CN111813074B - Fault noise ratio and feature extraction based tiny fault diagnosis method and system - Google Patents
Fault noise ratio and feature extraction based tiny fault diagnosis method and system Download PDFInfo
- Publication number
- CN111813074B CN111813074B CN202010547802.XA CN202010547802A CN111813074B CN 111813074 B CN111813074 B CN 111813074B CN 202010547802 A CN202010547802 A CN 202010547802A CN 111813074 B CN111813074 B CN 111813074B
- Authority
- CN
- China
- Prior art keywords
- fault
- data
- noise ratio
- low
- frequency data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 49
- 238000003745 diagnosis Methods 0.000 title claims abstract description 28
- 238000000605 extraction Methods 0.000 title claims abstract description 25
- 238000012544 monitoring process Methods 0.000 claims abstract description 39
- 238000013507 mapping Methods 0.000 claims abstract description 31
- 230000000007 visual effect Effects 0.000 claims abstract description 23
- 230000009467 reduction Effects 0.000 claims abstract description 14
- 238000004458 analytical method Methods 0.000 claims abstract description 9
- 238000000926 separation method Methods 0.000 claims description 12
- 238000001514 detection method Methods 0.000 claims description 9
- 230000007246 mechanism Effects 0.000 claims description 8
- 230000003321 amplification Effects 0.000 claims description 6
- 230000006872 improvement Effects 0.000 claims description 6
- 238000003199 nucleic acid amplification method Methods 0.000 claims description 6
- 230000001154 acute effect Effects 0.000 claims description 5
- 238000013461 design Methods 0.000 claims description 5
- 238000009825 accumulation Methods 0.000 claims description 4
- 239000006185 dispersion Substances 0.000 claims description 4
- 239000011159 matrix material Substances 0.000 claims description 4
- 238000000547 structure data Methods 0.000 claims description 4
- 239000013598 vector Substances 0.000 claims 2
- 238000012800 visualization Methods 0.000 abstract description 4
- 238000005516 engineering process Methods 0.000 description 4
- 238000012360 testing method Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000012545 processing Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 206010013647 Drowning Diseases 0.000 description 1
- PEDCQBHIVMGVHV-UHFFFAOYSA-N Glycerine Chemical compound OCC(O)CO PEDCQBHIVMGVHV-UHFFFAOYSA-N 0.000 description 1
- 241001125862 Tinca tinca Species 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000000513 principal component analysis Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24065—Real time diagnostics
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Geophysics And Detection Of Objects (AREA)
- Navigation (AREA)
Abstract
The embodiment of the invention provides a tiny fault diagnosis method and a tiny fault diagnosis system based on fault noise ratio and feature extraction, wherein the method comprises the steps of obtaining monitoring data of a spacecraft control system through kinematics and dynamics analysis of the control system; acquiring low-frequency data of the monitoring data, and improving the fault noise ratio of the low-frequency data by a superposition method; carrying out feature extraction on low-frequency data with improved fault noise ratio based on asymmetric importance factors among different faults, and then carrying out data dimension reduction on the low-frequency data to obtain corresponding asymmetric visual mapping; and separating fault data in the monitoring data according to the asymmetric visualization mapping. According to the technical scheme, the fault noise ratio and the visual mapping of data dimension reduction are improved, and the micro fault diagnosis performance based on the fault noise ratio and the feature extraction is improved, so that the method has important significance for improving the reliability of the on-orbit spacecraft and prolonging the service life of the spacecraft.
Description
Technical Field
The invention relates to the field of spacecraft data processing, in particular to a micro fault diagnosis method and system based on fault noise ratio and feature extraction.
Background
Spacecraft are typically complex systems that integrate many disciplines of mechanics, electronics, materials, control, energy, communications, and computer technology with their latest sophisticated efforts. The spacecraft control system undertakes tasks of attitude control, orbit control, solar sailboard and antenna driving control and the like, and is one of the most important and complex subsystems in the spacecraft [1 ]. In 2018, the success of the moon goddess in the fourth month of Chang E marks great progress of the aerospace industry. However, worldwide, spacecraft control system failures have also resulted in significant losses in space planning, scientific research, economic efficiency, and even political and military [2-4 ]. For example, in 12 months 2005, the attitude of the detector for tench in japan controlled the leakage of fuel from the reaction wheel, and the detector could not maintain the attitude. Loss of contact with the ground control centre for up to 2 months misses a return window, resulting in a "falcon" postponing 3 years to return to earth [5 ]. For another example, in 7 months in 2017, the practice satellite 18 carried by the rocket Changcheng 5 in China crashes into the sea, the task is lost, the technology is returned to zero, and the public opinion shakes. The package for the national academy indicates in the congress report of CAC 2017: if the early-stage micro fault of the spacecraft controller can be detected in time, the controller is intelligent enough, but not single in function, and the 16-billion-element east red 5 satellite platform cannot fall into the sea [6 ]. For another example, in 2018, when a small single-machine fault occurs in a certain type of Chinese spacecraft, the fault is not detected and alarmed in time, the actuator continuously injects air, the attitude angular velocity is rapidly increased, the platform is out of control and rolls, the SADA solar wing is broken, and finally the whole spacecraft fails. Due to the importance of the tasks undertaken by the spacecraft control system and the high occurrence of faults, the improvement of the reliability of the control system becomes the key for ensuring the on-orbit safety and the operation quality of the spacecraft.
Obvious faults are usually evolved from early tiny faults, and if tiny faults are detected in time within a controllable range of system operation and are separated and positioned, failure events are effectively avoided, so that diagnosis of tiny faults plays an important role in achieving high reliability and long service life of the whole satellite.
Disclosure of Invention
The embodiment of the invention provides a micro fault diagnosis method and system based on fault noise ratio and feature extraction, which improve the micro fault diagnosis performance based on the fault noise ratio and feature extraction by improving the fault noise ratio and visual mapping of data dimension reduction, and have important significance for improving the reliability of an on-orbit spacecraft and prolonging the service life of the spacecraft.
In order to achieve the above object, in one aspect, an embodiment of the present invention provides a minor fault diagnosis method based on a fault-to-noise ratio and feature extraction, where the method includes:
obtaining monitoring data of a spacecraft control system through kinematic and dynamic analysis of the control system;
acquiring low-frequency data of the monitoring data, and improving the fault noise ratio of the low-frequency data by a superposition method;
carrying out feature extraction on low-frequency data with improved fault noise ratio based on asymmetric importance factors among different faults, and then carrying out data dimension reduction on the low-frequency data to obtain corresponding asymmetric visual mapping;
and separating fault data in the monitoring data according to the asymmetric visualization mapping.
On the other hand, the embodiment of the invention provides a tiny fault diagnosis system based on fault noise ratio and feature extraction, and the system comprises:
the data acquisition unit is used for acquiring monitoring data of the control system through kinematic and dynamic analysis of the spacecraft control system;
the noise amplification unit is used for acquiring low-frequency data of the monitoring data and improving the fault noise ratio of the low-frequency data by a superposition method;
the mapping unit is used for extracting the characteristics of the low-frequency data with the improved fault noise ratio based on the asymmetric importance factors among different faults and then performing data dimension reduction on the low-frequency data to obtain corresponding asymmetric visual mapping;
and the fault separation unit is used for separating fault data in the monitoring data according to the asymmetric visual mapping.
The technical scheme has the following beneficial effects:
the technical scheme of the invention utilizes the tiny fault detection method based on the weak signal enhancement technology and the tiny fault separation method based on the visual mapping technology to break through the difficulty of tiny fault detection, improve the detection rate of tiny faults, provide an optimal fault information view, improve the fault separation rate, enhance the reliability of tiny fault diagnosis results, perfect the tiny fault diagnosis theory, complete the fault library of the on-orbit spacecraft control system, and provide theoretical and technical support for improving the on-orbit fault diagnosis and processing capability of the spacecraft.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a minor fault diagnosis method based on fault-to-noise ratio and feature extraction according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating micro fault data in an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a minor fault diagnosis system based on fault-to-noise ratio and feature extraction according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
As shown in fig. 1, the method is a flowchart of a minor fault diagnosis method based on fault-to-noise ratio and feature extraction in the embodiment of the present invention, and the method includes:
s101: obtaining monitoring data of a spacecraft control system through kinematic and dynamic analysis of the control system; preferably, the monitoring data includes an operation mechanism data, a model structure data and a model parameter data of the control system.
And acquiring an information source of a subsequent tiny fault diagnosis method through mechanism analysis and state acquisition. The information source of the on-track typical case mainly comprises two parts, namely a mechanism model and monitoring data. The operation mechanism, the model structure and the model parameters of the control system can be obtained through the kinematics and dynamics analysis of the spacecraft control system, and the information is not only a model information source of a tiny fault diagnosis method, but also a simulation basis of a Simulink control and an S function module of Matlab. The computer can store the actuator monitoring data (momentum wheel/thruster/sailboard driving mechanism, etc.) and the sensor monitoring data (gyroscope/infrared sensor/star sensor/sun sensor, etc.) in real time, and the information is not only the data information source of the tiny fault diagnosis method, but also the entry information of the Qt Widgets window application program.
S102: and acquiring low-frequency data of the monitoring data, and improving the fault noise ratio of the low-frequency data by a superposition method. Preferably, the acquiring low-frequency data of the monitoring data and improving a fault-to-noise ratio of the low-frequency data by a superposition method includes: quantifying the influence of the superposition window width and the forgetting factor on the detection delay; acquiring low-frequency data of the monitoring data through a low-pass filter; and improving the fault noise ratio of the low-frequency data by using a heap method, an accumulation method or an exponential moving average method.
As shown in fig. 2, the monitoring data y of the system can be decomposed into three parts: noise e, trend t, and fault f. The trend term is inherently smoother, affected by noise, and the data perturbs between the upper and lower envelopes. If the trend is non-stationary and the variance ratio of the noise is large, the minor fault is easily masked by the trend and the noise. In fig. 2, the monitoring data between the start of the fault and the end of the fault has a slight fault, but does not significantly break through the upper and lower envelopes, so that it is difficult to visually find the change caused by the fault. Similar phenomena exist in monitoring data of a gyroscope of a spacecraft control system under the condition of micro-drift faults. One of the core problems of the minor Fault detection is to suppress the "noise-drowning Fault effect", that is, to increase the Fault-noise ratio FNR (Fault-noise ratio).
Preferably, the fault-to-noise ratio FNR (Φ, n) is represented by:
FNR(Φ,n)＝||Φ(n,p)f||/R(Φ(n,p)e)，
and the ratio of improvement of the fault noise ratio is satisfied
Because in the test residual, the noise is random, andthe fault is always deterministic, and the formula can be met according to the independent superposition principle;
wherein Φ (n, p) represents an n-order design operator, n represents an order, and p represents a delay factor; f represents a fault, | f | | | represents a modulus of the fault; r (e) represents the radius of the sphere of dispersion in the noise space; i phi (n, p) f i represents the length,
r (Φ (n, p) e) represents the scatter sphere radius of Φ (n, p) e;
presentation boundary quilt
Controlled to be a normal number a,
if it is used
Representing the family of n-order operators under window width, forgetting factor and passband constraints, the above-described lines of study can be described as
S103: and carrying out feature extraction on the low-frequency data with the improved fault noise ratio based on the asymmetric importance factors among different faults, and then carrying out data dimension reduction on the low-frequency data to obtain corresponding asymmetric visual mapping.
In q faults after dimensionality reduction, the separation information of any two faults is Iso (r)i,rj)＝||Mri-sign(i,j)Mrj||2(i, j equals 1, …, q), wherein if the direction angle is acute, sign (i, j) equals 1, otherwise sign (i, j) equals-1. Symmetric optimal visual mapping falseThe importance of different types of faults is the same, so that the feature extraction method can be simply converted into a principal component analysis process. In spacecraft control systems, however, the importance of different fault separation information should be treated differently. For example, if the included angle between the two characteristic directions is very small, a slight loss of separation information may cause the two faults to be inseparable; on the contrary, if the included angle between the two characteristic directions is large, the influence of weak separation information loss on the fault separation result is not large.
Preferably, after the feature extraction is performed on the low-frequency data with the improved fault-to-noise ratio based on the asymmetric importance factors between different faults, the data dimension reduction is performed on the low-frequency data to obtain the corresponding asymmetric visual mapping, which is realized by the following formula:
wherein, wij(i, j ═ 1, …, q) is the asymmetric importance factor, p is the number of faults;
Iso(ri,rj) For any two faults ri,rjIs expressed as Iso (r)i,rj)＝||Mri-sign(i,j)Mrj||2(i, j ═ 1, …, q), if two faults ri,rjIf the inter-directional angle is an acute angle, sign (i, j) is 1, otherwise sign (i, j) is-1;
m is asymmetric visual mapping, and I is an identity matrix.
S104: and separating fault data in the monitoring data according to the asymmetric visualization mapping.
Further preferably, for the separation of the fault noise in the scheme, a test tool box is provided, and the tool box is divided into three layers. An input layer: and simulating the satellite attitude control system by using the Simulink control and the S function module, and using a QFileDialog library of a Qt Widgets designer as a data import port of the satellite attitude control system. And an algorithm layer: and the M file is used for realizing a tiny fault amplification detection algorithm, a tiny fault visual separation algorithm, a data graph export function and the like. An output layer: the toolbox GUI interface is completed with a Qt designer. The availability, the operational efficiency and the diagnosis performance of the data test tool kit and the tiny fault diagnosis method are monitored by utilizing the simulation data and the real satellite control system. The test feedback result can be used as the basis for improving the tiny fault diagnosis method.
Corresponding to the above method, as shown in fig. 3, a schematic structural diagram of a minor fault diagnosis system based on fault-to-noise ratio and feature extraction according to an embodiment of the present invention is shown, where the system includes:
a data acquisition unit 21, configured to obtain monitoring data of a spacecraft control system through kinematic and dynamic analysis of the control system;
the noise amplification unit 22 is configured to acquire low-frequency data of the monitoring data, and improve a fault-to-noise ratio of the low-frequency data by using a superposition method;
the mapping unit 23 is configured to perform feature extraction on the low-frequency data with the improved fault-to-noise ratio based on asymmetric importance factors between different faults, and then perform data dimension reduction on the low-frequency data to obtain corresponding asymmetric visual mapping;
and a fault separation unit 24, configured to separate fault data in the monitoring data according to the asymmetric visualization mapping.
Preferably, the monitoring data includes operational mechanism data, model structure data and model parameter data of the control system.
Preferably, the noise amplification unit 22 is specifically configured to:
quantifying the influence of the superposition window width and the forgetting factor on the detection delay;
acquiring low-frequency data of the monitoring data through a low-pass filter;
and improving the fault noise ratio of the low-frequency data by using a heap method, an accumulation method or an exponential moving average method.
Preferably, the fault noise is represented by the following formula:
FNR(Φ,n)＝||Φ(n,p)f||/R(Φ(n,p)e)，
and the ratio of improvement of the fault noise ratio is satisfied
Wherein Φ (n, p) represents an n-order design operator, n represents an order, and p represents a delay factor; f represents a fault, | f | | | represents a modulus of the fault; r (e) represents the radius of the sphere of dispersion in the noise space; i phi (n, p) f i represents the length,
r (Φ (n, p) e) represents the scatter sphere radius of Φ (n, p) e;
presentation boundary quilt
Controlled to be a normal number a,
preferably, the mapping unit 23 implements asymmetric visual mapping of the monitoring data by the following formula:
wherein, wij(i, j ═ 1, …, q) is the asymmetric importance factor, q is the number of faults after dimensionality reduction;
Iso(ri,rj) For any two faults ri,rjIs expressed as Iso (r)i,rj)＝||Mri-sign(i,j)Mrj||2(i, j ═ 1, …, q), if two faults ri,rjIf the inter-directional angle is an acute angle, sign (i, j) is 1, otherwise sign (i, j) is-1;
m is asymmetric visual mapping, and I is an identity matrix.
It should be understood that the specific order or hierarchy of steps in the processes disclosed is an example of exemplary approaches. Based upon design preferences, it is understood that the specific order or hierarchy of steps in the processes may be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, and are not intended to be limited to the specific order or hierarchy presented.
In the foregoing detailed description, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the subject matter require more features than are expressly recited in each claim. Rather, as the following claims reflect, invention lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. To those skilled in the art; various modifications to these embodiments will be readily apparent, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
What has been described above includes examples of one or more embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the aforementioned embodiments, but one of ordinary skill in the art may recognize that many further combinations and permutations of various embodiments are possible. Accordingly, the embodiments described herein are intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent that the term "includes" is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term "comprising" as "comprising" is interpreted when employed as a transitional word in a claim. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or".
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (4)
1. The tiny fault diagnosis method based on fault noise ratio and feature extraction is characterized by comprising the following steps:
obtaining monitoring data of a spacecraft control system through kinematic and dynamic analysis of the control system;
acquiring low-frequency data of the monitoring data, and improving the fault noise ratio of the low-frequency data by a superposition method;
carrying out feature extraction on low-frequency data with improved fault noise ratio based on asymmetric importance factors among different faults, and then carrying out data dimension reduction on the low-frequency data to obtain corresponding asymmetric visual mapping;
separating fault data in the monitoring data according to the asymmetric visual mapping;
the monitoring data comprises operation mechanism data, model structure data and model parameter data of the control system;
the acquiring low-frequency data of the monitoring data and improving the fault noise ratio of the low-frequency data by a superposition method comprises the following steps:
quantifying the influence of the superposition window width and the forgetting factor on the detection delay;
acquiring low-frequency data of the monitoring data through a low-pass filter;
improving the fault noise ratio of the low-frequency data by using a stacking method, an accumulation method or an exponential moving average method;
after the low-frequency data with the improved fault noise ratio is subjected to feature extraction based on the asymmetric importance factors among different faults, the low-frequency data is subjected to data dimension reduction to obtain corresponding asymmetric visual mapping, and the method is realized by the following formula:
wherein, wij(i, j ═ 1, …, q) is the asymmetric importance factor, q is the number of faults;
Iso(ri,rj) Feature vector r for any two faultsi,rjIs expressed as Iso (r)i,rj)＝||Mri-sign(i,j)Mrj||2(i, j ═ 1, …, q), if two fault feature vectors ri,rjIf the inter-directional angle is an acute angle, sign (i, j) is 1, otherwise sign (i, j) is-1;
m is asymmetric visual mapping, and I is an identity matrix.
2. The minor fault diagnosis method based on fault-to-noise ratio and feature extraction according to claim 1, wherein the fault-to-noise ratio FNR (Φ, n) is represented by:
FNR(Φ,n)＝||Φ(n,p)f||/R(Φ(n,p)e)，
and the ratio of improvement of the fault noise ratio is satisfied
Wherein Φ (n, p) represents an n-order design operator, n represents an order, and p represents a delay factor; f represents a fault, | f | | | represents a modulus of the fault; r (e) represents the radius of the sphere of dispersion in the noise space; i phi (n, p) f i represents the length,
r (Φ (n, p) e) represents the scatter sphere radius of Φ (n, p) e;
presentation boundary quilt
Controlled to be a normal number a,
3. a tiny fault diagnosis system based on fault noise ratio and feature extraction is characterized by comprising:
the data acquisition unit is used for acquiring monitoring data of the control system through kinematic and dynamic analysis of the spacecraft control system;
the noise amplification unit is used for acquiring low-frequency data of the monitoring data and improving the fault noise ratio of the low-frequency data by a superposition method;
the mapping unit is used for extracting the characteristics of the low-frequency data with the improved fault noise ratio based on the asymmetric importance factors among different faults and then performing data dimension reduction on the low-frequency data to obtain corresponding asymmetric visual mapping;
the fault separation unit is used for separating fault data in the monitoring data according to the asymmetric visual mapping;
wherein the monitoring data comprises operation mechanism data, model structure data and model parameter data of the control system;
the noise amplification unit is specifically configured to:
quantifying the influence of the superposition window width and the forgetting factor on the detection delay;
acquiring low-frequency data of the monitoring data through a low-pass filter;
improving the fault noise ratio of the low-frequency data by using a stacking method, an accumulation method or an exponential moving average method;
the mapping unit realizes asymmetric visual mapping of the monitoring data through the following formula:
wherein, wij(i, j ═ 1, …, q) is the asymmetric importance factor, q is the number of faults after dimensionality reduction;
Iso(ri,rj) For any two faults ri,rjIs expressed as Iso (r)i,rj)＝||Mri-sign(i,j)Mrj||2(i, j ═ 1, …, q), if two faults ri,rjIf the inter-directional angle is an acute angle, sign (i, j) is 1, otherwise sign (i, j) is-1;
m is asymmetric visual mapping, and I is an identity matrix.
4. The minor fault diagnosis system based on fault-to-noise ratio and feature extraction according to claim 3, wherein the fault-to-noise ratio FNR (Φ, n) is represented by the following formula:
FNR(Φ,n)＝||Φ(n,p)f||/R(Φ(n,p)e)，
and the ratio of improvement of the fault noise ratio is satisfied
Wherein Φ (n, p) represents an n-order design operator, n represents an order, and p represents a delay factor; f represents a fault, | f | | | represents a modulus of the fault; r (e) represents the radius of the sphere of dispersion in the noise space; i phi (n, p) f i represents the length,
r (Φ (n, p) e) represents the scatter sphere radius of Φ (n, p) e;
presentation boundary quilt
Controlled to be a normal number a,
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010547802.XA CN111813074B (en) | 2020-06-16 | 2020-06-16 | Fault noise ratio and feature extraction based tiny fault diagnosis method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010547802.XA CN111813074B (en) | 2020-06-16 | 2020-06-16 | Fault noise ratio and feature extraction based tiny fault diagnosis method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111813074A CN111813074A (en) | 2020-10-23 |
| CN111813074B true CN111813074B (en) | 2021-09-03 |
Family
ID=72845145
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010547802.XA Active CN111813074B (en) | 2020-06-16 | 2020-06-16 | Fault noise ratio and feature extraction based tiny fault diagnosis method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111813074B (en) |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7081729B2 (en) * | 2004-03-23 | 2006-07-25 | The Boeing Company | Variable-structure diagnostics approach achieving optimized low-frequency data sampling for EMA motoring subsystem |
| CN104571087B (en) * | 2014-12-26 | 2017-08-29 | 北京控制工程研究所 | Spacecraft control diagnosability determination method under a kind of influence of noise |
| CN106224067B (en) * | 2016-09-29 | 2018-07-17 | 重庆邮电大学 | A kind of method for diagnosing faults of the automobile three-way catalysts based on data-driven |
| CN106742068B (en) * | 2016-12-07 | 2019-01-04 | 中国人民解放军国防科学技术大学 | A method of diagnosis satellite attitude control system unknown failure |
| CN108303264B (en) * | 2017-01-13 | 2020-03-20 | 华为技术有限公司 | Cloud-based vehicle fault diagnosis method, device and system |
| CN111240297A (en) * | 2018-11-28 | 2020-06-05 | 中国科学院沈阳自动化研究所 | Spacecraft attitude control system fault diagnosis method based on Kalman filter |
| CN111239587A (en) * | 2020-01-20 | 2020-06-05 | 哈尔滨工业大学 | A Fault Diagnosis Method for Analog Circuits Based on FRFT and LLE Feature Extraction |
-
2020
- 2020-06-16 CN CN202010547802.XA patent/CN111813074B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN111813074A (en) | 2020-10-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Napolitano et al. | Aircraft failure detection and identification using neural networks | |
| CN105843240A (en) | Spacecraft attitude integral sliding mode fault tolerance control method taking consideration of performer fault | |
| Zhao et al. | Robust Data‐Driven Fault Detection: An Application to Aircraft Air Data Sensors | |
| Gao et al. | EKF‐Based Actuator Fault Detection and Diagnosis Method for Tilt‐Rotor Unmanned Aerial Vehicles | |
| Jia et al. | A multi-channel data-based fault diagnosis method integrating deep learning strategy for aircraft sensor system | |
| Grauer | Real-time data-compatibility analysis using output-error parameter estimation | |
| Lim et al. | Satellite fault detection and isolation scheme with modified adaptive fading EKF | |
| CN111813074B (en) | Fault noise ratio and feature extraction based tiny fault diagnosis method and system | |
| Dutta et al. | Multicopter fault detection and identification via data-driven statistical learning methods | |
| CN110412997B (en) | Spacecraft attitude control spray pipe fault intelligent diagnosis system and method based on neural network | |
| Sandaran et al. | Overview of the Helios version 2.0 computational platform for rotorcraft simulations | |
| Madruga et al. | Estimating the loss of effectiveness of UAV actuators in the presence of aerodynamic effects | |
| Kulik | Rational intellectualization of the aircraft control: Resources-saving safety improvement | |
| CN111783286A (en) | Micro-fault diagnosis method and system based on fault trend ratio and feature selection | |
| CN116520698B (en) | Fault-tolerant control method of launch vehicle actuator based on extended Kalman filter | |
| Zhang et al. | Fault monitoring and diagnosis of aerostat actuator based on pca and state observer | |
| Zhang et al. | Structure and Thrust Fault Detection and Diagnosis for Hypersonic Aircrafts via a Data-Driven Approach | |
| Shankar et al. | Mission critical software test philosophy a SILS based approach in Indian Mars Orbiter Mission | |
| Sun | Model and Sensor Based Nonlinear Adaptive Flight Control with Online System Identification | |
| Frank | Advanced fault diagnosis techniques in aerospace systems | |
| Odendaal | An analysis and comparison of two methods for UAV actuator fault detection and isolation | |
| CN114408162B (en) | Control surface reconstruction method, control surface reconstruction system and readable storage medium | |
| Zogopoulos-Papaliakos et al. | Parity-Based Diagnosis in UAVs: Detectability and Robustness Analyses | |
| Yang et al. | Fault Detection and Estimation for Drag-Free Satellite Actuator Based on Adaptive Sliding Mode Observer | |
| Li et al. | Multiple fault isolation method for micro thrusters of drag-free systems |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |