CN113378286B - Fatigue life prediction method, storage medium and terminal - Google Patents
Fatigue life prediction method, storage medium and terminal Download PDFInfo
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Abstract
The embodiment of the invention discloses a fatigue life prediction method, a storage medium and a terminal. The fatigue life prediction method of the invention comprises the following steps: s1, acquiring part classification information; s2, generating a residual life calculation formula group according to the part classification information, wherein the residual life calculation formula group comprises a residual life calculation formula associated with the part classification information; s3, acquiring the part classification information and the service life information of the part to be monitored; s4, acquiring a corresponding residual life calculation formula according to the part classification information of the part to be monitored and the residual life calculation formula group; s5, generating residual life information according to the used life information of the part to be monitored and the corresponding residual life calculation formula. The fatigue life prediction method can give consideration to the accuracy and economy of the residual life prediction of the parts.
Description
Technical Field
The embodiment of the invention relates to the field of machinery, in particular to a fatigue life prediction method, a storage medium and a terminal.
Background
During operation of the train, the components are subjected to wheel rail excitation caused by track irregularities and internal excitation caused by production process and equipment gaps between the vehicle components. Under the action of the two methods, the dynamic response of the vehicle component structure is complex, resonance forms of various parameters such as sub-resonance, super-resonance and the like are easy to appear under the excitation of certain specific frequencies, adverse effects are generated on the service environment of the component, and the resonance failure of the system is caused when the resonance is serious.
In the conventional technology, the same prediction formula is uniformly adopted for predicting the service life of the components, namely, the same components are assumed to have the same service life, and the stage of the life cycle of the components is predicted according to the working time of the components, so that specific maintenance or replacement is performed. Clearly, the life prediction method is simple and easy to operate, but the prediction result is quite inaccurate. Meanwhile, in order to ensure the safety of the train, the safety coefficient is often improved greatly, so that a plurality of parts with good performance have to be maintained or replaced in advance, and resource waste is caused.
In order to solve the above problems, a more accurate prediction mode appears in the prior art, and the influence of the actual stress condition on the residual life of the component is considered. However, the stress condition of the component is very complex and is dynamically changed, so that the calculation is very complex. For some components, the load data of which are existing data, such as a bogie of a motor car, various stress sensors have been provided for the sake of stress safety. Therefore, the load data can be directly obtained and applied to life prediction. For other components, besides life prediction, such as a spandrel girder, the accurate life prediction is adopted, so that the implementation difficulty is great.
Therefore, how to reduce the complexity of life prediction and improve the accuracy of life prediction and reduce the cost of life prediction on the premise of meeting the accuracy of life prediction becomes a technical problem to be solved in the prior art.
Disclosure of Invention
The embodiment of the invention aims to provide a fatigue life prediction method, a storage medium and a terminal, which can effectively improve the accuracy of predicting the residual life of a part, reduce the complexity of predicting the residual life and improve the resource utilization rate of the part.
The embodiment of the invention provides a fatigue life prediction method, which comprises the following steps of:
s1, acquiring part classification information, wherein the part classification information is used for reflecting the requirement of a part on life prediction precision, whether the residual life is related to load or not and whether load data are existing data or not;
s2, generating a residual life calculation formula group according to the part classification information, wherein the residual life calculation formula group comprises a residual life calculation formula associated with the part classification information;
s3, acquiring the part classification information and the service life information of the part to be monitored;
s4, acquiring a corresponding residual life calculation formula according to the part classification information of the part to be monitored and the residual life calculation formula group;
s5, generating residual life information according to the used life information of the part to be monitored and the corresponding residual life calculation formula.
By adopting the technical scheme, the defect that the residual life is predicted by the existing method for uniformly using the same service life can be avoided, the prediction accuracy of the residual life is improved, the complexity of the residual life prediction is reduced, meanwhile, the economy of the life prediction is considered, the difficulty and the workload of the residual life prediction of the parts are reduced, the efficiency is improved, the full utilization of the parts is realized, and the resource utilization rate is improved.
In one possible implementation, the component classification information in step S1 includes: classification information of the first type of component independent of the load;
and the remaining life calculation formula associated with the classification information of the first type component in step S2 is:
D 1 ＝L 1 -d 1
wherein D is 1 Indicating the remaining life time or the remaining number of times of use of the first type of component, L 1 Indicating the total life time or total number of uses of the first type of component, d 1 Indicating the duration or number of times that the first type of component has been used.
By adopting the technical scheme, the prediction reliability and the economy of the residual life of the first type of components irrelevant to the load can be improved.
In one possible implementation, the component classification information in step S1 includes: classification information of the second type of component related to the load and the load data is the existing data;
and the remaining life calculation formula associated with the classification information of the second class component in step S2 is:
D 2 ＝L 2 -k 1 d 2
wherein D is 2 Indicating the remaining life time or the remaining use times of the second type of component, L 2 Indicating the total life time or total number of uses of the second type of component, d 2 Representing the duration or number of times of use, k, of said second type of component 1 Representing a first equivalent transformation coefficient of said second type of component obtained from existing load data.
By adopting the technical scheme, the second type component can be predicted more accurately and economically, and the accuracy of the residual life of the second type component is improved.
In one possible implementation, the component classification information in step S1 includes: classification information of a third type of parts which are related to the load, have unknown load data and have high prediction accuracy requirements;
step S2 comprises the steps of:
s201, acquiring first monitoring load data of the third type of components according to the classification information of the third type of components, wherein the first monitoring load data is acquired by a newly-arranged acquisition tool;
s202, generating the residual life calculation formula associated with the classification information of the third class of components according to the first monitoring load data, wherein the residual life calculation formula comprises the following components:
D 3 ＝L 3 -k 2 d 3
wherein D is 3 Indicating the remaining life time or the remaining use times of the third type of component, L 3 Indicating the total life time or total number of times of use of the third type of component, d 3 Representing the duration or number of times of use, k, of the third class of components 2 Representing a second equivalent transformation coefficient of the third class of components obtained from the first monitored load data.
By adopting the technical scheme, the method can further predict the accurate residual life time or the residual use times of the third type of parts which are related to the load, unknown in load data and high in prediction precision requirement.
In one possible implementation, the component classification information in step S1 includes: classification information of a fourth type of parts which are related to the load, have unknown load data and have low prediction accuracy requirements;
and step S2 comprises the steps of:
s211, acquiring rough load data of the fourth type of components according to the classification information of the fourth type of components;
s212, generating the residual life calculation formula associated with the classification information of the fourth class of components according to the rough load data, wherein the residual life calculation formula is as follows:
D 4 ＝L 4 -k 3 d 4
wherein D is 4 Indicating the remaining life time or the remaining use times of the fourth type of component, L 4 Indicating the total life time or total use times of the fourth type of components, d 4 Represents the used time length or the used times number, k of the fourth type of components 3 Representing a third equivalent conversion coefficient of the fourth class of components obtained from the coarse load data.
By adopting the technical scheme, the estimation of the residual life of the fourth type of components can be realized, the accuracy can be ensured, and the accuracy and the scientificity of fatigue life prediction are improved; meanwhile, the rough load data are adopted, so that the calculation load of a computer can be reduced.
Embodiments of the present invention also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements any of the above-described fatigue life prediction methods.
The embodiment of the invention also provides a terminal device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes any one of the fatigue life prediction methods when executing the computer program.
Based on the scheme, the invention classifies different parts, introduces different reference factors for the parts classified differently, so that the purpose of accurately predicting the residual life is met, the scientificity and the economy of prediction are improved, and the utilization rate of the parts is further improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to the drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flowchart of a fatigue life prediction method in an embodiment of the invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "axial", "radial", "circumferential", etc. indicate orientations or positional relationships based on the drawings are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention.
In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; the device can be mechanically connected, electrically connected and communicated; either directly, or indirectly, through intermediaries, may be in communication with each other, or may be in interaction with each other, unless explicitly defined otherwise. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
The technical scheme of the invention is described in detail below by specific examples. The following embodiments may be combined with each other, and some embodiments may not be repeated for the same or similar concepts or processes.
FIG. 1 is a flowchart of a fatigue life prediction method in an embodiment of the invention.
As shown in fig. 1, the fatigue life prediction method of the present embodiment includes the steps of:
s1, acquiring part classification information, wherein the part classification information is used for reflecting the requirement of a part on life prediction precision, whether the residual life is related to load or not and whether load data are existing data or not.
The component classification information refers to different accuracy standards, such as high accuracy, medium accuracy and low accuracy, for different components, on one hand, in order to meet different life prediction precision requirements when predicting the residual life; on the other hand, whether the residual life prediction of the part is irrelevant to the load or not and whether new monitoring data need to be introduced or not are distinguished, so that the complexity of the residual life prediction is reduced, and the utilization rate of computer resources is improved. For whether or not it is necessary to introduce monitoring data such as load data, for example, the following is: under low accuracy, no monitoring data need to be introduced, while under high accuracy, specific monitoring data (such as load data) need to be introduced, and the monitoring data is taken into consideration as an influence factor, so that the judging result of the residual life is influenced.
The component classification information is classification information obtained by classifying components using a predetermined standard. One possible basis for classifying a component is a factor related to the prediction of remaining life.
For example. One or more components may be given the same component classification information, such as "first class component", if they are only relevant to the time of use and not to the load data.
Meanwhile, it should be emphasized that the component classification information in step S1 may be a set of component classification information, for example, including first component information, second component information, third component classification information, etc., where each component classification information is identified and distinguished according to a certain standard.
It should be emphasized that the component classification information may be manually added, deleted, modified, or dynamically updated, increased or decreased by a computer according to the prior art, such as machine learning.
The component classification information of a specific component should be one of the component classification information in step S1.
S2, generating a residual life calculation formula group according to the component classification information. Wherein the remaining life calculation formula group includes a remaining life calculation formula associated with the component classification information.
It should be noted that the remaining life calculation formula set may be a functional formula set, and different functional formulas are adopted for different independent variables. The obtaining of the remaining life calculation formula in the remaining life calculation formula group is performed according to the part classification information, and the setting can be performed empirically, or can be obtained according to the statistics of the service life data of a large number of similar parts.
It should be noted that, the remaining life calculation formula may be manually preset according to the component classification information, or may be obtained by machine learning when a lot of remaining life predictions of a certain class of components are processed. For example, a lot of parts are part A. The service life of the component part of the first class is only related to the long used time or the used times through analysis. Thus, the A-type parts can be divided into A-type (their correspondingThe part classification information may also be denoted as a), indicating that the remaining life of the part is only related to the length of time that has been used or the number of times that has been used. Correspondingly, a residual life calculation formula f for the A-class part is manually preset 1 . Thereafter, any component belonging to class A (not only the class A component) can use the remaining life calculation formula f 1 . Of course, the above process can be preset manually according to experience, or can be obtained by a computer in a machine learning mode. Meanwhile, the remaining life calculation formula f 1 And the remaining life calculation formula f of other parts i And a group F of residual life calculation formulas which are formed together.
It should be noted that, when the monitoring data needs to be introduced, the use of the remaining life calculation formula is affected by the introduced monitoring data when the corresponding remaining life calculation formula is generated. One possible way is that the monitoring data may influence the value of a certain parameter in the corresponding remaining life calculation formula.
S3, acquiring the part classification information and the service life information of the part to be monitored.
The part classification information and the service life information of the monitoring part can be manually input from the outside, and can be obtained by a computer by adopting the prior art, such as machine learning and the like. For example, by collecting the material properties or appearance of the part, the corresponding partial classification information and the service life information are obtained in comparison with a pre-established control. Specifically, for example, the brand label on the part to be monitored (the corresponding relation between the brand label and the part classification information is required to be established in advance) is collected to judge the part classification information of the part to be monitored; the used time period is acquired by acquiring a start use date label on the part to be monitored.
It should be noted that, as described above, the component classification information of the component to be monitored should be a subset of the component classification information in step S1. That is, the part classification information of the part to be monitored is one of the part classification information according to S1.
For example. It is assumed that the component classification information from which the remaining life calculation formula group is established includes "classification information of the first type component", "classification information of the second type component", and "classification information of the third type component". One or one to-be-monitored component is identified, and the corresponding component classification information is the classification information of the first type of component. Then, the component classification information corresponding thereto is acquired at this time as "the classification information of the first-type component".
The service life information refers to the used time length or the used times of the part to be monitored.
S4, acquiring a corresponding residual life calculation formula according to the part classification information of the part to be monitored and the residual life calculation formula group.
That is, according to the part classification information of the part to be monitored, a corresponding remaining life calculation formula is acquired in the remaining life calculation formula group.
For example. If the component classification information of a certain component to be monitored is "first class component", in the remaining life calculation formula group F (x), the remaining life calculation formula corresponding to the "first class component" is F (x). Then, the remaining life of the part to be monitored is calculated by using the formula f (x).
S5, generating residual life information according to the used life information of the part to be monitored and the corresponding residual life calculation formula.
In this step, the information of the used life is substituted into the remaining life calculation formula to obtain the corresponding remaining life, such as the remaining use time or the remaining use times.
It is particularly emphasized that this used lifetime information is not necessarily the only variable of the corresponding remaining lifetime calculation formula when other parameters are needed.
By adopting the technical scheme, the defect that the residual life is predicted by the existing method for uniformly using the same service life can be avoided, the prediction accuracy and complexity of the residual life are balanced, the full utilization of the parts is realized and the resource utilization rate is improved under the condition that the prediction accuracy of the residual life of the parts is ensured.
Optionally, in the present embodiment, the component classification information in step S1 of the fatigue life prediction method includes: classification information for the first type of component independent of the load.
And the remaining life calculation formula associated with the classification information of the first type component in step S2 is:
D 1 ＝L 1 -d 1
wherein D is 1 Indicating the remaining life time or the remaining number of times of use of the first type of component, L 1 Indicating the total life time or total number of uses of the first type of component, d 1 Indicating the duration or number of times that the first type of component has been used.
The L is 1 The total life time or total number of uses of the first type of component represented may be obtained from theoretical analysis, or may be obtained experimentally or empirically.
The remaining life information may be a specific value of the remaining life, or may include a processing suggestion for a component that is in the remaining life.
It should be noted that the "first type of component" is merely a division of components whose service life is independent of load, and is not necessarily a specific component. The following "second-type component" and the like are similar to this, and will not be described.
For example. The service life of a certain material is independent of a specific load, the total service life is 1000 hours, the service life is 300 hours now, and the residual service life is (1000-300) =700 hours.
By adopting the technical scheme, the first type of components irrelevant to the load are distinguished, the prediction load of the residual life is reduced, and the economy and the efficiency of the residual life prediction are improved.
Optionally, in the present embodiment, the component classification information in step S1 in the fatigue life prediction method includes: classification information of the second type of component related to the load and the load data is the existing data;
and the remaining life calculation formula associated with the classification information of the second class component in step S2 is:
D 2 ＝L 2 -k 1 d 2
wherein D is 2 Indicating the remaining life time or the remaining use times of the second type of component, L 2 Indicating the total life time or total number of uses of the second type of component, d 2 Representing the duration or number of times of use, k, of said second type of component 1 Representing a first equivalent transformation coefficient of said second type of component obtained from existing load data.
It is to be noted that k 1 The first equivalent conversion coefficient is represented, and possible acquisition modes thereof are as follows:
and analyzing the same type or even the same part by adopting big data analysis to generate a proportional relation between the load data and the standard load data, and generating the influence of the load data on the service life by combining the corresponding actual total service life duration or total use times as a first equivalent conversion coefficient.
For example. If the standard load data is 100 tons and the total service life time is 10000 hours, the data is obtained through big data analysis, and when the actual load data is 120 tons and 5000 hours, the residual service life is reduced by 1.2 hours every 1 hour. Then, the first equivalent conversion coefficient k can be set 1 1.2. At this time, the remaining life calculation formula is: d (D) 2 =10000-1.2×5000=4000 hours.
In particular, the k 1 It may not be a constant value but a function, for example, that varies with load data and with the increase or decrease in the duration or number of uses.
By adopting the technical scheme, the second type component can be further predicted more accurately, and the accuracy of the residual life of the second type component is improved.
Optionally, in the present embodiment, the component classification information in the fatigue life prediction method step S1 includes: the classification information of the third class of parts which are related to the load, have unknown load data and have high prediction accuracy requirements.
And step S2 comprises the steps of:
s201, according to the component classification information, acquiring a first load parameter to be monitored of the third class of components.
It should be noted that, the first load parameter to be monitored refers to a parameter closely related to the remaining life of the third type of component. In particular, the first load parameter to be monitored may be different for different components. Since the load data is unknown, the first monitored load data is acquired by a newly provided acquisition tool.
S202, generating the residual life calculation formula associated with the classification information of the third class of components according to the first monitoring load data, wherein the residual life calculation formula comprises the following components:
D 3 ＝L 3 -k 2 d 3
wherein D is 3 Indicating the remaining life time or the remaining use times of the third type of component, L 3 Indicating the total life time or total number of times of use of the third type of component, d 3 Representing the duration or number of times of use, k, of the third class of components 2 Representing a second equivalent transformation coefficient of the third class of components obtained from the first monitored load data.
Note that k 2 Either by theoretical calculation or experimental data or by empirical values. At the same time k 2 To refer to the previous k 1 And will not be described further again.
It should be noted that, the remaining life calculation formula is specific to a component that needs to be added with a new monitoring term to accurately predict. In the formula, new first monitoring load data are introduced to generate new second equivalent conversion coefficients, so that the prediction of the residual life duration or the residual use times is corrected, and the accuracy of the residual life prediction is improved.
By adopting the technical scheme, the accurate residual life time or residual frequency prediction can be further carried out on the third type of components which are related to the load, have unknown load data and have high prediction precision requirements.
Optionally, in the present embodiment, the component classification information in step S1 in the fatigue life prediction method includes: the classification information of the fourth class of components which are related to the load, have unknown load data and have low prediction accuracy requirements.
Step S2 comprises the steps of:
s211, acquiring rough load data of the fourth type of components according to the classification information of the fourth type of components.
It should be noted that, the rough load data is only acquired when the component to be monitored is judged to be the fourth type of component. The specific acquisition mode can be manually input or automatically set by the equipment. For example, a database is established in which a calculation formula relating to the number of votes sold is stored, and when the number of votes sold is acquired, the average weight value is multiplied by the number of votes to obtain coarse load data.
S212, generating the residual life calculation formula associated with the classification information of the fourth class of components according to the rough load data, wherein the residual life calculation formula is as follows:
D 4 ＝L 4 -k 3 d 4
wherein D is 4 Indicating the remaining life time or the remaining use times of the fourth type of component, L 4 Indicating the total life time or total use times of the fourth type of components, d 4 Represents the used time length or the used times number, k of the fourth type of components 3 Representing a third equivalent conversion coefficient of the fourth class of components obtained from the coarse load data.
Note that k 3 Either by theoretical calculation or experimental data or by empirical values. At the same time k 3 To refer to the previous k 1 And will not be described further again.
By adopting the technical scheme, the estimation of the residual life of the fourth type of components can be realized, the accuracy can be ensured, and the accuracy and the scientificity of fatigue life prediction are improved.
Furthermore, the above-described processes in the embodiments are implemented in the form of software functional units and sold or used as independent products, which may be stored in a computer-readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
In the present invention, unless expressly stated or limited otherwise, a first feature "up" or "down" a second feature may be a direct contact between the first feature and the second feature, or an indirect contact between the first feature and the second feature through an intervening medium.
Moreover, a first feature "above," "over" and "on" a second feature may be a first feature directly above or obliquely above the second feature, or simply indicate that the first feature is higher in level than the second feature. The first feature being "under", "below" and "beneath" the second feature may be the first feature being directly under or obliquely below the second feature, or simply indicating that the first feature is at a lower level than the second feature.
In the description of the present specification, reference to the description of the terms "one embodiment," "some embodiments," "examples," "particular examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (3)
1. A fatigue life prediction method, comprising the steps of:
s1, acquiring part classification information, wherein the part classification information is used for reflecting the requirement of a part on life prediction precision, whether the residual life is related to load or not and whether load data are existing data or not;
s2, generating a residual life calculation formula group according to the part classification information, wherein the residual life calculation formula group comprises a residual life calculation formula associated with the part classification information;
the part classification information in step S1 includes: classification information of the first type of component independent of the load;
and the remaining life calculation formula associated with the classification information of the first type component in step S2 is:
D l ＝L 1 -d 1
wherein D is 1 Indicating the remaining life time or the remaining number of times of use of the first type of component, L 1 Indicating the total life time or total number of uses of the first type of component, d 1 Indicating a duration or a number of times the first type of component has been used;
the part classification information in step S1 includes: classification information of the second type of component related to the load and the load data is the existing data;
and the remaining life calculation formula associated with the classification information of the second class component in step S2 is:
D 2 ＝L 2 -k 1 d 2
wherein D is 2 Indicating the remaining life time or the remaining use times of the second type of component, L 2 Indicating the total life time or total number of uses of the second type of component, d 2 Representing the duration or number of times of use, k, of said second type of component 1 Representing a first equivalent transformation coefficient of said second class of components obtained from existing load data;
the part classification information in step S1 includes: classification information of a third type of parts which are related to the load, have unknown load data and have high prediction accuracy requirements;
step S2 comprises the steps of:
s201, acquiring first monitoring load data of the third type of components according to the classification information of the third type of components, wherein the first monitoring load data is acquired by an acquisition tool;
s202, generating the residual life calculation formula associated with the classification information of the third class of components according to the first monitoring load data, wherein the residual life calculation formula comprises the following components:
D 3 ＝L 3 -k 2 d 3
wherein D is 3 Indicating the remaining life time or the remaining use times of the third type of component, L 3 Representing the sum of the third class of componentsDuration of life or total number of uses d 3 Representing the duration or number of times of use, k, of the third class of components 2 Representing a second equivalent transformation coefficient of the third class of components obtained from the first monitored load data;
the component classification information in step S1 includes: classification information of a fourth type of parts which are related to the load, have unknown load data and have low prediction accuracy requirements;
and step S2 comprises the steps of:
s211, acquiring rough load data of the fourth type of components according to the classification information of the fourth type of components;
s212, generating the residual life calculation formula associated with the classification information of the fourth class of components according to the rough load data, wherein the residual life calculation formula is as follows:
D 4 ＝L 4 -k 3 d 4
wherein D is 4 Indicating the remaining life time or the remaining use times of the fourth type of component, L 4 Indicating the total life time or total use times of the fourth type of components, d 4 Represents the used time length or the used times number, k of the fourth type of components 3 A third equivalent conversion coefficient representing the fourth class of components obtained from the coarse load data;
s3, acquiring the part classification information and the service life information of the part to be monitored;
s4, acquiring a corresponding residual life calculation formula according to the part classification information of the part to be monitored and the residual life calculation formula group;
s5, generating residual life information according to the used life information of the part to be monitored and the corresponding residual life calculation formula.
2. A computer readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the fatigue life prediction method as in claim 1.
3. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the fatigue life prediction method as in claim 1 when executing the computer program.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110197288A (en) * | 2019-05-30 | 2019-09-03 | 重庆大学 | The remaining life prediction technique of equipment under the influence of failure |
| CN110222436A (en) * | 2019-06-12 | 2019-09-10 | 中国神华能源股份有限公司 | Appraisal procedure, device and the storage medium of Train Parts health status |
| CN110609524A (en) * | 2019-08-14 | 2019-12-24 | 华中科技大学 | Industrial equipment residual life prediction model and construction method and application thereof |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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-
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110197288A (en) * | 2019-05-30 | 2019-09-03 | 重庆大学 | The remaining life prediction technique of equipment under the influence of failure |
| CN110222436A (en) * | 2019-06-12 | 2019-09-10 | 中国神华能源股份有限公司 | Appraisal procedure, device and the storage medium of Train Parts health status |
| CN110609524A (en) * | 2019-08-14 | 2019-12-24 | 华中科技大学 | Industrial equipment residual life prediction model and construction method and application thereof |
Non-Patent Citations (2)
| Title |
|---|
| Remaining lifetime modeling using State-of-Health estimation;Nejra Beganovic 等;《Mechanical Systems and Signal Processing》;第92卷;107-123 * |
| 精密机电设备寿命评估方法;胡昌华 等;《导航与控制》;第17卷(第1期);21-33 * |
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