output of power subsystem is connected with the ADC input end of intelligent trouble diagnosis device, to obtain the simulating signal of parts such as power subsystem accumulator.
In the time of when carrying out subsystem test, need gathering unit ground inspection output end signal, mouth is examined on the ground of unit be connected, to obtain its ground inspection signal with the MAX232 or the MAX489E interface of intelligent trouble diagnosis device.
B) software setting: with power subsystem key parameter abnormal occurrence is the neural network input signal; Fault type with the location is a neural network output signal; Set up FD-BPNN to power subsystem, be cured as the power subsystem neural networks application programs after the completion training.This program after carrying out the debugging emulation success on the simulation test platform CCS of DSP, is write among the FLASH of this device dsp system, accomplish software setting.
C) diagnostic procedure: when the power subsystem accumulator is in charging process; Through bus transceiver SN65HVD230 gather on the CAN bus remote temperature sensing value when monitoring charge in batteries; Simultaneously, the temperature normality threshold is 20 ℃ when from FLASH, reading in the normality threshold table charge in batteries, when this parameter exceeds 20 ℃ of setting thresholds; Start I/O mouth output signal and make the fault pre-alarming pilot lamp glimmer, on liquid crystal display, show fault parameter corresponding equipment accumulator and power subsystem through CPLD simultaneously.DSP is at this moment according to faulty equipment and subsystem information; Start the power subsystem neural networks application programs; The remote temperature sensing value is as the FD-BPNN input information during with charge in batteries; Neural networks application programs is calculated through the FD-BPNN multilayer, and output is orientated as and effectively do not stopped charging after accumulator is full of electricity, causes and overcharges.Thereafter DSP reads prestore among the FLASH suggestion processing scheme code and demonstration.Diagnostic result according to the intelligent trouble diagnosis device detects accumulator, can verify diagnostic result.As be applied to other model moonlet power subsystems, and need reconfigure test parameter, can reinitialize setting to the intelligent trouble diagnosis device.
3) Star Service subsystem fault diagnosis:
A) hardware setting: at first, utilize keyboard operation to select the subsystem of Star Service subsystem as current diagnosis, the reading displayed screen is confirmed to select successfully, and this moment, the Star Service subsystem should be painted demonstration.Secondly, this intelligent trouble diagnosis device is inserted CAN bus network on the star, obtain the state telemetry parameter of Star Service subsystem through the CAN bus.
When Star Service subsystem simulating signal need is gathered in test, the analog signal output of Star Service subsystem is connected with the ADC input end of intelligent trouble diagnosis device, to obtain the simulating signal of Star Service subsystem parts.
In the time of when carrying out subsystem test, need gathering unit ground inspection output end signal, mouth is examined on the ground of unit be connected, to obtain its ground inspection signal with the MAX232 or the MAX489E interface of intelligent trouble diagnosis device.
B) software setting: with Star Service subsystem key parameter abnormal occurrence is the neural network input signal; Fault type with the location is a neural network output signal; Set up FD-BPNN to the Star Service subsystem, be cured as Star Service subsystem neural networks application programs after the completion training.This program after carrying out the debugging emulation success on the simulation test platform CCS of DSP, is write among the FLASH of this device dsp system, accomplish software setting.
C) diagnostic procedure: the Star Service subsystem begins accumulative total when working on power the back star; Through bus transceiver SN65HVD230 gather on the CAN bus remote measurement value when monitoring Star Service central computer master part star; Simultaneously, from FLASH, read normality threshold comparison in the normality threshold table, when accumulative total is chaotic when star; Start I/O mouth output signal and make the fault pre-alarming pilot lamp glimmer, on liquid crystal display, show fault parameter corresponding equipment Star Service central computer and Star Service subsystem through CPLD simultaneously.DSP is at this moment according to faulty equipment and subsystem information; Start Star Service subsystem neural networks application programs; The remote measurement value is as the FD-BPNN input information during with star, and neural networks application programs is calculated through the FD-BPNN multilayer, and Star Service central computer master part operation irregularity is orientated in output as.Thereafter DSP reads prestore among the FLASH suggestion processing scheme code and demonstration.According to the diagnostic result of intelligent trouble diagnosis device, the Star Service central computer is detected, can verify diagnostic result.As be applied to other model moonlet Star Service subsystems, and need reconfigure test parameter, can reinitialize setting to the intelligent trouble diagnosis device.
The content of not doing to describe in detail in the utility model instructions belongs to those skilled in the art's known technology.
Claims (6)
1. the neural network moonlet intelligent trouble diagnosis device based on DSP is characterized in that comprising: digital signal processor DSP, programmable logic device CPLD, memory cell, bus transceiver, serial communication modular; The threshold value of storage failure diagnosis BP neural network algorithm and Diagnostic parameters in the memory cell; Serial communication modular is gathered on-board equipment ground inspection output end signal and is delivered to digital signal processor DSP; Bus transceiver is gathered the parameter source code of being monitored on the satellite CAN bus and is delivered to digital signal processor DSP; Digital signal processor DSP converts analog quantity on the star into digital quantity signal through ADC; Arrange according to data layout; Obtain the parameter value that comprises in said digital quantity signal, parameter source code or the on-board equipment ground inspection output end signal; And with memory cell in the threshold value of the corresponding parameter of storing compare; When parameter value exceeds corresponding threshold, send fault pre-alarming through the I/O mouth, call simultaneously that fault diagnosis BP neural network algorithm in the memory cell obtains corresponding trouble spot and fault type is delivered to programmable logic device CPLD by digital signal processor DSP; Programmable logic device CPLD starts display, makes display export current trouble spot and fault type; When needs selected other subsystems to carry out fault diagnosis, programmable logic device CPLD started keyboard and keyboard input information is read in, and is uploaded to the failure diagnostic process that digital signal processor DSP carries out new argument.
2. a kind of neural network moonlet intelligent trouble diagnosis device based on DSP according to claim 1, it is characterized in that: described digital signal processor DSP is the TMS320F2812 of TI company.
3. a kind of neural network moonlet intelligent trouble diagnosis device based on DSP according to claim 1, it is characterized in that: described programmable logic device CPLD is the CPLDMAX7128 of altera corp.
4. a kind of neural network moonlet intelligent trouble diagnosis device based on DSP according to claim 1, it is characterized in that: described memory cell is the IS61LV25616 chip of Integrated Circuit Solution company and the MBM29LV800BA chip of FUJITSU company.
5. a kind of neural network moonlet intelligent trouble diagnosis device based on DSP according to claim 1, it is characterized in that: described bus transceiver is the SN65HVD230 of PHILIPS Co..
6. a kind of neural network moonlet intelligent trouble diagnosis device based on DSP according to claim 1, it is characterized in that: described serial communication modular is MAX232 or the MAX489E of MAXIM.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104216746A (en) * | 2014-08-30 | 2014-12-17 | 中国科学院长春光学精密机械与物理研究所 | Real-time monitoring and calibrating method for ground on-line programming of on-board equipment DSP (digital signal processor) program |
| CN104874175A (en) * | 2015-05-06 | 2015-09-02 | 东华大学 | Intelligent CS (counter strike) game costume system |
| CN106428649A (en) * | 2016-10-26 | 2017-02-22 | 中国运载火箭技术研究院 | Automatic support system for reusable carriers |
| CN106767801A (en) * | 2016-12-01 | 2017-05-31 | 北京航天时代光电科技有限公司 | A kind of highly reliable single shaft slack gyro is used to examining system |
| CN109934130A (en) * | 2019-02-28 | 2019-06-25 | 中国空间技术研究院 | The in-orbit real-time fault diagnosis method of satellite failure and system based on deep learning |
| CN110567485A (en) * | 2019-08-14 | 2019-12-13 | 北京控制工程研究所 | on-orbit autonomous fault diagnosis and repair method for multi-probe star sensor |
| CN110749790A (en) * | 2019-10-21 | 2020-02-04 | 中国科学院微小卫星创新研究院 | Comprehensive test fault positioning method |
| CN111516908A (en) * | 2020-02-26 | 2020-08-11 | 上海航天控制技术研究所 | Fault diagnosis method suitable for Mars detector propulsion system |
| CN111541474A (en) * | 2020-04-21 | 2020-08-14 | 中国电子科技集团公司第五十四研究所 | Health management system based on satellite mobile communication system ground gateway station |
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2011
- 2011-07-28 CN CN2011202718901U patent/CN202183018U/en not_active Expired - Lifetime
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| CN104216746A (en) * | 2014-08-30 | 2014-12-17 | 中国科学院长春光学精密机械与物理研究所 | Real-time monitoring and calibrating method for ground on-line programming of on-board equipment DSP (digital signal processor) program |
| CN104216746B (en) * | 2014-08-30 | 2017-06-09 | 中国科学院长春光学精密机械与物理研究所 | Monitor in real time and method of calibration of a kind of on-board equipment DSP programs ground in line writing |
| CN104874175A (en) * | 2015-05-06 | 2015-09-02 | 东华大学 | Intelligent CS (counter strike) game costume system |
| CN106428649A (en) * | 2016-10-26 | 2017-02-22 | 中国运载火箭技术研究院 | Automatic support system for reusable carriers |
| CN106428649B (en) * | 2016-10-26 | 2018-12-21 | 中国运载火箭技术研究院 | A kind of Control System for Reusable Launch Vehicle self -support system |
| CN106767801A (en) * | 2016-12-01 | 2017-05-31 | 北京航天时代光电科技有限公司 | A kind of highly reliable single shaft slack gyro is used to examining system |
| CN109934130A (en) * | 2019-02-28 | 2019-06-25 | 中国空间技术研究院 | The in-orbit real-time fault diagnosis method of satellite failure and system based on deep learning |
| CN110567485A (en) * | 2019-08-14 | 2019-12-13 | 北京控制工程研究所 | on-orbit autonomous fault diagnosis and repair method for multi-probe star sensor |
| CN110567485B (en) * | 2019-08-14 | 2021-04-13 | 北京控制工程研究所 | On-orbit autonomous fault diagnosis and repair method for multi-probe star sensor |
| CN110749790A (en) * | 2019-10-21 | 2020-02-04 | 中国科学院微小卫星创新研究院 | Comprehensive test fault positioning method |
| CN111516908A (en) * | 2020-02-26 | 2020-08-11 | 上海航天控制技术研究所 | Fault diagnosis method suitable for Mars detector propulsion system |
| CN111541474A (en) * | 2020-04-21 | 2020-08-14 | 中国电子科技集团公司第五十四研究所 | Health management system based on satellite mobile communication system ground gateway station |
| CN111541474B (en) * | 2020-04-21 | 2021-08-31 | 中国电子科技集团公司第五十四研究所 | Health management system based on satellite mobile communication system ground gateway station |
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