CN113378286A - Fatigue life prediction method, storage medium and terminal - Google Patents
Fatigue life prediction method, storage medium and terminal Download PDFInfo
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- CN113378286A CN113378286A CN202010163836.9A CN202010163836A CN113378286A CN 113378286 A CN113378286 A CN 113378286A CN 202010163836 A CN202010163836 A CN 202010163836A CN 113378286 A CN113378286 A CN 113378286A
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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 comprises the following steps: s1, acquiring component classification information; s2, generating a residual life calculation formula group according to the component classification information, wherein the residual life calculation formula group comprises residual life calculation formulas associated with the component classification information; s3, acquiring the component classification information and the used service life information of the component to be monitored; s4, acquiring a corresponding residual life calculation formula according to the component classification information of the component to be monitored and the residual life calculation formula group; and S5, generating the residual life information according to the used life information of the component to be monitored and the corresponding residual life calculation formula. The fatigue life prediction method can give consideration to the accuracy and the 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 subject to wheel track excitation from rail irregularities and also to internal excitation between vehicle components due to manufacturing processes and equipment clearances. Under the two types of cooperation, the dynamic response of the vehicle component structure is complex, resonance forms of various parameters such as sub-harmonic resonance, super-harmonic resonance and the like easily appear under the excitation of certain specific frequencies, the service environment of the component is adversely affected, and the resonance failure of a system is caused in severe cases.
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 carried out. Undoubtedly, the service 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 greatly improved, 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 method is provided 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 components is often very complex and is often dynamically changed, so that the calculation is very complex. For some components, the load data of which is existing data, such as the bogie of a motor car, various stress sensors have been provided for stress safety. Therefore, the load data can be directly acquired and applied to the life prediction. For other parts, such as the load-bearing beam, besides the service life prediction, the accurate service life prediction is difficult to realize.
Therefore, how to reduce the complexity of life prediction, 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 urgently 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, aiming at the problems, the accuracy of the residual life prediction of parts can be effectively improved, the complexity of the residual life prediction is reduced, and the resource utilization rate of the parts is improved.
The embodiment of the invention provides a fatigue life prediction method, which comprises the following steps:
s1, acquiring component classification information, wherein the component classification information is used for reflecting the requirements of components on the service life prediction precision, whether the residual service life is related to the load and whether the load data is the existing data;
s2, generating a residual life calculation formula group according to the component classification information, wherein the residual life calculation formula group comprises residual life calculation formulas associated with the component classification information;
s3, acquiring the component classification information and the used service life information of the component to be monitored;
s4, acquiring a corresponding residual life calculation formula according to the component classification information of the component to be monitored and the residual life calculation formula group;
and S5, generating the residual life information according to the used life information of the component to be monitored and the corresponding residual life calculation formula.
By adopting the technical scheme, the defect that the residual service life is predicted by using the conventional method for uniformly using the same service life can be overcome, the prediction accuracy of the residual service life is improved, the complexity of the residual service life prediction is reduced, the economy of the service life prediction is considered, the difficulty and the workload of the residual service life prediction of the parts are reduced, the efficiency is improved, the parts are fully utilized, and the resource utilization rate is improved.
In one possible approach, 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 class component in step S2 is:
D1＝L1-d1
wherein D is1Indicating the remaining life duration or the number of remaining uses, L, of said first type of component1Indicating the total life-time or total number of uses of the first type of componentNumber, d1Indicating the length of time the first type of component has been used or the number of times it has been used.
By adopting the technical scheme, the reliability and the economy of predicting the residual life of the first type of components irrelevant to the load can be improved.
In one possible approach, the component classification information in step S1 includes: classification information of a second type of component related to the load and having load data as existing data;
and the remaining life calculation formula associated with the classification information of the second class component in step S2 is:
D2＝L2-k1d2
wherein D is2Indicating the remaining life duration or the number of remaining uses, L, of said second type of component2Representing the total life duration or total number of uses of said second type of component, d2Indicating the length of time used or the number of times used, k, of said second type of component1Representing a first equivalence transformation factor for said second class of components obtained from existing load data.
By adopting the technical scheme, the second type of component can be predicted more accurately and economically, and the accuracy of the residual life of the second type of component is improved.
In one possible approach, the component classification information in step S1 includes: classification information of a third class component which is related to the load, has unknown load data and has high prediction accuracy requirement;
step S2 includes the following steps:
s201, acquiring first monitoring load data of the third type of component according to the classification information of the third type of component, wherein the first monitoring load data is acquired by a newly-arranged acquisition tool;
s202, according to the first monitoring load data, generating the residual life calculation formula associated with the classification information of the third type of component as follows:
D3＝L3-k2d3
wherein D is3Indicating the remaining life duration or the number of remaining uses, L, of said third type of component3Representing the total life duration or total number of uses of said third type of component, d3Indicating the length of time used or number of times used, k, of said third type of component2A second equivalent transformation factor representing the third class of components obtained from the first monitored load data.
By adopting the technical scheme, the accurate residual service life duration or residual using times of the third type of component which is related to the load, has unknown load data and has high prediction accuracy requirement can be predicted.
In one possible approach, the component classification information in step S1 includes: classification information of a fourth class of components related to the load, having unknown load data and having low prediction accuracy requirements;
and step S2 includes the steps of:
s211, acquiring rough load data of the fourth type of component according to the classification information of the fourth type of component;
s212, according to the rough load data, generating the remaining life calculation formula associated with the classification information of the fourth class component as follows:
D4＝L4-k3d4
wherein D is4Indicating the remaining life duration or the number of remaining uses, L, of said fourth type of component4Representing the total life duration or total number of uses of said fourth type of component, d4Indicating the length of time used or number of times used, k, of said fourth type of component3A third equivalent transformation factor representing said fourth class of components obtained from said coarse load data.
By adopting the technical scheme, the residual life of the fourth type of component can be estimated, the accuracy can be ensured, and the accuracy and the scientificity of fatigue life prediction are improved; meanwhile, the load data of the rough load is adopted, so that the operation burden of the computer can be reduced.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements any one of the above fatigue life prediction methods.
The embodiment of the present invention further provides a terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the processor implements any one of the above fatigue life prediction methods.
Based on the scheme, different parts are classified, and different reference factors are introduced for the parts of different classifications, so that the purpose of accurately predicting the residual service life is met, the scientificity and the economy of prediction are improved, and the utilization rate of the parts is improved.
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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 needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of a fatigue life prediction method according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "axial," "radial," "circumferential," and the like are used in the indicated orientations and positional relationships based on the drawings for convenience in describing and simplifying the description, but do not indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the invention.
In the present invention, unless otherwise specifically stated or limited, the terms "mounted," "connected," "fixed," and the like are to be construed broadly and may, for example, be fixedly connected, detachably connected, or integrally formed; the connection can be mechanical connection, electrical connection or communication connection; either directly or indirectly through intervening media, either internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
The technical solution of the present invention will be described in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
Fig. 1 is a flowchart of a fatigue life prediction method according to an embodiment of the present invention.
As shown in fig. 1, the fatigue life prediction method of the present embodiment includes the following steps:
and S1, acquiring component classification information, wherein the component classification information is used for reflecting the requirements of the components on the life prediction precision, whether the residual life is related to the load and whether the load data is the existing data.
The component classification information refers to different accuracy standards, such as high accuracy, medium accuracy and low accuracy, adopted to meet different requirements of life prediction accuracy on one hand when residual life prediction is performed on different parts; on the other hand, whether the residual life prediction of the part is unrelated to the load or not and whether new monitoring data needs 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 example, the following is given for the case that monitoring data such as load data needs to be introduced: at low accuracy, the monitoring data is not required to be introduced, and at high accuracy, specific monitoring data (such as load data) is required to be introduced and taken into consideration as an influence factor, so that the judgment result of the residual service life is influenced.
The component classification information is classification information obtained by classifying components using a predetermined standard. One possible basis for classifying components is a factor related to remaining life prediction.
For example. If one or more components are only relevant to the duration of use and not to the load data, the component may be given the same component classification information, such as "first class component".
Meanwhile, it should be emphasized that the component classification information in step S1 may be a set of component classification information, such as a set including first component information, second component information, third component classification information, and the like, where each component classification information is identified and distinguished according to a certain criterion.
It is particularly emphasized that the component classification information can be manually updated, added, deleted, modified, or dynamically updated, increased or decreased by a computer according to the prior art, such as machine learning.
Note that, for a specific component, the component classification information included in the component should be one of the component classification information in step S1.
And 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 function formula set, and different function formulas are adopted for different independent variables. The remaining life calculation formulas in the remaining life calculation formula group are obtained by setting according to the classification information of the components, can be set by experience, and can also be obtained by counting according to the service life data of a large number of similar components.
It should be noted that the remaining life calculation formula may be preset manually according to the component classification information, or may be obtained by machine learning when the remaining life of a certain type of component is predicted in a large amount. For example, there is a batch of parts that are nail type parts. Through analysis, the service life of the nail type part is only related to the used time or the used times. Therefore, the class a parts can be classified into class a (the corresponding part classification information thereof can also be represented as a), which indicates that the remaining life of the parts is only related to the used time length or the used times. Correspondingly, a residual life calculation formula f for the class A parts is preset manually1. Thereafter, any component belonging to class A (other than class A components) can be used by the remaining life calculation formula f1. Of course, the above processes can be preset manually according to experience, and can also be obtained by a computer in a machine learning manner. Meanwhile, the remaining life calculation formula f1And the formula f for calculating the remaining life of other partsiAnd a residual life calculation formula group F is formed together.
It should be noted that, when the monitoring data needs to be introduced, the introduced monitoring data may affect the usage of the remaining life calculation formula 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 component classification information and the used service life information of the component to be monitored.
The component classification information and the used service life information of the monitoring component can be input manually from the outside or can be obtained by a computer by adopting the prior art, such as machine learning and the like. For example, by collecting material properties or appearance of the part, and comparing with a pre-established control, corresponding partial classification information and used life information is obtained. Specifically, for example, the part classification information of the part to be monitored is judged by collecting a brand label (a corresponding relation between the brand label and the part classification information needs to be established in advance) on the part to be monitored; the used time is obtained by collecting a date of use label on the component to be monitored.
It should be noted that, as mentioned 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 component classification information of the component to be monitored is one of the component classification information in S1.
For example. It is assumed that the component classification information from which the remaining life calculation formula set is established includes "classification information of a first class component", "classification information of a second class component", and "classification information of a third class component". One or one type of component to be monitored is identified, and the classification information of the component corresponding to the component to be monitored is the classification information of the first type of component. Then, at this time, the classification information of its corresponding component is acquired as "classification information of the first-class component".
The used life information refers to the used time or the number of times of the component to be monitored.
S4, obtaining the corresponding residual life calculation formula according to the component classification information of the component to be monitored and the residual life calculation formula group.
That is, according to the component classification information of the component to be monitored, a corresponding remaining life calculation formula is obtained in the remaining life calculation formula group.
For example. If the component classification information of a certain component to be monitored is "first type component", the remaining life calculation formula corresponding to the "first type component" in the remaining life calculation formula group f (x) is f (x). Then, the formula for calculating the remaining life of the component to be monitored is f (x).
And S5, generating the residual life information according to the used life information of the component to be monitored and the corresponding residual life calculation formula.
In this step, the used life information is substituted into the remaining life calculation formula to obtain the corresponding remaining life, such as the remaining usage time or the remaining number of uses.
It is particularly emphasized that the used life information is not necessarily the only variable of the corresponding remaining life calculation formula when other parameters are required.
By adopting the technical scheme, the defect that the residual service life is predicted by using the conventional method for uniformly using the same service life can be overcome, the prediction accuracy and complexity of the residual service life are balanced, the parts are fully utilized under the condition of ensuring the prediction accuracy of the residual service life of the parts, and the resource utilization rate is improved.
Optionally, in this embodiment, the component classification information in step S1 of the fatigue life prediction method 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 class component in step S2 is:
D1＝L1-d1
wherein D is1Indicating the remaining life duration or the number of remaining uses, L, of said first type of component1Representing the total life-time or total number of uses of said first type of component, d1Indicating the length of time the first type of component has been used or the number of times it has been used.
Note that L is1The indicated total life time or total number of uses of the first type of component may be obtained from theoretical analysis, or may be obtained from experimentation or experience.
The remaining life information may be a specific value of the remaining life, or may include a processing recommendation for the component in the remaining life.
It should be noted that the "first type component" is merely a division of a certain type of component whose service life is independent of the load, and is not necessarily a specific component. The following "second type of component" and the like are similar and will not be described again.
For example. The service life of a certain material is independent of specific load, the total service life is 1000 hours, the used time is 300 hours, and the remaining service life is 700 hours (1000- & ltSUB & gt 300- & ltSUB & gt).
By adopting the technical scheme, the first type of components irrelevant to the load are distinguished, the prediction burden of the residual service life is reduced, and the economy and the efficiency of the residual service life prediction are improved.
Optionally, in this embodiment, the component classification information in step S1 in the fatigue life prediction method includes: classification information of a second type of component related to the load and having load data as existing data;
and the remaining life calculation formula associated with the classification information of the second class component in step S2 is:
D2＝L2-k1d2
wherein D is2Indicating the remaining life duration or the number of remaining uses, L, of said second type of component2Representing the total life duration or total number of uses of said second type of component, d2Indicating the length of time used or the number of times used, k, of said second type of component1Representing a first equivalence transformation factor for said second class of components obtained from existing load data.
In particular, k is1The first equivalent transformation coefficient represented may be obtained as follows:
and analyzing the same type or even the same component 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, wherein the influence is used as a first equivalent conversion coefficient.
For example. If the standard load data is 100 tons and the total service life duration is 10000 hours, the residual service life is reduced by 1.2 hours every 1 hour when the actual load data is 120 tons and 5000 hours through big data analysis. Then, the first equivalent conversion coefficient k may be set1Is 1.2. At this time, the remaining life calculation formula is: d210000-1.2 × 5000-4000 hours.
In particular, k is1It may not be a constant value but a function, for example, which varies with the increase and decrease of the load data and the length of use or the number of uses.
By adopting the technical scheme, the second type of component can be further predicted more accurately, and the accuracy of the residual life of the second type of component is improved.
Optionally, in this embodiment, the component classification information in step S1 of the fatigue life prediction method includes: classification information of a third type of component that is load-related, has unknown load data, and requires high prediction accuracy.
And step S2 includes the steps of:
s201, acquiring a first load parameter to be monitored of the third type of component according to the component classification information.
It should be noted that the first load parameter to be monitored is a parameter closely related to the remaining life of the third type component. In particular, the first load parameter to be monitored may differ from component to component. Since the load data is unknown, the first monitored load data is collected by a newly-built collection tool.
S202, according to the first monitoring load data, generating the residual life calculation formula associated with the classification information of the third type of component as follows:
D3＝L3-k2d3
wherein D is3Indicating the remaining life duration or the number of remaining uses, L, of said third type of component3Representing the total life duration or total number of uses of said third type of component, d3Indicating the length of time used or number of times used, k, of said third type of component2A second equivalent transformation factor representing the third class of components obtained from the first monitored load data.
In addition, k is2Can be obtained by theoretical calculation or experimental data, and can also be obtained byAnd setting an experimental value. At the same time, k2To refer to k above1And will not be further described again.
It should be noted that the remaining life calculation formula is for a component that needs to add a new monitoring term to make an accurate prediction. In the formula, a new second equivalent conversion coefficient is generated by introducing new first monitoring load data, so that the prediction of the residual service life duration or the residual using times is corrected, and the accuracy of the residual service life prediction is improved.
By adopting the technical scheme, the accurate residual life duration or residual frequency of the third-class component which is related to the load, has unknown load data and has high prediction accuracy requirement can be predicted.
Optionally, in this embodiment, the component classification information in step S1 in the fatigue life prediction method includes: classification information of a fourth class of components related to the load, for which load data is unknown, and for which prediction accuracy requirements are low.
Step S2 includes the following steps:
and S211, acquiring the rough load data of the fourth type of component according to the classification information of the fourth type of component.
It should be noted that the rough load data is obtained only when the component to be monitored is determined to be the fourth type component. The specific acquisition mode can be manually input or automatically set by equipment. For example, a database is established in which a calculation formula related to the number of sold tickets is stored, and when the number of sold tickets is acquired, the average weight value is multiplied by the number of tickets to obtain rough load data.
S212, according to the rough load data, generating the remaining life calculation formula associated with the classification information of the fourth class component as follows:
D4＝L4-k3d4
wherein D is4Indicating the remaining life duration or the number of remaining uses, L, of said fourth type of component4Representing the total life of said fourth type of componentLong or total number of uses, d4Indicating the length of time used or number of times used, k, of said fourth type of component3A third equivalent transformation factor representing said fourth class of components obtained from said coarse load data.
In addition, k is3The method can be obtained through theoretical calculation or experimental data, and can also be set according to empirical values. At the same time, k3To refer to k above1And will not be further described again.
By adopting the technical scheme, the residual life of the fourth type of component can be estimated, the accuracy can be ensured, and the accuracy and the scientificity of fatigue life prediction are improved.
In addition, when the above-described processes in the embodiments are implemented in the form of software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
In the present invention, unless otherwise explicitly specified or limited, the first feature "on" or "under" the second feature may be directly contacting the first feature and the second feature or indirectly contacting the first feature and the second feature through an intermediate.
Also, a first feature "on," "above," and "over" a second feature may mean that the first feature is directly above or obliquely above the second feature, or that only the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lower level than the second feature.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example" or "some examples," or the like, 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, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. A fatigue life prediction method, comprising the steps of:
s1, acquiring component classification information, wherein the component classification information is used for reflecting the requirements of components on the service life prediction precision, whether the residual service life is related to the load and whether the load data is the existing data;
s2, generating a residual life calculation formula group according to the component classification information, wherein the residual life calculation formula group comprises residual life calculation formulas associated with the component classification information;
s3, acquiring the component classification information and the used service life information of the component to be monitored;
s4, acquiring a corresponding residual life calculation formula according to the component classification information of the component to be monitored and the residual life calculation formula group;
and S5, generating the residual life information according to the used life information of the component to be monitored and the corresponding residual life calculation formula.
2. A fatigue life prediction method according to claim 1, wherein 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 class component in step S2 is:
Dl＝L1-d1
wherein D is1Indicating the remaining life duration or the number of remaining uses, L, of said first type of component1Representing the total life-time or total number of uses of said first type of component, d1Indicating the length of time the first type of component has been used or the number of times it has been used.
3. A fatigue life prediction method according to claim 1, wherein the component classification information in step S1 includes: classification information of a second type of component related to the load and having load data as existing data;
and the remaining life calculation formula associated with the classification information of the second class component in step S2 is:
D2＝L2-k1d2
wherein D is2Indicating the remaining life duration or the number of remaining uses, L, of said second type of component2Representing the total life duration or total number of uses of said second type of component, d2Indicating the length of time used or the number of times used, k, of said second type of component1Representing a first equivalence transformation factor for said second class of components obtained from existing load data.
4. A fatigue life prediction method according to claim 1, wherein the component classification information in step S1 includes: classification information of a third class component which is related to the load, has unknown load data and has high prediction accuracy requirement;
step S2 includes the following steps:
s201, acquiring first monitoring load data of the third type of component according to the classification information of the third type of component, wherein the first monitoring load data is acquired by a newly-arranged acquisition tool;
s202, according to the first monitoring load data, generating the residual life calculation formula associated with the classification information of the third type of component as follows:
D3＝L3-k2d3
wherein D is3Indicating the remaining life duration or the number of remaining uses, L, of said third type of component3Representing the total life duration or total number of uses of said third type of component, d3Indicating the length of time used or number of times used, k, of said third type of component2A second equivalent transformation factor representing the third class of components obtained from the first monitored load data.
5. The fatigue life prediction method according to claim 1, wherein the component classification information in step S1 includes: classification information of a fourth class of components related to the load, having unknown load data and having low prediction accuracy requirements;
and step S2 includes the steps of:
s211, acquiring rough load data of the fourth type of component according to the classification information of the fourth type of component;
s212, according to the rough load data, generating the remaining life calculation formula associated with the classification information of the fourth class component as follows:
D4＝L4-k3d4
wherein D is4Indicating the remaining life duration or the number of remaining uses, L, of said fourth type of component4Representing the total life duration or total number of uses of said fourth type of component, d4Indicating the length of time used or number of times used, k, of said fourth type of component3A third equivalent transformation factor representing said fourth class of components obtained from said coarse load data.
6. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the fatigue life prediction method according to any one of claims 1 to 5.
7. 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 according to any one of claims 1 to 5 when executing the computer program.
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