CN113916563A - Method and system for detecting health state of full-hydraulic steering system - Google Patents
Method and system for detecting health state of full-hydraulic steering system Download PDFInfo
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- 238000012544 monitoring process Methods 0.000 claims abstract description 23
- 230000002159 abnormal effect Effects 0.000 claims abstract description 20
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- 230000003862 health status Effects 0.000 claims description 9
- 239000003921 oil Substances 0.000 claims description 8
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Abstract
The invention discloses a method and a system for detecting the health state of a full hydraulic steering system. The health state detection method includes the steps of: s100: obtaining measured values of process parameters
And according to the measured value
Obtaining corresponding monitoring value
S200: comparing the monitored value of the process parameter to a threshold value
Converting into digital quantity; s300: the monitoring value for digital quantity
Fuzzification processing is carried out to obtain a health state parameter sign of the full hydraulic steering system; s400: and indicating the health state of the full hydraulic steering system according to the health state parameter sign of the full hydraulic steering system. The invention provides a method and a system for detecting the health state of a full-hydraulic steering system, which can monitor the health state of the full-hydraulic steering system in real time and further can feed back the health state of the full-hydraulic steering system in time when an abnormal condition is found.
Description
Technical Field
The invention relates to the technical field of vehicle detection, in particular to a method and a system for detecting the health state of a full hydraulic steering system.
Background
The unmanned port vehicle is a large engineering vehicle specially used for ports, generally has a rated load of more than 50 tons, and has the functions of remote control driving, crab walking, bidirectional driving and the like. If the port vehicle breaks down, the transfer efficiency and the overall dispatching of the port container can be greatly influenced, and even safety accidents occur. The steering system of the port vehicle usually adopts a full hydraulic steering system, but due to the inherent complexity of the full hydraulic steering system, almost 60 percent of faults of the port vehicle are generated by the full hydraulic steering system, and the existing port vehicle health management system feeds back the faults to a background after the faults occur, cannot give an alarm in time and prevents the faults from occurring.
Therefore, it is desirable to provide a method and a system for detecting the health status of a full hydraulic steering system to solve the above problems.
Disclosure of Invention
The invention aims to provide a method and a system for detecting the health state of a full-hydraulic steering system, which can monitor the health state of the full-hydraulic steering system in real time and further can feed back the health state in time when an abnormal condition is found.
In order to realize the purpose, the following technical scheme is provided:
a method for detecting the health state of a full hydraulic steering system comprises the following steps:
s100: obtaining measured values of process parameters
And according to the measured value
Obtaining corresponding monitoring value
The process parameters comprise wheel rotation angle theta output by a rotation angle sensor, inlet pressure P of the hydraulic steering oil cylinder output by a pressure sensor, temperature T of hydraulic oil output by a temperature sensor and inlet flow of the hydraulic steering oil cylinder output by a flow sensor
S200: comparing the monitored value of the process parameter to a threshold value
Converting into digital quantity;
s300: the monitoring value for digital quantity
Fuzzification processing is carried out to obtain a health state parameter sign of the full hydraulic steering system;
s400: and indicating the health state of the full hydraulic steering system according to the health state parameter sign of the full hydraulic steering system.
Further, step S100 specifically includes the following steps:
s101: collecting information of the rotation angle sensor, the pressure sensor, the temperature sensor and the flow sensor to obtain the measured value of the process parameter
S102: the measured values are chronologically combined
Stored in a data buffer of the sensor;
s103: using Kalman filtering method to measure said measured value
Filtering to obtain the monitoring value
Further, step S300 includes the steps of:
s301: storing the reference value r of each process parameter in a fuzzy knowledge base, comparing the reference value r with the corresponding process parameter after anti-fuzzy processing, and calculating to obtain an offset value e and an offset value change rate c of the process parameter relative to the reference value r;
s302: calculating the ratio | e |/r of the absolute value | e | of the deviation value e of all the process parameters to the reference value r, and judging whether | e |/r is larger than s, wherein s is a preset threshold of the ratio; if so, judging that the health state parameter sign of the full hydraulic steering system is 1, and judging that the full hydraulic steering system is abnormal; if not, continuing the next step.
Further, step S300 further includes the steps of:
s303: fuzzification processing and fuzzy reasoning are carried out on the deviation value e and the deviation value change rate c of a certain process parameter by using a fuzzy membership criterion in a fuzzy knowledge base to obtain a fuzzy value of the process parameter;
s304: performing anti-fuzzy processing on the fuzzy value of the process parameter to obtain an accurate value of the process parameter;
s305: obtaining the health degree of the process parameter according to the accurate value of the process parameter; the value range of the health degree is 0-100;
s306: repeating steps S303-S305 to obtain the health degree of each process parameter;
s307: weighting the health degrees of all the process parameters to obtain the health degree of the whole full hydraulic steering system;
if the value of the health degree of the full hydraulic steering system is more than 90, judging that the full hydraulic steering system is healthy when the health state parameter sign of the full hydraulic steering system is 0; and if the numerical value of the health degree of the full hydraulic steering system is less than 90, judging that the full hydraulic steering system is relatively healthy when the health state parameter sign of the full hydraulic steering system is-1.
Further, step S300 is accomplished using a fuzzy controller.
Further, step S400 is followed by the following steps:
and when the health state of the full hydraulic steering system is abnormal, storing the data of the process parameters in the abnormal state as the basis for system setting optimization.
Further, step S400 is followed by the following steps:
and when the health state of the full-hydraulic steering system is healthy or relatively healthy, storing the data of the process parameters in the health state or the relatively healthy state as sample data.
A health state detection system of a full hydraulic steering system for implementing any one of the above health state detection methods, comprising:
the signal acquisition and processing module is used for acquiring measured data of the corner sensor, the pressure sensor, the temperature sensor and the flow sensor and processing the data to obtain monitoring values of relevant process parameters;
the health state judgment module is used for receiving the monitoring values of the process parameters transmitted by the signal acquisition and processing module, and performing fuzzification processing to finally obtain a health state parameter sign of the full hydraulic steering system;
and the indication alarm module is used for receiving the health state parameters sign of the health state judgment module and carrying out state indication according to the health state parameters sign.
Further, the indication alarm module comprises an indicator light and an alarm; when the health state parameter sign of the full hydraulic steering system is 1, the indicator light is turned on, and the alarm gives an alarm; when the health state parameter sign of the full hydraulic steering system is 0, the indicator light is turned on; and when the health state parameter sign of the full hydraulic steering system is equal to-1, the indicator lamp turns on a yellow lamp.
Further, the health status detection system further includes:
the fault data storage module is used for storing the data of the process parameters in the abnormal state as the basis of system setting optimization when the health state of the full hydraulic steering system is abnormal;
and the health data storage module is used for storing the data of the process parameters in the health state or the relative health state as sample data when the health state of the full hydraulic steering system is healthy or relatively healthy.
Compared with the prior art, the invention has the beneficial effects that:
the invention relates to a method and a system for detecting the health state of a full hydraulic steering system, which are characterized in that the rotation angle theta of wheels, the inlet pressure P of a hydraulic steering oil cylinder, the temperature T of hydraulic oil and the inlet flow of the hydraulic steering oil cylinder are acquired
The health state of the full hydraulic steering system can be comprehensively predicted and evaluated by the aid of the four process parameters; in the method, the health state of the full-hydraulic steering system is represented by a health state parameter sign, namely different health states are indicated according to different health state parameters sign, so that the health state of the full-hydraulic steering system can be timely fed back to a remote monitoring person or a dispatching person, and therefore, the health state of the full-hydraulic steering system can be timely fed back to the remote monitoring person or the dispatching personThe barrier can give an alarm in time, and the potential risk can be checked according to the indication, so that the occurrence of safety accidents is reduced, and the efficiency of transporting the container is improved.
Drawings
FIG. 1 is a general step diagram of a method for detecting the state of health of a fully hydraulic steering system in an embodiment of the present invention;
fig. 2 is a detailed step diagram of step S100 in the method for detecting the state of health of the full hydraulic steering system according to the embodiment of the present invention;
fig. 3 is a detailed step diagram of step S300 in the method for detecting the state of health of the full hydraulic steering system according to the embodiment of the present invention;
fig. 4 is a block diagram of a flow of fuzzification processing in a method for detecting a state of health of a full hydraulic steering system according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a state of health detection system of the full hydraulic steering system in the embodiment of the present invention.
Detailed Description
The following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment provides a method for detecting the health state of a full-hydraulic steering system, which is mainly used in the field of unmanned port vehicles, and can monitor the health state of the full-hydraulic steering system of the unmanned port vehicle in real time, so that the health state can be fed back in time when an abnormal condition is found. Specifically, referring to fig. 1, the health status detection method includes the steps of:
s100: obtaining measured values of process parameters
And based on the measured value
Obtain correspondingMonitoring value
The process parameters comprise wheel rotation angle theta output by a rotation angle sensor, inlet pressure P of the hydraulic steering oil cylinder output by a pressure sensor, temperature T of hydraulic oil output by a temperature sensor and inlet flow of the hydraulic steering oil cylinder output by a flow sensor
S200: monitoring values of process parameters
Converting into digital quantity;
s300: monitoring value of digital quantity
Fuzzification processing is carried out to obtain a health state parameter sign of the full hydraulic steering system;
s400: and indicating the health state of the full hydraulic steering system according to the health state parameter sign of the full hydraulic steering system.
Wherein the content of the first and second substances,
a collection of four data is represented.
The method for detecting the health state of the full hydraulic steering system provided by this embodiment collects the wheel rotation angle θ, the inlet pressure P of the hydraulic steering cylinder, the temperature T of the hydraulic oil, and the inlet flow rate of the hydraulic steering cylinder
The health state of the full hydraulic steering system can be comprehensively predicted and evaluated by the aid of the four process parameters; in the method, the health state of the full hydraulic steering system is represented by a health state parameter sign, namely different health states are indicated according to different health state parameters sign, so that the health state of the full hydraulic steering system can be timely fed back to a remote placeThe monitoring personnel or the dispatching personnel can give an alarm in time when a fault occurs, and can check potential risks according to the indication, thereby reducing the occurrence of safety accidents and improving the efficiency of transporting containers.
The step S300 of the related fuzzification processing is completed by using a fuzzy controller, so that the step S200 is required to convert the monitoring values of the process parameters into digital values before inputting the digital values into the fuzzy controller.
Specifically, referring to fig. 2, step S100 specifically includes the following steps:
s101: collecting information of the rotation angle sensor, the pressure sensor, the temperature sensor and the flow sensor to obtain the measured value of the process parameter
S102: measured values are measured in time sequence
Stored in a data buffer of the sensor;
s103: using Kalman filtering method to measure actual value
Filtering to obtain monitoring value
Steps S101 to S103 detail how the corresponding monitored values are obtained from the measured values of the process parameters. Firstly, the measured values obtained by all the sensors are uniformly stored in an additionally arranged but shared sensor for data buffering, then the measured values are filtered by a Kalman filtering method to obtain monitoring values, the uncertainty caused by the data noise of the sensors is effectively processed, and the numerical value finally entering the fuzzy controller is the optimal estimation of the real data.
Further, referring to fig. 3 and 4, the step S300 specifically includes the following steps:
s301: storing the reference value r of each process parameter in a fuzzy knowledge base, comparing the reference value r with the corresponding process parameter after anti-fuzzy processing, and calculating to obtain an offset value e and an offset value change rate c of the process parameter relative to the reference value r;
s302: calculating the ratio | e |/r of the absolute value | e | of the deviation value e of all the process parameters to the reference value r, and judging whether | e |/r is larger than s; if so, judging that the health state parameter sign of the full hydraulic steering system is 1, and judging that the full hydraulic steering system is abnormal; if not, continuing the next step.
The above steps provide how to take the value of the health state parameter sign of the full hydraulic steering system, that is, the absolute value | e | of the deviation value e of the process parameter is compared with the reference value r to obtain a ratio, and the ratio is compared with the preset threshold value s of the ratio, when the ratios | e |/r of all the process parameters are greater than s, it means that the data of the selected four process parameters of the full hydraulic steering system at the time have larger deviation relative to the data under the system health state, at the time, the state of the full hydraulic steering system can be determined to be abnormal, that is, the full hydraulic steering system is no longer healthy, and the value of the health state parameter sign of the full hydraulic steering system under the abnormal state is 1. When only the data of individual process parameters in the four process parameters of the full hydraulic steering system have large deviation with the data of the health state, but not all the data of the individual process parameters are subjected to subsequent steps. Namely, step S300 further includes the steps of:
s303: fuzzification processing and fuzzy reasoning are carried out on the deviation value e and the deviation value change rate c of a certain process parameter by using a fuzzy membership criterion in a fuzzy knowledge base to obtain a fuzzy value of the process parameter;
s304: performing anti-fuzzy processing on the fuzzy health value of the process parameter to obtain a standard value of the process parameter;
s305: obtaining the health degree of the process parameter according to the accurate value of the process parameter; the value range of the health degree is 0-100;
s306: repeating the steps S303-S305 to obtain the health degree of each process parameter;
s307: and weighting the health degrees of all the process parameters to obtain the overall health degree of the full hydraulic steering system.
The accurate value of the process parameter is obtained through fuzzification, and when the full hydraulic steering system works in a healthy state, the data value of the relevant process parameter has a reference range, so that the accurate value of the process parameter obtained through fuzzification is compared with the reference range, the health degree of the process parameter can be obtained, the closer to the reference range, the larger the health degree value of the process parameter is, and the farther from the reference range, the smaller the health degree value of the process parameter is. The health state of the full hydraulic steering system is measured by four process parameters, and the health influence of each process parameter on the full hydraulic steering system is necessarily the same, namely different process parameters have different weights in the method for influencing the health state of the full hydraulic steering system, so the health degree of each process parameter needs to be calculated, and the health degrees of all the process parameters are weighted to obtain the overall health degree of the full hydraulic steering system. Because the fuzzy controller has better robustness, when the fuzzy processor is adopted to carry out on-line evaluation on the process parameters, even if the instantaneous error of individual data is larger, the fuzzy controller still has quick adaptability to the characteristic change of an evaluated object, and then more accurate data is obtained, so that the real health state of the system can be reflected.
After the integral health degree of the full-hydraulic steering system is obtained, the health state can be judged; the method comprises the following specific steps: if the value of the health degree of the full-hydraulic steering system is more than 90, judging that the full-hydraulic steering system is healthy when a health state parameter sign of the full-hydraulic steering system is 0; and if the value of the health degree of the full-hydraulic steering system is less than 90, judging that the full-hydraulic steering system is relatively healthy when the health state parameter sign of the full-hydraulic steering system is-1.
Further, in order to further improve the whole health state detection method and conveniently provide reference for the analysis and optimization of the full hydraulic steering system for the following workers, after the step S400 is completed, the following steps can be further provided: and when the health state of the full hydraulic steering system is abnormal, storing the data of the process parameters in the abnormal state as the basis for setting and optimizing the system. And/or when the health state of the full hydraulic steering system is healthy or relatively healthy, storing the data of the process parameters in the health state or the relatively healthy state as sample data.
The embodiment also provides a health state detection system of the full hydraulic steering system, which is mainly used for realizing the health state detection method. Specifically, referring to fig. 5, the health status detection system includes:
the signal acquisition and processing module is used for acquiring measured data of the corner sensor, the pressure sensor, the temperature sensor and the flow sensor and processing the data to obtain monitoring values of relevant process parameters;
the health state judgment module is used for receiving the monitoring values of the process parameters transmitted by the signal acquisition and processing module, performing fuzzification processing and finally obtaining a health state parameter sign of the full hydraulic steering system;
and the indication alarm module is used for receiving the health state parameter sign of the health state judgment module and carrying out state indication according to the health state parameter sign.
Further, the health status detection system further comprises:
the fault data storage module is used for storing the data of the process parameters in the abnormal state as the basis of system setting optimization when the health state of the full hydraulic steering system is abnormal;
and the health data storage module is used for storing the data of the process parameters in the health state or the relative health state as sample data when the health state of the full hydraulic steering system is healthy or relatively healthy.
Specifically, the indication alarm module comprises an indicator light and an alarm; when the health state parameter sign of the full hydraulic steering system is 1, the indicator light is on, and the alarm gives an alarm; when the health state parameter sign of the full hydraulic steering system is equal to 0, the indicator light is turned on; and when the health state parameter sign of the full hydraulic steering system is equal to-1, the indicator lamp lights a yellow lamp. The setting of the indicator light and the alarm enables the health state of the full hydraulic steering system to be displayed more visually and has a warning function; if the indicator light is red and an alarm is given, remote monitoring or dispatching personnel can be remarkably reminded to stop the vehicle for inspection in time, so that loss in a larger range is avoided; when the indicator light is yellow, the device can be reminded to perform equipment troubleshooting after the work is finished, and possible potential safety hazards can be timely discharged.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A method for detecting the health state of a full hydraulic steering system is characterized by comprising the following steps:
s100: obtaining measured values of process parameters
And according to the measured value
Obtaining corresponding monitoring value
The process parameters comprise wheel rotation angle theta output by a rotation angle sensor, inlet pressure P of the hydraulic steering oil cylinder output by a pressure sensor, temperature T of hydraulic oil output by a temperature sensor and inlet flow of the hydraulic steering oil cylinder output by a flow sensor
S200: comparing the monitored value of the process parameter to a threshold value
Converting into digital quantity;
s300: the monitoring value for digital quantity
Fuzzification processing is carried out to obtain a health state parameter sign of the full hydraulic steering system;
s400: and indicating the health state of the full hydraulic steering system according to the health state parameter sign of the full hydraulic steering system.
2. The method according to claim 1, wherein step S100 specifically comprises the steps of:
s101: collecting information of the rotation angle sensor, the pressure sensor, the temperature sensor and the flow sensor to obtain the measured value of the process parameter
S102: the measured values are chronologically combined
Stored in a data buffer of the sensor;
s103: using Kalman filtering method to measure said measured value
Filtering to obtain the monitoring value
3. The health status detection method according to claim 1, wherein the step S300 comprises the steps of:
s301: storing the reference value r of each process parameter in a fuzzy knowledge base, comparing the reference value r with the corresponding process parameter after anti-fuzzy processing, and calculating to obtain an offset value e and an offset value change rate c of the process parameter relative to the reference value r;
s302: calculating the ratio | e |/r of the absolute value | e | of the deviation value e of all the process parameters to the reference value r, and judging whether | e |/r is larger than s, wherein s is a preset threshold of the ratio; if so, judging that the health state parameter sign of the full hydraulic steering system is 1, and judging that the full hydraulic steering system is abnormal; if not, continuing the next step.
4. The health status detection method according to claim 3, wherein the step S300 further comprises the steps of:
s303: fuzzification processing and fuzzy reasoning are carried out on the deviation value e and the deviation value change rate c of a certain process parameter by using a fuzzy membership criterion in a fuzzy knowledge base to obtain a fuzzy value of the process parameter;
s304: performing anti-fuzzy processing on the fuzzy value of the process parameter to obtain an accurate value of the process parameter;
s305: obtaining the health degree of the process parameter according to the accurate value of the process parameter; the value range of the health degree is 0-100;
s306: repeating steps S303-S305 to obtain the health degree of each process parameter;
s307: weighting the health degrees of all the process parameters to obtain the health degree of the whole full hydraulic steering system;
if the value of the health degree of the full hydraulic steering system is more than 90, judging that the full hydraulic steering system is healthy when the health state parameter sign of the full hydraulic steering system is 0; and if the numerical value of the health degree of the full hydraulic steering system is less than 90, judging that the full hydraulic steering system is relatively healthy when the health state parameter sign of the full hydraulic steering system is-1.
5. The method according to claim 1, wherein step S300 is performed by a fuzzy controller.
6. The health status detection method according to claim 1, further comprising the following steps after step S400:
and when the health state of the full hydraulic steering system is abnormal, storing the data of the process parameters in the abnormal state as the basis for system setting optimization.
7. The health status detection method according to claim 1, further comprising the following steps after step S400:
and when the health state of the full-hydraulic steering system is healthy or relatively healthy, storing the data of the process parameters in the health state or the relatively healthy state as sample data.
8. A state of health detection system of an all-hydraulic steering system for implementing the state of health detection method according to any one of claims 1 to 7, comprising:
the signal acquisition and processing module is used for acquiring measured data of the corner sensor, the pressure sensor, the temperature sensor and the flow sensor and processing the data to obtain monitoring values of relevant process parameters;
the health state judgment module is used for receiving the monitoring values of the process parameters transmitted by the signal acquisition and processing module, and performing fuzzification processing to finally obtain a health state parameter sign of the full hydraulic steering system;
and the indication alarm module is used for receiving the health state parameters sign of the health state judgment module and carrying out state indication according to the health state parameters sign.
9. The system for detecting the state of health of a full hydraulic steering system according to claim 8, wherein the indication alarm module comprises an indicator light and an alarm; when the health state parameter sign of the full hydraulic steering system is 1, the indicator light is turned on, and the alarm gives an alarm; when the health state parameter sign of the full hydraulic steering system is 0, the indicator light is turned on; and when the health state parameter sign of the full hydraulic steering system is equal to-1, the indicator lamp turns on a yellow lamp.
10. The system for detecting the state of health of an all-hydraulic steering system according to claim 8, characterized by further comprising:
the fault data storage module is used for storing the data of the process parameters in the abnormal state as the basis of system setting optimization when the health state of the full hydraulic steering system is abnormal;
and the health data storage module is used for storing the data of the process parameters in the health state or the relative health state as sample data when the health state of the full hydraulic steering system is healthy or relatively healthy.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130082833A1 (en) * | 2011-09-30 | 2013-04-04 | General Electric Company | System and method for monitoring health of airfoils |
| CN105096053A (en) * | 2015-08-14 | 2015-11-25 | 哈尔滨工业大学 | Health management decision-making method suitable for complex process system |
| KR20170079271A (en) * | 2015-12-30 | 2017-07-10 | 한국전자통신연구원 | Health estimation based fault-tolerant apparatus and method thereof |
| CN107576920A (en) * | 2017-08-02 | 2018-01-12 | 北京空间飞行器总体设计部 | A kind of satellite power supply health degree computational methods based on fuzzy multi-attribute evaluation |
| CN109934453A (en) * | 2019-01-24 | 2019-06-25 | 新奥数能科技有限公司 | The determination method and device of equipment Health Category, storage medium, electronic device |
| US20200116553A1 (en) * | 2018-10-12 | 2020-04-16 | Industrial Technology Research Institute | Health monitor method for an equipment and system thereof |
| CN111650917A (en) * | 2020-05-14 | 2020-09-11 | 中铁第四勘察设计院集团有限公司 | Multi-dimensional state online monitoring method and system for equipment |
| AU2020103881A4 (en) * | 2020-12-03 | 2021-02-11 | Zhimin Li | A Diagnosis Method of Train Body Assembly Deviation in the Manufacturing Process of High-Speed Train |
| CN113063611A (en) * | 2021-03-15 | 2021-07-02 | 深圳市创捷科技有限公司 | Equipment monitoring management method and system |
-
2021
- 2021-09-29 CN CN202111149194.8A patent/CN113916563B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130082833A1 (en) * | 2011-09-30 | 2013-04-04 | General Electric Company | System and method for monitoring health of airfoils |
| CN105096053A (en) * | 2015-08-14 | 2015-11-25 | 哈尔滨工业大学 | Health management decision-making method suitable for complex process system |
| KR20170079271A (en) * | 2015-12-30 | 2017-07-10 | 한국전자통신연구원 | Health estimation based fault-tolerant apparatus and method thereof |
| CN107576920A (en) * | 2017-08-02 | 2018-01-12 | 北京空间飞行器总体设计部 | A kind of satellite power supply health degree computational methods based on fuzzy multi-attribute evaluation |
| US20200116553A1 (en) * | 2018-10-12 | 2020-04-16 | Industrial Technology Research Institute | Health monitor method for an equipment and system thereof |
| CN109934453A (en) * | 2019-01-24 | 2019-06-25 | 新奥数能科技有限公司 | The determination method and device of equipment Health Category, storage medium, electronic device |
| CN111650917A (en) * | 2020-05-14 | 2020-09-11 | 中铁第四勘察设计院集团有限公司 | Multi-dimensional state online monitoring method and system for equipment |
| AU2020103881A4 (en) * | 2020-12-03 | 2021-02-11 | Zhimin Li | A Diagnosis Method of Train Body Assembly Deviation in the Manufacturing Process of High-Speed Train |
| CN113063611A (en) * | 2021-03-15 | 2021-07-02 | 深圳市创捷科技有限公司 | Equipment monitoring management method and system |
Non-Patent Citations (2)
| Title |
|---|
| 王凌霄: "光伏发电系统的故障诊断与健康监测技术研究", 《CNKI硕士电子期刊》 * |
| 陈伟;孙阳;: "发射车液压系统状态监测与故障诊断方法研究", 海军航空工程学院学报, no. 01, pages 15 - 19 * |
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