CN114357745A - New energy automobile incomplete failure fault diagnosability evaluation method and device - Google Patents

New energy automobile incomplete failure fault diagnosability evaluation method and device Download PDF

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CN114357745A
CN114357745A CN202111586761.6A CN202111586761A CN114357745A CN 114357745 A CN114357745 A CN 114357745A CN 202111586761 A CN202111586761 A CN 202111586761A CN 114357745 A CN114357745 A CN 114357745A
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彭坚
陈鋆纯
王雪鹏
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Hunan University
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Abstract

The embodiment of the invention provides a method for evaluating the diagnostic performance of a non-complete failure fault of a new energy automobile, which comprises the following steps: converting the fault problem of diagnosable evaluation into a similarity problem of multivariate distribution; establishing a calculation model according to the similarity problem of the multivariate distribution, and optimizing the calculation model; obtaining a detectable efficiency coefficient of the incomplete failure fault according to the similarity problem of the multivariate distribution; and obtaining a diagnosability evaluation result of the incomplete failure fault of the new energy automobile according to the calculation model and the detectable efficiency coefficient. Effectively increasing the application range of the algorithm; in addition, a maximum detectable efficiency coefficient analysis method of the incomplete failure fault is provided; by changing the length of the time window and the efficiency coefficient, the influence of different factors on the diagnosis result is analyzed.

Description

New energy automobile incomplete failure fault diagnosability evaluation method and device
Technical Field
The invention relates to the technical field of new energy automobile fault diagnosability, in particular to a new energy automobile non-complete failure fault diagnosability evaluation method and device.
Background
The existing quantitative evaluation method for the fault diagnosis of the actual system does not consider the condition of the incomplete failure fault of the system, and actually, the incomplete failure fault of the system is a representative fault in the actual power system; meanwhile, compared with additive faults, incomplete failure faults are related to input of a power supply system, so that detectability and isolability have more special properties, and therefore, research on an actual quantitative evaluation method for the incomplete failure fault diagnosis of the new energy system has important theoretical significance.
The applicant has found that at least the following problems exist in the prior art: the quantitative evaluation of the incomplete failure fault diagnosis of the new energy system cannot be effectively carried out.
Disclosure of Invention
The technical problem solved by the embodiment of the invention is that the diagnosis and quantitative evaluation of the incomplete failure fault of the new energy system cannot be effectively carried out.
In order to achieve the above object, in one aspect, an embodiment of the present invention provides a method for evaluating the diagnostic performance of a non-complete failure fault of a new energy vehicle, including the following steps:
converting the fault problem of diagnosable evaluation into a similarity problem of multivariate distribution;
establishing a calculation model according to the similarity problem of the multivariate distribution, and optimizing the calculation model;
obtaining a detectable efficiency coefficient of the incomplete failure fault according to the similarity problem of the multivariate distribution;
and obtaining a diagnosability evaluation result of the incomplete failure fault of the new energy automobile according to the calculation model and the detectable efficiency coefficient.
On the other hand, the embodiment of the invention provides a device for evaluating the diagnostic performance of the incomplete failure fault of the new energy automobile, which comprises the following components:
the conversion unit is used for converting the fault problem of diagnosability evaluation into a similarity problem of multivariate distribution;
the optimization unit is used for establishing a calculation model according to the similarity problem of the multivariate distribution and optimizing the calculation model;
the calculation coefficient unit is used for obtaining a detectable efficiency coefficient of the incomplete failure fault according to the similarity problem of the multivariate distribution;
and the evaluation result unit is used for obtaining a diagnostic evaluation result of the incomplete failure fault of the new energy automobile according to the calculation model and the detectable efficiency coefficient.
The technical scheme has the following beneficial effects: the method optimizes the calculation method, and effectively increases the application range of the algorithm; in addition, a maximum detectable efficiency coefficient analysis method of the incomplete failure fault is provided; by changing the length of the time window and the efficiency coefficient, the influence of different factors on the diagnosis result is analyzed.
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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 flowchart of a method for evaluating the diagnostic performance of an incomplete failure fault of a new energy vehicle according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a device for evaluating the diagnostic performance of the incomplete failure of the new energy vehicle according to the embodiment of the 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.
The invention provides a method for evaluating the diagnostic performance of a non-complete failure fault of a new energy automobile, which comprises the following steps of:
s101: converting the fault problem of diagnosable evaluation into a similarity problem of multivariate distribution;
s102: establishing a calculation model according to the similarity problem of the multivariate distribution, and optimizing the calculation model;
s103: obtaining a detectable efficiency coefficient of the incomplete failure fault according to the similarity problem of the multivariate distribution;
s104: and obtaining a diagnosability evaluation result of the incomplete failure fault of the new energy automobile according to the calculation model and the detectable efficiency coefficient.
The method for converting the fault problem of diagnosability evaluation into the similarity problem of multivariate distribution specifically comprises the following steps:
evaluating the fault problem of diagnosability evaluation by adopting a Bhattacharyya distance;
the calculation formula of the Bhattacharyya distance is as follows: BD (p, q) ═ ln [ BC (p, q) ], where:
BC (p, q) is a Bhattacharyya coefficient, p is a first probability density function, q is a second probability density function, and BD (p, q) ≧ 0, a larger value of BD (p, q) indicates a larger difference between distributions.
The establishing of the calculation model according to the similarity problem of the multivariate distribution specifically comprises the following steps:
the calculation model is as follows:
Figure BDA0003428074050000031
wherein:
θifor fault forms at a particular time series i, p0Is a function of the probability density in the absence of faults,
Figure BDA0003428074050000032
as a function of the probability density of the fault at a particular time series i.
The optimizing the calculation model specifically includes: and analyzing diagnosability by using a least square method to obtain an optimized calculation model as follows:
Figure BDA0003428074050000033
wherein:
θiin the form of a fault at a particular time series i,
Figure BDA0003428074050000034
is a matrix
Figure BDA0003428074050000035
Left null-space orthogonal basis.
The obtaining of the detectable efficiency coefficient of the incomplete failure fault according to the similarity problem of the multivariate distribution specifically includes:
the detectable efficiency factor for a non-complete failure is determined based on the factors involved in the non-complete failure.
The invention also provides a device for evaluating the diagnosis performance of the incomplete failure fault of the new energy automobile, which is shown in fig. 2 and comprises:
a conversion unit 21, configured to convert the failure problem of diagnosability evaluation into a similarity problem of multivariate distribution;
the optimizing unit 22 is configured to establish a calculation model according to the similarity problem of the multivariate distribution, and optimize the calculation model;
a calculating coefficient unit 23, configured to obtain a detectable efficiency coefficient of the incomplete failure fault according to the similarity problem of the multivariate distribution;
and the evaluation result unit 24 is used for obtaining a new energy automobile incomplete failure fault diagnosability evaluation result according to the calculation model and the detectable efficiency coefficient.
The conversion unit 21 specifically includes:
fault problems for evaluation of diagnosability evaluation using the Bhattacharyya distance;
the calculation formula of the Bhattacharyya distance is as follows: BD (p, q) ═ ln [ BC (p, q) ], where:
BC (p, q) is a Bhattacharyya coefficient, p is a first probability density function, q is a second probability density function, and BD (p, q) ≧ 0, a larger value of BD (p, q) indicates a larger difference between distributions.
The optimization unit 22 specifically includes:
the calculation model is as follows:
Figure BDA0003428074050000041
wherein:
θifor fault forms at a particular time series i, p0Is a function of the probability density in the absence of faults,
Figure BDA0003428074050000042
as a function of the probability density of the fault at a particular time series i.
The optimization unit 22 further comprises: and analyzing diagnosability by using a least square method to obtain an optimized calculation model as follows:
Figure BDA0003428074050000043
wherein:
θiin the form of a fault at a particular time series i,
Figure BDA0003428074050000044
is a matrix
Figure BDA0003428074050000045
Left null-space orthogonal basis.
The calculating coefficient unit 23 specifically includes:
for determining a detectable efficiency factor for a non-complete failure based on factors involved in the non-complete failure.
The method optimizes the calculation method, and effectively increases the application range of the algorithm; in addition, a maximum detectable efficiency coefficient analysis method of the incomplete failure fault is provided; by changing the length of the time window and the efficiency coefficient, the influence of different factors on the diagnosis result is analyzed.
The above technical solutions of the embodiments of the present invention are described in detail below with reference to specific application examples, and reference may be made to the foregoing related descriptions for technical details that are not described in the implementation process.
Example 1:
the invention provides a method for evaluating the diagnostic performance of a non-complete failure fault of a new energy automobile, which comprises the following steps of:
converting the fault problem of diagnosable evaluation into a similarity problem of multivariate distribution;
establishing a calculation model according to the similarity problem of the multivariate distribution, and optimizing the calculation model;
obtaining a detectable efficiency coefficient of the incomplete failure fault according to the similarity problem of the multivariate distribution;
and obtaining a diagnosability evaluation result of the incomplete failure fault of the new energy automobile according to the calculation model and the detectable efficiency coefficient.
Similarity is a numerical measure reflecting the degree of similarity between two objects. The degree of similarity between two objects is proportional to the degree of similarity between them, and is usually reflected in "distance". The Bhattacharyya distance is often used in statistics to measure the separability of two probability distributions. Thus, the Bhattacharyya distance was used to evaluate the diagnosability of the system.
The calculation formula of the Bhattacharyya distance is given as follows:
BD(p,q)＝-ln[BC(p,q)]
wherein BC (p, q) is a Bhattacharyya coefficient,
Figure BDA0003428074050000051
p and q and are each a multivariate distribution ZpAnd ZqAnd 0 ≦ BD (p, q), and an equality sign holds if and only if p ≦ q. Obviously, a larger value of BD (p, q) indicates a larger difference between the two distributions.
Considering two different faults fiAnd fjThe failure modes of the two under different given time sequences are respectively thetaiAnd thetaj，
Figure BDA0003428074050000052
To respective multivariate probability density functions of
Figure BDA0003428074050000053
And
Figure BDA0003428074050000054
failure fault fiAnd fjThe similarity of the multivariate probability density function is as follows:
Figure BDA0003428074050000055
when in use
Figure BDA0003428074050000056
Time, fault fiAnd fjThe isolation can not be carried out,
Figure BDA0003428074050000057
the larger the isolation difficulty is.
Figure BDA0003428074050000058
The result is a difference in the random distribution of the two faults under a specific time series of system inputs. However, isolatability is more than taking into account the differences between all forms of one particular fault and another. Based on this, an input θ under a specific time series is giveniThe following expression for failure fault isolatability:
Figure BDA0003428074050000059
for any pθjAll satisfy pθj∈Zj，Di,j(θi) Larger indicates a failure fault fiAnd fjThe lower the isolation difficulty between them.
Due to the fact that factors such as noise distribution, diagnosis residual construction, threshold selection and the like are involved in actual fault diagnosis, it is difficult to provide a general reference standard for minimum diagnosable faults by using the existing method, namely, the problem that the fault can be diagnosed only when the performance of a part is at least reduced by a small amount due to the fault cannot be answered.
Diagnosability evaluation indexes are mainly related to the structure of the system, and are not related to a diagnostic algorithm. In addition to this, the index takes into account the influence of external factors (measurement noise, process noise and uncertainty). In summary, the complexity of the problem can be greatly simplified by the proposed diagnostic evaluation method of a non-complete failure fault.
For a given thetaiThe maximum isolatability evaluation value is obtained when the actuator is completely disabled, i.e., when ∈ 0. The maximum isolatability evaluation value can then be used as a "reference value" to find the minimum isolatability evaluation value required for the fault to be isolated, and thus the maximum diagnosable performance coefficient.
The method optimizes the calculation method, and effectively increases the application range of the algorithm; in addition, a maximum detectable efficiency coefficient analysis method of the incomplete failure fault is provided; by changing the length of the time window and the efficiency coefficient, the influence of different factors on the diagnosis result is analyzed.
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".
Those of skill in the art will further appreciate that the various illustrative logical blocks, units, and steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate the interchangeability of hardware and software, various illustrative components, elements, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design requirements of the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present embodiments.
The various illustrative logical blocks, or elements, described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor, an Application Specific Integrated Circuit (ASIC), a field programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. For example, a storage medium may be coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC, which may be located in a user terminal. In the alternative, the processor and the storage medium may reside in different components in a user terminal.
In one or more exemplary designs, the functions described above in connection with the embodiments of the invention may be implemented in hardware, software, firmware, or any combination of the three. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media that facilitate transfer of a computer program from one place to another. Storage media may be any available media that can be accessed by a general purpose or special purpose computer. For example, such computer-readable media can include, but is not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store program code in the form of instructions or data structures and which can be read by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. Additionally, any connection is properly termed a computer-readable medium, and, thus, is included if the software is transmitted from a website, server, or other remote source via a coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), or wirelessly, e.g., infrared, radio, and microwave. Such discs (disk) and disks (disc) include compact disks, laser disks, optical disks, DVDs, floppy disks and blu-ray disks where disks usually reproduce data magnetically, while disks usually reproduce data optically with lasers. Combinations of the above may also be included in the computer-readable medium.
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 (10)

1. The method for evaluating the diagnostic performance of the incomplete failure fault of the new energy automobile is characterized by comprising the following steps of:
converting the fault problem of diagnosable evaluation into a similarity problem of multivariate distribution;
establishing a calculation model according to the similarity problem of the multivariate distribution, and optimizing the calculation model;
obtaining a detectable efficiency coefficient of the incomplete failure fault according to the similarity problem of the multivariate distribution;
and obtaining a diagnosability evaluation result of the incomplete failure fault of the new energy automobile according to the calculation model and the detectable efficiency coefficient.
2. The method for diagnosing and evaluating the fault possibility of the incomplete failure of the new energy automobile according to claim 1, wherein the step of converting the fault problem of the diagnostic evaluation into the similarity problem of the multivariate distribution specifically comprises the following steps:
evaluating the fault problem of diagnosability evaluation by adopting a Bhattacharyya distance;
the calculation formula of the Bhattacharyya distance is as follows: BD (p, q) ═ ln [ BC (p, q) ], where:
BC (p, q) is a Bhattacharyya coefficient, p is a first probability density function, q is a second probability density function, and BD (p, q) ≧ 0, a larger value of BD (p, q) indicates a larger difference between distributions.
3. The method for evaluating the non-complete failure fault diagnosability of the new energy automobile according to claim 1, wherein the building of the calculation model according to the multivariate distribution similarity problem specifically comprises:
the calculation model is as follows:
Figure FDA0003428074040000011
wherein:
θifor fault forms at a particular time series i, p0Is a function of the probability density in the absence of faults,
Figure FDA0003428074040000012
as a function of the probability density of the fault at a particular time series i.
4. The method for evaluating the non-complete failure fault diagnosability of the new energy automobile according to claim 1, wherein the optimizing the calculation model specifically includes: and analyzing diagnosability by using a least square method to obtain an optimized calculation model as follows:
Figure FDA0003428074040000013
wherein:
θiin the form of a fault at a particular time series i,
Figure FDA0003428074040000014
is a matrix
Figure FDA0003428074040000015
Left null-space orthogonal basis.
5. The method according to claim 1, wherein the obtaining of the detectable performance coefficient of the incomplete failure fault according to the multivariate distribution similarity problem specifically includes:
the detectable efficiency factor for a non-complete failure is determined based on the factors involved in the non-complete failure.
6. The utility model provides a new energy automobile incomplete failure fault diagnosability evaluation device which characterized in that includes:
the conversion unit is used for converting the fault problem of diagnosability evaluation into a similarity problem of multivariate distribution;
the optimization unit is used for establishing a calculation model according to the similarity problem of the multivariate distribution and optimizing the calculation model;
the calculation coefficient unit is used for obtaining a detectable efficiency coefficient of the incomplete failure fault according to the similarity problem of the multivariate distribution;
and the evaluation result unit is used for obtaining a diagnostic evaluation result of the incomplete failure fault of the new energy automobile according to the calculation model and the detectable efficiency coefficient.
7. The device for evaluating the fault diagnosability of the new energy automobile in the incomplete failure according to claim 6, wherein the conversion unit specifically comprises:
fault problems for evaluation of diagnosability evaluation using the Bhattacharyya distance;
the calculation formula of the Bhattacharyya distance is as follows: BD (p, q) ═ ln [ BC (p, q) ], where:
BC (p, q) is a Bhattacharyya coefficient, p is a first probability density function, q is a second probability density function, and BD (p, q) ≧ 0, a larger value of BD (p, q) indicates a larger difference between distributions.
8. The device for evaluating the non-complete failure fault diagnosability of the new energy automobile according to claim 6, wherein the optimization unit specifically comprises:
the calculation model is as follows:
Figure FDA0003428074040000021
wherein:
θifor fault forms at a particular time series i, p0Is a function of the probability density in the absence of faults,
Figure FDA0003428074040000022
as a function of the probability density of the fault at a particular time series i.
9. The device for evaluating the diagnostic performance of the incomplete failure fault of the new energy automobile according to claim 6, wherein the optimization unit further comprises: and analyzing diagnosability by using a least square method to obtain an optimized calculation model as follows:
Figure FDA0003428074040000023
wherein:
θiin the form of a fault at a particular time series i,
Figure FDA0003428074040000024
is a matrix
Figure FDA0003428074040000025
Left null-space orthogonal basis.
10. The device for evaluating the non-complete failure fault diagnosability of the new energy automobile according to claim 6, wherein the calculation coefficient unit specifically comprises:
for determining a detectable efficiency factor for a non-complete failure based on factors involved in the non-complete failure.
CN202111586761.6A 2021-12-23 2021-12-23 New energy automobile incomplete failure fault diagnosability evaluation method and device Pending CN114357745A (en)

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