CN111709453B - Online fault diagnosis method for electrical system of aircraft engine - Google Patents
Online fault diagnosis method for electrical system of aircraft engine Download PDFInfo
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- CN111709453B CN111709453B CN202010439955.2A CN202010439955A CN111709453B CN 111709453 B CN111709453 B CN 111709453B CN 202010439955 A CN202010439955 A CN 202010439955A CN 111709453 B CN111709453 B CN 111709453B
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- G06F18/20—Analysing
- G06F18/24—Classification techniques
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
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- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
Abstract
The invention discloses an online fault diagnosis method for an electrical system of an aero-engine, which belongs to the technical field of aero-engines and is characterized by comprising the following steps: a. collecting and extracting characteristic values through a distributed collecting and analyzing unit; b. storing the characteristic value information of the electrical accessories under static and dynamic conditions into a standard database through a centralized fault interpretation and health management center; c. carrying out denoising treatment through wavelet transformation; d. acquiring knowledge through an expert system, and performing information interaction with a knowledge database; e. and fault diagnosis is carried out by combining an expert system, data analysis and a centralized fault interpretation and health management center of a database. The invention can accurately position the fault type, save the cost of human resources, reduce the economic loss caused by frequent driving and troubleshooting of the engine, greatly shorten the troubleshooting period, and better ensure the safety and the reliability of the use of the engine through fault prediction and health management measures.
Description
Technical Field
The invention relates to the technical field of aero-engines, in particular to an on-line fault diagnosis method for an aero-engine electrical system.
Background
Due to the diversification of the types of the aero-engines, the traditional engine electrical system detection method cannot meet the requirements of the aero-engines on efficient and high-accuracy fault judgment and health management of electrical system fault diagnosis.
The aircraft engine electrical system is used as a key component of an aircraft power system, and is important for monitoring, fault prediction and health management in daily use. However, due to the large variety and number of sensors and valves involved in the electrical system of the engine, the electrical circuit is also extremely complex, which makes the diagnosis of faults in the electrical system in daily use and regular maintenance and inspection of the engine rather troublesome.
Common faults of an electrical system include sensor faults, valve faults, line short-circuit faults and open-circuit faults, although the engine can realize primary self-checking on all electrical lines after adopting a full-authority digital electronic controller, accurate positioning of the faults cannot be realized. Therefore, the detection mode has certain limitation, and faults can only be checked item by item in a manual mode. If the electric system has faults in the engine test run, the engine is stopped and manual inspection is carried out point by point, so that the inspection mode consumes time and a large amount of human resources, and certain potential safety hazards exist.
Chinese patent publication No. CN 108303262a, whose publication date is 2018, 07/20, discloses an on-line monitoring and fault diagnosis system for an automobile engine, which comprises an automobile engine, the system of the automobile engine is composed of a state monitoring system and a fault diagnosis system, the state monitoring system comprises a signal measuring module, a signal processing module, a data collecting module and an industrial control computer, the automobile engine is electrically connected with the signal measuring module, the signal processing module, the data collecting module and the industrial control computer in sequence through electric conductors, the signal measuring module is composed of a piezoelectric acceleration sensor, a hall element type rotation speed sensor, a thermal resistance sensor, a resistance strain type pressure sensor and a liquid level sensor, the piezoelectric acceleration sensor is installed on an engine cylinder cover, the hall element type rotation speed sensor is installed on a flywheel shell, the industrial control computer is connected with the fault diagnosis system through an electric conductor, the fault diagnosis system comprises a real-time monitoring module, an alarm module, a fault diagnosis module, a fault prediction module, a self-learning module and a printing module, the real-time monitoring module is respectively and electrically connected with the alarm module, the fault prediction module, the printing module and the self-learning module through the electric conductor, and the alarm module is electrically connected with the fault diagnosis module through the electric conductor.
Although the online monitoring and fault diagnosis system for the automobile engine disclosed in this patent document systematically analyzes the engine fault by using the fuzzy control and the expert system, the cause of the fault can be found out. However, the fault type cannot be accurately positioned, and the problems of long troubleshooting period and low troubleshooting efficiency exist.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an online fault diagnosis method for an electric system of an aircraft engine.
The invention is realized by the following technical scheme:
an online fault diagnosis method for an aircraft engine electrical system is characterized by comprising the following steps:
a. the aviation plug is connected with an electric accessory of the engine, various signals are separated through the transfer plug, and the signals are collected and extracted through the distributed collection and analysis unit;
b. storing the characteristic value information of the electrical accessories under static and dynamic conditions into a standard database through a centralized fault interpretation and health management center;
c. measuring the information of the electrical accessory by using an I/O module through a control circuit, a driving circuit and an acquisition circuit, extracting and analyzing the characteristic value of the acquired information, and performing noise reduction processing through wavelet transformation;
d. acquiring knowledge through an expert system, performing information interaction with a knowledge database, and explaining characteristic value information of electrical accessories of various engines in different working modes;
e. and when the standard characteristic value is deviated and the fault critical value is not exceeded, the electric accessory is diagnosed to be in a sub-health state, and when the standard characteristic value is deviated and the fault critical value is exceeded, the electric accessory is diagnosed to be in a fault.
The electrical accessory comprises a rotating speed sensor, a temperature sensor, a vibration sensor, an angular displacement sensor, a pressure sensor, an ion flame detector, a metal scrap annunciator, an oxygen supplement ignition device, an ignition electric nozzle, a surge annunciator, an electromagnetic valve and a valve displacement measuring device.
In the step b, the standard database is formed by acquiring initial characteristic parameters of the electrical accessories in a static mode and recording the initial characteristic parameters into the database when the engine is not driven, and writing dynamic characteristic values of various sensors into the database when the electrical accessories are normal by combining the driving of the engine.
In the step e, diagnosing the fault of the electrical accessory specifically means establishing a fuzzy domain value of fault characteristics, and establishing a fault isolation equation:
wherein, λ is the number of test points, and λ is 20;
after an engine is installed on the airplane, the electric accessory of the pth engine is subjected to power-on inspection for the first time and a normal voltage value recorded when the engine is started for the first time; vk(Fp) Indicating failure mode F of electrical accessory or linepA node voltage value of time;
if any one of the test node voltages deviates from the normal value, the inequality of the equation 1 is established, and a fault point is formed.
In the step e, diagnosing the fault of the electrical accessory specifically refers to establishing a correlation coefficient C of the temperature sensor to be diagnosedj(Ai)：
Wherein x isoijConverting the standard parameter value of the temperature sensor in normal operation into a voltage value and taking the voltage value as a standard characteristic value; e.g. of the typeijFor the upper limit of the fluctuation range, t, of the monitored sensor parameter in normal operationijAlpha is a correction factor for the limit deviation of the monitored temperature sensor; x is the number ofjTemperature sensor values collected in real time;
the temperature sensor j is aligned with the target pattern aiThe basic probability distribution of (c) is:
wherein N iscFor the number of target patterns, take Nc2; n is the total number of sensors, and N is 10; wjIs a weighting coefficient of the temperature sensor, and is Wj1 is ═ 1; for the coefficient alpha in different modesj、βj、RjRespectively obtained by calculation of formula 4-formula 6;
αj＝max{Cj(Aj)} i＝1，2，…Ncformula 4
Combining an engine electrical system database, realizing fault interpretation and health management through an expert system, and obtaining F through data analysis and processingpAnd mjSequenced and stored in an electrical database.
The beneficial effects of the invention are mainly shown in the following aspects:
1. the invention combines an expert system, data analysis and a centralized fault interpretation and health management center of a database, thereby realizing that the fault condition of the electrical accessory can be directly judged when the engine is started, and the visual maintenance and replacement of the electrical accessory which deviates from the standard characteristic value but does not exceed the fault critical value and shows that the electrical accessory is in the sub-health state are realized; for the fault deviating from the standard characteristic value and exceeding the fault critical value, the system directly positions the fault without the need of checking the fault through flight parameter interpretation or frequent trial run, thereby greatly saving the cost of economic and human resources; compared with the prior art, the method can accurately position the fault type, save the cost of human resources, reduce the economic loss caused by frequent driving and troubleshooting of the engine, greatly shorten the troubleshooting period, and better ensure the safety and the reliability of the use of the engine through fault prediction and health management measures.
2. According to the invention, the automatic positioning of the electric system fault is realized by utilizing the characteristic parameter extraction, analysis and data analysis of different types of sensors, special electric accessories and electromagnetic valve valves in the aircraft engine, and the fault prediction of the electric accessories can be realized.
Drawings
The invention will be further described in detail with reference to the drawings and the detailed description, wherein:
FIG. 1 is an overall system framework of the present invention;
FIG. 2 is a schematic diagram of an electrical fault system for an engine according to the present invention.
Detailed Description
Example 1
Referring to fig. 1 and 2, an online fault diagnosis method for an aircraft engine electrical system comprises the following steps:
a. the aviation plug is connected with an electric accessory of the engine, various signals are separated through the transfer plug, and the signals are collected and extracted through the distributed collection and analysis unit;
b. storing the characteristic value information of the electrical accessories under static and dynamic conditions into a standard database through a centralized fault interpretation and health management center;
c. measuring the information of the electrical accessory by using an I/O module through a control circuit, a driving circuit and an acquisition circuit, extracting and analyzing the characteristic value of the acquired information, and performing noise reduction processing through wavelet transformation;
d. acquiring knowledge through an expert system, performing information interaction with a knowledge database, and explaining characteristic value information of electrical accessories of various engines in different working modes;
e. and when the standard characteristic value is deviated and the fault critical value is not exceeded, the electric accessory is diagnosed to be in a sub-health state, and when the standard characteristic value is deviated and the fault critical value is exceeded, the electric accessory is diagnosed to be in a fault.
Example 2
Referring to fig. 1 and 2, an online fault diagnosis method for an aircraft engine electrical system comprises the following steps:
a. the aviation plug is connected with an electric accessory of the engine, various signals are separated through the transfer plug, and the signals are collected and extracted through the distributed collection and analysis unit;
b. storing the characteristic value information of the electrical accessories under static and dynamic conditions into a standard database through a centralized fault interpretation and health management center;
c. measuring the information of the electrical accessory by using an I/O module through a control circuit, a driving circuit and an acquisition circuit, extracting and analyzing the characteristic value of the acquired information, and performing noise reduction processing through wavelet transformation;
d. acquiring knowledge through an expert system, performing information interaction with a knowledge database, and explaining characteristic value information of electrical accessories of various engines in different working modes;
e. and when the standard characteristic value is deviated and the fault critical value is not exceeded, the electric accessory is diagnosed to be in a sub-health state, and when the standard characteristic value is deviated and the fault critical value is exceeded, the electric accessory is diagnosed to be in a fault.
The electrical accessory comprises a rotating speed sensor, a temperature sensor, a vibration sensor, an angular displacement sensor, a pressure sensor, an ion flame detector, a metal scrap annunciator, an oxygen supplement ignition device, an ignition electric nozzle, a surge annunciator, an electromagnetic valve and a valve displacement measuring device.
Example 3
Referring to fig. 1 and 2, an online fault diagnosis method for an aircraft engine electrical system comprises the following steps:
a. the aviation plug is connected with an electric accessory of the engine, various signals are separated through the transfer plug, and the signals are collected and extracted through the distributed collection and analysis unit;
b. storing the characteristic value information of the electrical accessories under static and dynamic conditions into a standard database through a centralized fault interpretation and health management center;
c. measuring the information of the electrical accessory by using an I/O module through a control circuit, a driving circuit and an acquisition circuit, extracting and analyzing the characteristic value of the acquired information, and performing noise reduction processing through wavelet transformation;
d. acquiring knowledge through an expert system, performing information interaction with a knowledge database, and explaining characteristic value information of electrical accessories of various engines in different working modes;
e. and when the standard characteristic value is deviated and the fault critical value is not exceeded, the electric accessory is diagnosed to be in a sub-health state, and when the standard characteristic value is deviated and the fault critical value is exceeded, the electric accessory is diagnosed to be in a fault.
The electrical accessory comprises a rotating speed sensor, a temperature sensor, a vibration sensor, an angular displacement sensor, a pressure sensor, an ion flame detector, a metal scrap annunciator, an oxygen supplement ignition device, an ignition electric nozzle, a surge annunciator, an electromagnetic valve and a valve displacement measuring device.
In the step b, the standard database is formed by acquiring initial characteristic parameters of the electrical accessories in a static mode and recording the initial characteristic parameters into the database when the engine is not driven, and writing dynamic characteristic values of various sensors into the database when the electrical accessories are normal by combining the driving of the engine.
Example 4
Referring to fig. 1 and 2, an online fault diagnosis method for an aircraft engine electrical system comprises the following steps:
a. the aviation plug is connected with an electric accessory of the engine, various signals are separated through the transfer plug, and the signals are collected and extracted through the distributed collection and analysis unit;
b. storing the characteristic value information of the electrical accessories under static and dynamic conditions into a standard database through a centralized fault interpretation and health management center;
c. measuring the information of the electrical accessory by using an I/O module through a control circuit, a driving circuit and an acquisition circuit, extracting and analyzing the characteristic value of the acquired information, and performing noise reduction processing through wavelet transformation;
d. acquiring knowledge through an expert system, performing information interaction with a knowledge database, and explaining characteristic value information of electrical accessories of various engines in different working modes;
e. and when the standard characteristic value is deviated and the fault critical value is not exceeded, the electric accessory is diagnosed to be in a sub-health state, and when the standard characteristic value is deviated and the fault critical value is exceeded, the electric accessory is diagnosed to be in a fault.
The electrical accessory comprises a rotating speed sensor, a temperature sensor, a vibration sensor, an angular displacement sensor, a pressure sensor, an ion flame detector, a metal scrap annunciator, an oxygen supplement ignition device, an ignition electric nozzle, a surge annunciator, an electromagnetic valve and a valve displacement measuring device.
In the step b, the standard database is formed by acquiring initial characteristic parameters of the electrical accessories in a static mode and recording the initial characteristic parameters into the database when the engine is not driven, and writing dynamic characteristic values of various sensors into the database when the electrical accessories are normal by combining the driving of the engine.
In the step e, diagnosing the fault of the electrical accessory specifically means establishing a fuzzy domain value of fault characteristics, and establishing a fault isolation equation:
wherein, λ is the number of test points, and λ is 20;
after an engine is installed on the airplane, the electric accessory of the pth engine is subjected to power-on inspection for the first time and a normal voltage value recorded when the engine is started for the first time; vk(Fp) Indicating failure mode F of electrical accessory or linepA node voltage value of time;
if any one of the test node voltages deviates from the normal value, the inequality of the equation 1 is established, and a fault point is formed.
Example 5
Referring to fig. 1 and 2, an online fault diagnosis method for an aircraft engine electrical system comprises the following steps:
a. the aviation plug is connected with an electric accessory of the engine, various signals are separated through the transfer plug, and the signals are collected and extracted through the distributed collection and analysis unit;
b. storing the characteristic value information of the electrical accessories under static and dynamic conditions into a standard database through a centralized fault interpretation and health management center;
c. measuring the information of the electrical accessory by using an I/O module through a control circuit, a driving circuit and an acquisition circuit, extracting and analyzing the characteristic value of the acquired information, and performing noise reduction processing through wavelet transformation;
d. acquiring knowledge through an expert system, performing information interaction with a knowledge database, and explaining characteristic value information of electrical accessories of various engines in different working modes;
e. and when the standard characteristic value is deviated and the fault critical value is not exceeded, the electric accessory is diagnosed to be in a sub-health state, and when the standard characteristic value is deviated and the fault critical value is exceeded, the electric accessory is diagnosed to be in a fault.
The electrical accessory comprises a rotating speed sensor, a temperature sensor, a vibration sensor, an angular displacement sensor, a pressure sensor, an ion flame detector, a metal scrap annunciator, an oxygen supplement ignition device, an ignition electric nozzle, a surge annunciator, an electromagnetic valve and a valve displacement measuring device.
In the step b, the standard database is formed by acquiring initial characteristic parameters of the electrical accessories in a static mode and recording the initial characteristic parameters into the database when the engine is not driven, and writing dynamic characteristic values of various sensors into the database when the electrical accessories are normal by combining the driving of the engine.
In the step e, diagnosing the fault of the electrical accessory specifically means establishing a fuzzy domain value of fault characteristics, and establishing a fault isolation equation:
wherein, λ is the number of test points, and λ is 20;
after an engine is installed on the airplane, the electric accessory of the pth engine is subjected to power-on inspection for the first time and a normal voltage value recorded when the engine is started for the first time; vk(Fp) Indicating failure mode F of electrical accessory or linepA node voltage value of time;
if any one of the test node voltages deviates from the normal value, the inequality of the equation 1 is established, and a fault point is formed.
In the step e, diagnosing the fault of the electrical accessory specifically refers to establishing a correlation coefficient C of the temperature sensor to be diagnosedj(Ai)：
Wherein x isoijConverting the standard parameter value of the temperature sensor in normal operation into a voltage value and taking the voltage value as a standard characteristic value; e.g. of the typeijFor the upper limit of the fluctuation range, t, of the monitored sensor parameter in normal operationijAlpha is a correction factor for the limit deviation of the monitored temperature sensor; x is the number ofjTemperature sensor values collected in real time;
the temperature sensor j is aligned with the target pattern aiThe basic probability distribution of (c) is:
wherein N iscFor the number of target patterns, take Nc2; n is the total number of sensors, and N is 10; wjIs a weighting coefficient of the temperature sensor, and is Wj1 is ═ 1; for the coefficient alpha in different modesj、βj、RjRespectively obtained by calculation of formula 4-formula 6;
αj＝max{Cj(Aj)} i＝1，2，…Ncformula 4
Combining an engine electrical system database, realizing fault interpretation and health management through an expert system, and obtaining F through data analysis and processingpAnd mjSequenced and stored in an electrical database.
The method is characterized in that an expert system, data analysis and a centralized fault interpretation and health management center of a database are combined, so that the fault condition of the electrical accessory can be directly judged when the engine is started, and visual maintenance and replacement can be performed on the electrical accessory which deviates from a standard characteristic value but does not exceed a fault critical value and is in a sub-health state; for the fault deviating from the standard characteristic value and exceeding the fault critical value, the system directly positions the fault without the need of checking the fault through flight parameter interpretation or frequent trial run, thereby greatly saving the cost of economic and human resources; compared with the prior art, the method can accurately position the fault type, save the cost of human resources, reduce the economic loss caused by frequent driving and troubleshooting of the engine, greatly shorten the troubleshooting period, and better ensure the safety and the reliability of the use of the engine through fault prediction and health management measures.
The method has the advantages that the automatic positioning of the electric system fault is realized by utilizing the characteristic parameter extraction, analysis and data analysis of different types of sensors, special electric accessories and electromagnetic valve valves in the aircraft engine, and the fault prediction of the electric accessories can be realized.
Claims (3)
1. An online fault diagnosis method for an aircraft engine electrical system is characterized by comprising the following steps:
a. the aviation plug is connected with an electric accessory of the engine, various signals are separated through the transfer plug, and the signals are collected and extracted through the distributed collection and analysis unit;
b. storing the characteristic value information of the electrical accessories under static and dynamic conditions into a standard database through a centralized fault interpretation and health management center;
c. measuring the information of the electrical accessory by using an I/O module through a control circuit, a driving circuit and an acquisition circuit, extracting and analyzing the characteristic value of the acquired information, and performing noise reduction processing through wavelet transformation;
d. acquiring knowledge through an expert system, performing information interaction with a knowledge database, and explaining characteristic value information of electrical accessories of various engines in different working modes;
e. the method comprises the steps that a centralized fault interpretation and health management center of an expert system, data analysis and a database is combined to carry out fault diagnosis, when the standard characteristic value is deviated and the fault critical value is not exceeded, the electric accessory is diagnosed to be in a sub-health state, and when the standard characteristic value is deviated and the fault critical value is exceeded, the electric accessory is diagnosed to have a fault;
in the step e, diagnosing the fault of the electrical accessory specifically means establishing a fuzzy domain value of fault characteristics, and establishing a fault isolation equation:
wherein, λ is the number of test points, and λ is 20;
after an engine is installed on the airplane, the electric accessory of the pth engine is subjected to power-on inspection for the first time and a normal voltage value recorded when the engine is started for the first time; vk(Fp) Indicating failure mode F of electrical accessory or linepA node voltage value of time;
if the voltage of any test node deviates from a normal value, the inequality of the formula 1 is established to form a fault point;
in the step e, diagnosing the fault of the electrical accessory specifically refers to establishing a correlation coefficient C of the temperature sensor to be diagnosedj(Ai)：
Wherein x isoijConverting the standard parameter value of the temperature sensor in normal operation into a voltage value and taking the voltage value as a standard characteristic value; e.g. of a cylinderijFor the upper limit of the fluctuation range, t, of the monitored sensor parameter in normal operationijAlpha is a correction factor for the limit deviation of the monitored temperature sensor; x is the number ofjTemperature sensor values collected in real time;
the temperature sensor j is aligned with the target pattern aiThe basic probability distribution of (c) is:
wherein N iscFor the target number of patterns, take Nc2; n is the total number of sensors, and N is 10; wjIs a weighting coefficient of the temperature sensor, and is Wj1 is ═ 1; for the coefficient alpha in different modesj、βj、RjRespectively obtained by calculation of formula 4-formula 6;
αj＝max{Cj(Aj)} i＝1，2，…Ncformula 4
The engine electrical system database is combined, fault interpretation and health management are realized through an expert system, and a fault mode F is obtained through data analysis and processingpAnd mjSequenced and stored in an electrical database.
2. The online fault diagnosis method for the electric system of the aircraft engine according to claim 1, characterized in that: the electrical accessory comprises a rotating speed sensor, a temperature sensor, a vibration sensor, an angular displacement sensor, a pressure sensor, an ion flame detector, a metal scrap annunciator, an oxygen supplement ignition device, an ignition electric nozzle, a surge annunciator, an electromagnetic valve and a valve displacement measuring device.
3. The online fault diagnosis method for the electric system of the aircraft engine according to claim 1, characterized in that: in the step b, the standard database is formed by acquiring initial characteristic parameters of the electrical accessories in a static mode and recording the initial characteristic parameters into the database when the engine is not driven, and writing dynamic characteristic values of various sensors into the database when the electrical accessories are normal by combining the driving of the engine.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6289735B1 (en) * | 1998-09-29 | 2001-09-18 | Reliance Electric Technologies, Llc | Machine diagnostic system and method for vibration analysis |
| CN1417961A (en) * | 2002-11-22 | 2003-05-14 | 天津大学 | Intelligent engineering machinery fault diagnosing system based on networked movable operation machines |
| CN102519582A (en) * | 2011-12-22 | 2012-06-27 | 南京航空航天大学 | Blind source separation method of aeroengine vibration signal |
| CN102840882A (en) * | 2012-09-04 | 2012-12-26 | 中国海洋石油总公司 | State monitoring and fault diagnosis system of gas turbine generating unit and use method of state monitoring and fault diagnosis system |
| CN105911975A (en) * | 2016-04-14 | 2016-08-31 | 南京航空航天大学 | Integrated diagnosis method of gas path fault of aeroengine |
| CN108303262A (en) * | 2018-01-19 | 2018-07-20 | 南京世界村汽车动力有限公司 | A kind of automobile engine on-line monitoring and fault diagnosis system |
| CN108883824A (en) * | 2016-03-23 | 2018-11-23 | 冯春魁 | The method and system of acquisition, the processing and flight condition monitoring of the data of aircraft |
| CN109711663A (en) * | 2018-11-15 | 2019-05-03 | 国网山东省电力公司淄博供电公司 | Substation's oil-immersed transformer status assessment and modification method and system based on big data analysis |
| CN110073301A (en) * | 2017-08-02 | 2019-07-30 | 强力物联网投资组合2016有限公司 | The detection method and system under data collection environment in industrial Internet of Things with large data sets |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6757665B1 (en) * | 1999-09-28 | 2004-06-29 | Rockwell Automation Technologies, Inc. | Detection of pump cavitation/blockage and seal failure via current signature analysis |
| US7379799B2 (en) * | 2005-06-29 | 2008-05-27 | General Electric Company | Method and system for hierarchical fault classification and diagnosis in large systems |
| US9104650B2 (en) * | 2005-07-11 | 2015-08-11 | Brooks Automation, Inc. | Intelligent condition monitoring and fault diagnostic system for preventative maintenance |
| US8606554B2 (en) * | 2009-10-19 | 2013-12-10 | Siemens Aktiengesellschaft | Heat flow model for building fault detection and diagnosis |
| CN106093612B (en) * | 2016-05-26 | 2019-03-19 | 国网江苏省电力公司电力科学研究院 | A kind of method for diagnosing fault of power transformer |
| CN106124982A (en) * | 2016-06-14 | 2016-11-16 | 都城绿色能源有限公司 | Automatic expert's resultant fault diagnostic system of a kind of Wind turbines and diagnostic method |
| CN108491622A (en) * | 2018-03-20 | 2018-09-04 | 华北水利水电大学 | A kind of fault diagnosis method and system of Wind turbines |
| CA3099659A1 (en) * | 2018-05-07 | 2019-11-14 | Strong Force Iot Portfolio 2016, Llc | Methods and systems for data collection, learning, and streaming of machine signals for analytics and maintenance using the industrial internet of things |
| CN110782190A (en) * | 2019-12-04 | 2020-02-11 | 江苏方天电力技术有限公司 | Phase modulator remote diagnosis system based on ubiquitous power internet of things technology |
-
2020
- 2020-05-22 CN CN202010439955.2A patent/CN111709453B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6289735B1 (en) * | 1998-09-29 | 2001-09-18 | Reliance Electric Technologies, Llc | Machine diagnostic system and method for vibration analysis |
| CN1417961A (en) * | 2002-11-22 | 2003-05-14 | 天津大学 | Intelligent engineering machinery fault diagnosing system based on networked movable operation machines |
| CN102519582A (en) * | 2011-12-22 | 2012-06-27 | 南京航空航天大学 | Blind source separation method of aeroengine vibration signal |
| CN102840882A (en) * | 2012-09-04 | 2012-12-26 | 中国海洋石油总公司 | State monitoring and fault diagnosis system of gas turbine generating unit and use method of state monitoring and fault diagnosis system |
| CN108883824A (en) * | 2016-03-23 | 2018-11-23 | 冯春魁 | The method and system of acquisition, the processing and flight condition monitoring of the data of aircraft |
| CN105911975A (en) * | 2016-04-14 | 2016-08-31 | 南京航空航天大学 | Integrated diagnosis method of gas path fault of aeroengine |
| CN110073301A (en) * | 2017-08-02 | 2019-07-30 | 强力物联网投资组合2016有限公司 | The detection method and system under data collection environment in industrial Internet of Things with large data sets |
| CN108303262A (en) * | 2018-01-19 | 2018-07-20 | 南京世界村汽车动力有限公司 | A kind of automobile engine on-line monitoring and fault diagnosis system |
| CN109711663A (en) * | 2018-11-15 | 2019-05-03 | 国网山东省电力公司淄博供电公司 | Substation's oil-immersed transformer status assessment and modification method and system based on big data analysis |
Non-Patent Citations (6)
| Title |
|---|
| "Engine fault diagnosis based on multi-sensor information fusion using Dempster–Shafer evidence theory";Basir, Otman 等;《Information fusion》;20071031;第 8 卷(第 4 期);379-386 * |
| "Expert System of Fault Diagnosis for Gear Box in Wind Turbine";YANGZhi-Ling 等;《在线出版https://doi.org/10.1016/j.sepro.2011.11.065》;20120119 * |
| "Research on aero-engine fault diagnosis based on integrated neural network";Shi Xiangyang等;《Mathematical Models in Engineering》;20190630;第5卷(第 2期);41-47 * |
| "基于Alopex神经网络的旋转机械故障诊断专家系统";刘贵立 等;《振动、测试与诊断》;20040130(第1期);63-66+79 * |
| "基于数据驱动的航空发动机状态监测关键技术研究";周媛;《中国博士学位论文全文数据库 (工程科技Ⅱ辑)》;20171115(第（2017）11期);C031-55 * |
| "混合动力汽车故障诊断及容错策略的研究";董威;《中国优秀硕士学位论文全文数据库 (工程科技Ⅱ辑)》;20070315(第（2007）03期);C035-240 * |
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