CN109033615A - Autocrane jib structure methods of risk assessment based on potential failure mode - Google Patents
Autocrane jib structure methods of risk assessment based on potential failure mode Download PDFInfo
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Abstract
The present invention relates to a kind of autocrane jib structure methods of risk assessment based on potential failure mode, suitable for the prediction of in-service autocrane steel flanged beam jib structure failure mode and its safety evaluation, it is achieved by the steps of: (1) builds failure mode fuzzy database；(2) prediction and amendment of potential failure mode；(3) foundation of risk assessment index set；(4) evaluation index weight is calculated；(5) risk of in-service autocrane jib structure is determined.The present invention solves that the failure test period is long, costly jib structure safety is caused to be difficult to determining engineering problem, and how effectively to predict the failure mode of autocrane jib structure, become passive maintenance as active maintenance, the incidence of failure is greatly lowered, the technical issues of prolonging its service life, improving the rapidity and accuracy that cantilever crane structure risk is assessed under potential failure mode.
Description
Technical field
The invention belongs to crane field of engineering technology, are related to a kind of methods of risk assessment of Crane Jib Structure, special
It is not to be related to a kind of autocrane jib structure methods of risk assessment based on potential failure mode.
Background technique
Primary load bearing structure part of the jib structure system as autocrane, in long-term use process, since machinery carries
The influence for phenomenon of colliding in lotus, environmental condition, corrosion factor and transport, assembling process, and violation operation, design defect,
Manufacturing defect and maintenance system fall behind relatively, and leading to jib structure, there are major defect, various failure modes, failure
Form emerges one after another, and fatal crass's class accident happens occasionally, and very big hidden danger is brought to safety work, has seriously affected construction
Progress, benefit and the people's property safety of project.Currently, there are two types of the main maintenance modes of hoisting machinery industry: " fire fighting formula
Maintenance " and " periodicmaintenance ".The former is passive type maintenance, and with " failure " for cost, maintenance period is long, sets caused by glitch
Standby paralysis happens occasionally；The latter is active maintenance, is safeguarded by periodically frequent shutdown, is reduced to a certain extent
The generation of catastrophic failure, but excessively frequent maintenance can greatly increase the maintenance cost of client.Therefore, how automobile is effectively predicted
The potential failure mode of Crane Jib Structure becomes passive maintenance as actively targetedly maintenance, failure is being greatly lowered
Incidence while the maintenance cost of client's periodicmaintenance is effectively reduced, prolong its service life, improve coupling failure mould
The rapidity and accuracy that cantilever crane structure risk is assessed under formula, it has also become current problem urgently to be resolved.
Summary of the invention
In order to solve deficiency present in " maintenance of fire fighting formula " in the prior art and " periodicmaintenance ", the invention discloses
A kind of autocrane jib structure methods of risk assessment based on potential failure mode, by failure mode fuzzy database,
In conjunction with case illation technology, the potential failure mode of Crane Jib Structure to be assessed is predicted, utilize autocrane cantilever crane knot
Structure parameter finite element model and simulation calculation platform correct potential failure mode, detect be corresponding to it dangerouse cross-section and danger
Point (i.e. test point), obtains the testing result of each dangerous point at this stage, using DEMATEL method is improved, in conjunction with fuzzy overall evaluation
Theory quantifies the failure risk of autocrane jib structure to be assessed.This method has fully considered that risk assessment relies on expert
The uncertainty and ambiguity of experience and knowledge have proposed cantilever crane structure risk is assessed under high coupling failure mode rapidity and standard
True property.
The technical scheme of the present invention is realized as follows:
The invention discloses a kind of autocrane jib structure methods of risk assessment based on potential failure mode comprising such as
Lower step:
(1) failure mode fuzzy database is built
When establishing failure mode fuzzy database, according to simplifying as far as possible, the principle that library should not be excessive is built, according to autocrane
" offline → use → regular inspection → failure " process, establishes autocrane jib structure failure mode fuzzy database, at least wraps
Three primitive character parameter subdata base, typically used as operating condition subdata base and failure mode subdata base subdata bases are included, respectively
Same model is identified by matching to crane model identification and detection serial number mark and detects serial number by subdata base
Autocrane primitive character parameter, typically used as operating condition and the failure mode of mark form a case history；
(2) prediction and amendment of potential failure mode
On the basis of step (1), autocrane jib structure failure mould under same type, different typically used as operating conditions is established
Formula case library；In-service autocrane jib structure to be assessed, which then passes through, collects typically used as operating condition, and as target
Example determines object instance corresponding with the evaluation index of typically used as operating condition and case library with case illation technology is improved
Similarity between middle source instance；When there is corresponding best similar case in each evaluation index of typically used as operating condition, group
At best similar case collection, the failure mode that best similar case concentrates each element is exported, and as potential failure mode；Work as allusion quotation
When each evaluation index of type applying working condition is no or part does not have corresponding best similar case, according to expertise to mistake
Effect mode is estimated, on this basis, flat by autocrane jib structure parameter finite element model and simulation calculation
Platform is modified the result of failure mode, and the result after forecast value revision is saved in failure mode as new case and is obscured
In database；
(3) foundation of risk assessment index set
On the basis of step 2, the distribution of dangerous point on dangerouse cross-section is corresponded to according to failure mode, establishes wind in conjunction with failure criteria
Dangerous evaluation index collection；
(4) evaluation index weight is calculated
In view of the finiteness of jib structure failure mode fuzzy database, the uncertainty of expert and its experience and knowledge
Ambiguity, with fuzzy improvement DEMATEL method, the detection knot of the test point obtained in conjunction with non-destructive testing technology (i.e. dangerous point)
Fruit determines the weight of each risk assessment index of autocrane jib structure；
(5) risk of in-service autocrane jib structure is determined
Based on the weight coefficient of each test point of autocrane jib structure, the synthetic evaluation matrix of test point is established, arm is obtained
The synthetic evaluation matrix of frame structure carries out sharpening processing to evaluation result using parameter level method, finally obtains jib structure
Whole risk.
Further, in step (1), the typically used as operating condition includes the working level of jib structure, loading spectrum, stretches
Cylinder efficient mode, supporting leg working condition and overload service condition；The primitive character parameter includes running parameter, operating speed
With shell system structural parameters, the running parameter include maximum rated lifting capacity, basic arm maximum hoisting moment, basic arm most
Big elevating height, principal arm maximum lifting height, auxiliary maximum lifting height；The operating speed includes single rope maximum speed, rises
Weighing arm plays the width time, crane arm falls width time, crane arm and stretches time, crane arm full reduced time, rotational speed entirely；The skeleton system
System structural parameters include the parameters of structural dimension of material, cantilever crane type, arm joint number, principal arm section form and each arm joint.
Further, in step (2), the improvement case illation technology is by quadratic search strategy and improved Europe
Formula distance algorithm establishes the case retrieval optimization side during in-service autocrane jib structure potential failure mode prediction
Case determines the best similar case of each evaluation index under typically used as operating condition；
Autocrane jib structure parameter finite element model and emulation in step (2) as a preferred implementation manner,
Computing platform, using Visual studio2013 as developing instrument, is utilized using microsoft.Net Framework as platform
Encapsulation and calling of the C# language to ANSYS parametrization file, develop autocrane jib structure parameter under different applying working conditions
Change finite element model and simulation calculation platform, by the primitive character parameter, the allusion quotation that input autocrane jib structure to be assessed
Type applying working condition quantifies the evaluation mode of its failure mode, is modified to potential failure mode.
As a preferred implementation manner, in step (4), the fuzzy improvement DEMATEL method is by constructing wind at different levels
Between dangerous evaluation index it is direct, influence fuzzy relation matrix indirectly, derive the disturbance degree matrixes of evaluation indexes at different levels with by shadow
Loudness matrix analyzes the relevance between evaluation index and influence property to obtain the centrad and reason degree of evaluation indexes at different levels,
Weight is distributed according to correlation degree and influence degree.
Compared with prior art, the present invention has the effect that
(1) present invention can cause safe shadow for all based on failure mode fuzzy database to vibrative mechanism
Including loud potential failure mode information all covers, with the time-division consider risk assessment rely on expertise and knowledge not really
Qualitative and ambiguity effectively prevents failure test period length, drawback at high cost under coupling failure mode, quantifies vapour to be assessed
The potential failure risk of vehicle Crane Jib Structure can in time, quickly with accurately carry out before jib structure destroys
Targeted active maintenance can not only be greatly lowered the incidence of failure, prolong its service life；And it avoids excessively
Frequent maintenance increases excessive customer repair cost, can effectively predict the potential failure mould of autocrane jib structure
Formula becomes passive maintenance as actively targetedly maintenance, has important practical significance.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention without any creative labor, may be used also for those of ordinary skill in the art
To obtain other drawings based on these drawings.
Fig. 1 is the general frame of autocrane jib structure risk assessment of the present invention.
Fig. 2 is the general frame of autocrane jib structure failure mode fuzzy database.
Fig. 3 is the pre- flow gauge of autocrane jib structure failure mode.
Fig. 4 is the general frame of autocrane jib structure failure mode case library.
Fig. 5 is the snibbing position of telescopic oil cylinder on cantilever crane No.1.
Fig. 6 is corrected based on the potential failure mode of parameter finite element model and simulation calculation platform.
Fig. 7 is jib structure risk assessment index.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
Embodiment
Autocrane jib structure methods of risk assessment based on potential failure mode as shown in Figure 1, specific steps
It is as follows:
Step 1: building failure mode fuzzy database
For anti fuzzy method process cumbersome in the ambiguity feature and data information use process of failure mode information data,
Autocrane jib structure failure mode fuzzy database is established in proposition, so that data during collection, not only include essence
True quantitative data description, also comprising fuzzy qualitative data information；When establishing failure mode fuzzy database, according to smart as far as possible
The principle that library should not be excessive is built in letter, from the process of autocrane " offline → use → regular inspection → failure ", establishes failure mode
Fuzzy database.As shown in Fig. 2, failure mode fuzzy database includes primitive character parameter subdata base, typically used as operating condition
Subdata base and failure mode subdata base, each subdata base pass through crane model identification and detection serial number mark progress
Match, crane primitive character parameter, typically used as operating condition and the failure mode of same model mark and same detection serial number mark
Form a case history.Primitive character parameter subdata base is quiet for storing Crane Jib Structure 3S × 2(Strength-
Intensity, fatigue strength, Stiffness-Static stiffness, dynamic stiffness, Stability-local stability, overall stability) calculate,
The initial parameter of emulation, failure analysis, risk assessment, fatigue surplus life estimation, the work ginseng including different crane types
Number (maximum rated lifting capacity, basic arm maximum hoisting moment, basic arm maximum lifting height, principal arm maximum lifting height, auxiliary
Maximum lifting height), operating speed (single rope maximum speed, crane arm play width/fall the width time, crane arm stretch entirely/full reduced time,
Rotational speed), boom system structural parameters (material, cantilever crane type, arm joint number, principal arm section form and each arm joint structure ruler
Very little parameter)；Typically used as operating condition subdata base is used to store normal/improper operation information of crane, including different liftings
Machine type, the jib structure working level (service rating, stress state rank) of different detection serial numbers, loads typical spectrum (lifting
Amount, cantilever crane active length, work range and working cycles number), (each arm joint hydraulic cylinder is inserted for the working method of telescopic hydraulic cylinder
Pins position is set), supporting leg working condition and overload service condition；Failure mode subdata base is used to store the detection of each arm joint of crane
And failure mode information as a result.
For realize autocrane jib structure failure mode historical information express statistic, with Microsoft Access
Based on 2013 databases, the visual design tool provided using VC++ works out visual information input interface, realizes failure
The operation such as addition, modification, insertion, deletion of mode history information.
Step 2: the prediction and amendment of potential failure mode
1) potential failure mode prediction and modified detailed process
As shown in figure 3, establishing lifting under same type, different typically used as operating conditions based on failure mode fuzzy database
Horn frame structural failure schema instance library.It is typical by collecting for in-service autocrane jib structure to be assessed
Applying working condition, as object instance, with improved case illation technology, determining respectively evaluate with typically used as operating condition refers to
The similarity between corresponding object instance and source instance (example stored in case library) is marked, if typically used as operating condition is respectively commented
There is corresponding best similar case in valence index, form best similar case collection, exports best similar case and concentrates each element
Failure mode, and the potential failure mode as autocrane jib structure to be assessed, otherwise according to expertise to latent
It is estimated in failure mode, on this basis, is counted by autocrane jib structure parameter finite element model and emulation
Platform is calculated, the result of potential failure mode is modified, and be saved in failure mode for revised result as new example
In fuzzy database, to improve database.
2) building of failure mode case library
As shown in figure 4, autocrane jib structure failure mode case library is by the typically used as operating condition of crane and is corresponding to it
Jib structure failure mode composition.The judging quota of the typically used as operating condition of crane includes the working level of jib structure, carries
Lotus spectrum, telescopic oil cylinder working method, supporting leg working condition and overload service condition.
(1) jib structure working level
According to the service rating of jib structureWith stress state rank, by its work
Partition of the level is 8 grades, i.e.,.Therefore, jib structure working level can be described as, right
The quantification manner answered is,WithFor the influence factor of working level.
(2) lifting capacity, cantilever crane active length, work range and working cycles number
Determine the loading spectrum in the failure Crane Jib Structure history military service stage in Inspection cycle under typically used as operating condition, including
Lifting capacity, cantilever crane active length, work rangeAnd working cycles number) i.e.:
。
(3) telescopic oil cylinder working method
The working method of its telescopic oil cylinder is determined according to cantilever crane active length, i.e. which for corresponding to arm joint telescopic oil cylinder bolt be in
Bolt point.WithFor autocrane, when its cantilever crane active lengthWhen, as shown in Table 1, telescopic oil cylinder is inserted
Pin is in each arm jointThe position of upper bolt point is。
Wherein, for cantilever craneFor, bolt point position minute is as shown in figure 5, remaining arm joint is similar with its.Based on this, it stretches
Cylinder efficient mode can be described as, whereinSubscript represent arm joint number,Bolt point is represented,
Corresponding quantification manner is。
(4) landing leg stretching situation
The difference of supporting leg overhang will lead to Crane Truck Chassis and be in heeling condition.It is horizontal when crane tilts lifting cargo
The effect of power can be such that the deflection angle of cargo increases, long-term so doing, it is possible to which jib structure overall collapse even complete machine is caused to be tumbled.
Landing leg stretching situation can be described as, whereinTheA supporting leg,
The Comment gathers that supporting leg overhang grade comment is constituted are { very well, good, generally, poor, very poor }, and corresponding numerical value is。
(5) overload service condition
Pass through lifting capacity, cantilever crane active length, work rangeAnd operations numberTo overload crane service condition
It is described, it may be assumed that
Based on above-mentioned analysis, the evaluation indice of example can be described as:
(3)
In formula,For the evaluation indice of example；It is in applying working conditionA evaluation index (), with
、、、WithIt is corresponding.
3) the potential failure mode prediction under case retrieval is improved
Improve the specific steps of case retrieval process are as follows:
(1) example preliminary search
If the number of source instance is in case librarySource instance collection may be expressed as:
In formula,It isA source instance, wherein。
Each source instance is made of five parts, including source instance label, applying working condition evaluation index, Property Name corresponding to evaluation index, attribute valueAnd attribute weight.Therefore, each
Example may be expressed as:
Object instance can indicate are as follows:
In formula,For object instance label；WhereinFor corresponding evaluation indexAttribute number.
In actual conditions, when being counted to the typically used as operating condition of autocrane jib structure, due to statistical time, the uncertainty of personnel
It is difficult the phenomenon that avoiding some attribute from failing to record.If attribute in former example or object instanceWhen error of omission, during example expression,
WithValue should be empty.
Attribute value in source instance and object instance is normalized, result that treated are as follows:
Source instance in case libraryWhen, determine its evaluation indexDeterminant attribute, i.e. the attribute of maximum weight,
Attribute value after normalization is, found in object instance withThe middle corresponding attribute of determinant attribute, normalization
Attribute value afterwards is, the value range of the attribute isIf meeting formula (9),For effective example.
In formula,For determinant attribute local similarity threshold value, industry specialists are according to its knowledge and empirically determined (are set as
0.5).
(2) quadratic search of example
When the typically used as operating condition evaluation index of jib structure isWhen, if the number of effective example is in temporal instance library, have
Effect example set may be expressed as:
In effective example and object instance, if effectively instance properties value is not that empty attribute number is, determining to have
Imitate exampleWith object instanceThe weight of same alike result during progress similarity calculation.
According to formula (11), the average value of effective example Yu object instance same alike result similarity is determined:
In formula,It is for jib structure applying working condition evaluation indexWhen, theA effective example and object instance are about category
PropertyLocal similarity, whereinIt is all effective examples and object instance about attributeOffice
The average value of portion's similarity.
It can be obtained by formula (12)WithStandard deviation on same alike result local similarity:
In formula,For measuring evaluation indexIn each attribute significance level, reflect each effective example in attributeThe otherness of upper value.It is bigger, declared attributeValue in various embodiments differs greatly, which can obvious area
Divide each example, should assign the attribute biggish weight at this time.
By formula (13), calculateWithThe weight corresponding with same alike result in similitude solution procedure:
To consider that attribute value lacks the influence to search result, by introducing attribute value deficiency factor, European to improve tradition
Distance calculating method, then improved Euclidean distance are as follows:
In formula,, whereinForWithIn the sum of weight when being failed to record without attribute value,ForIn have andThe sum of the weight of middle no attribute value error of omission,ForIn have andThe weight of middle no attribute value error of omission
The sum of, concrete condition is shown in Table 2.
WithSimilarity are as follows:
Set similarity threshold, evaluation indexFor and work asWhen, it is believed that this effective example is object instance
Similar case, if it exists when multiple similar cases, then choose the maximum example of similarity be best similar case。
When each evaluation index has best similar case, by typically used as operating condition evaluation indiceIn, with each element
Corresponding best similar case takes union ∪, forms best similar case collection
.Best similar case is concentrated, and failure mode corresponding with each element is the potential failure of autocrane jib structure to be assessed
Mode.
4) potential failure mode prediction result is corrected
As shown in fig. 6, the potential failure mode makeover process based on parameter finite element model and simulation calculation platform includes 2
Point: part 1-utilizes autocrane jib structure parameter finite element model and simulation calculation platform, obtains to be assessed
The characteristic value of potential failure mode at heavy-duty machine jib structure dangerous point, i.e., by inputting the original of Crane Jib Structure to be assessed
Characteristic parameter and typically used as operating condition and quantify its failure mode (according to the qualitative description of failure mode, determine danger position and
Dangerous point), the gib arm of crane to be assessed is established using the APDL programming language in C#, ANSYS in conjunction with failure mode interpretational criteria
The rough model (calculating data .RST comprising rough model file .DB and rough model) of frame structure, constructs jib structure crackle
Submodel, weld seam submodel and local stability submodel (file .DB corresponding with each submodel), and provide each submodel
Cut-boundary (submodel cut-boundary .NODE) reads in rough model data calculated result and extracts submodeling analysis boundary, from
And obtain submodel data boundary .CBDO, read in submodel data and data boundary and be inserted into submodel progress analytical calculation, from
The characteristic value (stress) of failure mode at dangerous point is read in calculated result；Part 2-potential failure mode amendment, i.e. basis
Failure mode characteristic value in each arm joint of the jib structure at danger position and dangerous point, if the spy at danger position and dangerous point
Characteristic value where value indicative is greater than it around arm joint otherwise should be by arm joint then without being modified to danger position and dangerous point
The upper maximum position of characteristic value is set as the danger position of the arm joint, and provides the dangerous point at danger position.
Step 3: the foundation of risk assessment index set
It is built according to autocrane jib structure feature in conjunction with autocrane jib structure potential failure mode prediction technique
Vertical autocrane jib structure Risk Evaluation Factors.As shown in fig. 7, jib structure Risk Evaluation Factors are divided into 4 grades, level-one
Index；Two-level index、With；The three-level of principal arm refers to
Mark、With the level Four index or auxiliary of principal arm and the three-level index of gooseneck boom frame(), wherein each test point in each arm joint is (i.e. dangerous
Point) it is (high from apparent size, it is wide, it is thick), notch cracks, deformation (verticality, angularity, local buckling degree),
Stress, weld seamTotally 5 aspects are assessed, and each section is established by principle from bottom to top.
Step 4: calculating evaluation index weight
Structure risk grade is divided into 5 grades by way of quantization by the influence according to each index to autocrane jib structure,
I.e. failure (F) higher (Y), high (L), general (Z), low (C), specific fuzzy evaluation quantification gradation are shown in Table 3.
In view of on-site test signal drift, the problems such as data distortion, using more people's detection datas as foundation, each detection is established
The fuzzy evaluating matrix of point:
In formula,a pkq For test pointpWhen, measure indexkBelong to gradeqNumber and total number of persons ratio.Wherein；。
The evaluation indice of test point in jib structure risk assessment are as follows:
。
Assessment experts are using autocrane failure conditions statistical result as theoretical foundation, in conjunction with knowledge and experience,
Indicate mutual between evaluation index with influence " high, high, general, low " fuzzy semantics variable and corresponding Triangular Fuzzy Number
Influence degree, wherein Triangular Fuzzy Number may be expressed as:, it is subordinate to
Membership fuction are as follows:
That establishes each test point evaluation index directly affects fuzzy relation matrix:
In formula,For test pointDirectly affect fuzzy relation matrix,For test pointEvaluation indexIt is rightInfluence relationship.
Normalization process is carried out to (17) formula, to obtain relatively direct influence fuzzy relation matrix:
In formula,For test pointRelatively direct influence fuzzy relation matrix.
(18) formula is split as 3 submatrixs according to matrix-split method, is respectively as follows:,,, corresponding combined influence matrix is determined by (19) respectively,,。
The combined influence matrix of test point are as follows:
In formula,Refer to test pointEvaluation indexIt is rightCombined influence degree, that is, directly affect and influence indirectly
Degree, if, indicate have an impact.
Define test pointThe disturbance degree matrix and degree of being affected matrix of evaluation index, so that it is determined that test point evaluation refers to
Target centrad and reason degree.
The disturbance degree matrix of evaluation index:
In formula,For test pointThe disturbance degree matrix of evaluation index,It isA evaluation index is to all fingers
The sum of target combined influence degree.
Degree of the being affected matrix of evaluation index:
In formula,For test pointDegree of the being affected matrix of evaluation index,It isA evaluation index by
The sum of combined influence degree of all indexs.
Therefore, test pointEvaluation indexCentradWith reason degreeIt is respectively as follows:
CentradIt is bigger, show indexIt is bigger with the relevance of remaining index.If, then it represents that indexTo it
Remaining index is affected, and is reason index；Otherwise it is result index, showsIt is affected by remaining index.
Determine test pointEvaluation criterion weight matrix are as follows:
Weight matrix is divided by matrix-split method
Three submatrixs find out corresponding synthetic evaluation matrix by formula (29) respectively。
To which test point can be obtainedSynthetic evaluation matrix and it is normalized:
In formula,。
Step 5: determining the risk of in-service autocrane jib structure
The synthetic evaluation matrix of primary arm part two-level index is calculated by formula (31):
In formula,For test point in principal arm three-level indexR 1Weight coefficient in arm joint No.1, subscript " 1,3,1 " difference table
Show arm joint No.1,3 grades of indexs and test pointR 1, remaining symbol (meaning, calculation method) is similar with its.
The synthetic evaluation matrix of first class index is calculated by formula (32) ~ formula (34):
In formula,For arm joint No. in two-level indexiWeight coefficient in principal arm, wherein。
In formula,For test point in two-level indexR p Weight coefficient in auxiliary, wherein。
In formula,For test point in two-level indexR p Weight coefficient in gooseneck boom frame, wherein。
Jib structure synthetic evaluation matrix is calculated by formula (35):
In formula,For the weight coefficient of first class index, wherein。
Sharpening processing is carried out to evaluation result according to parameter level method, obtains the risk of jib structure:
In formula,For the risk of jib structure.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention
Within mind and principle, any modification, equivalent replacement, improvement and so on be should all be included in the protection scope of the present invention.
Claims (6)
1. a kind of autocrane jib structure methods of risk assessment based on potential failure mode, it is characterised in that including as follows
Step:
(1) failure mode fuzzy database is built
According to autocrane " offline → use → regular inspection → failure " process, autocrane jib structure failure mode is established
Fuzzy database includes at least primitive character parameter subdata base, typically used as operating condition subdata base and failure mode subdata
Three, library subdata base, each subdata base are matched by identifying to crane model identification with detection serial number, and will be identical
Autocrane primitive character parameter, typically used as operating condition and the failure mode of model identification and detection serial number mark form one
Case history；
(2) prediction and amendment of potential failure mode
On the basis of step (1), autocrane jib structure failure mould under same type, different typically used as operating conditions is established
Formula case library；In-service autocrane jib structure to be assessed, which then passes through, collects typically used as operating condition, and as target
Example determines object instance corresponding with the evaluation index of typically used as operating condition and case library with case illation technology is improved
Similarity between middle source instance；When there is corresponding best similar case in each evaluation index of typically used as operating condition, group
At best similar case collection, the failure mode that best similar case concentrates each element is exported, and as potential failure mode；Work as allusion quotation
When each evaluation index of type applying working condition is no or part does not have corresponding best similar case, according to expertise to mistake
Effect mode is estimated, on this basis, flat by autocrane jib structure parameter finite element model and simulation calculation
Platform is modified the result of failure mode, and the result after forecast value revision is saved in failure mode as new case and is obscured
In database；
(3) foundation of risk assessment index set
On the basis of step 2, the distribution of dangerous point on dangerouse cross-section is corresponded to according to failure mode, establishes wind in conjunction with failure criteria
Dangerous evaluation index collection；
(4) evaluation index weight is calculated
Automobile crane is determined in conjunction with the testing result for the test point that non-destructive testing technology obtains with fuzzy improvement DEMATEL method
The weight of each risk assessment index of machine jib structure；
(5) risk of in-service autocrane jib structure is determined
Based on the weight coefficient of each test point of autocrane jib structure, the synthetic evaluation matrix of test point is established, arm is obtained
The synthetic evaluation matrix of frame structure carries out sharpening processing to evaluation result using parameter level method, finally obtains jib structure
Whole risk.
2. the autocrane jib structure methods of risk assessment based on potential failure mode as described in claim 1, feature
Be: in step (1), the typically used as operating condition includes the working level, loading spectrum, telescopic oil cylinder work side of jib structure
Formula, supporting leg working condition and overload service condition.
3. the autocrane jib structure methods of risk assessment based on potential failure mode as described in claim 1, feature
Be: in step (1), the primitive character parameter includes running parameter, operating speed and shell system structural parameters, the work
It include maximum rated lifting capacity, basic arm maximum hoisting moment, basic arm maximum lifting height, principal arm maximum raising as parameter
Degree, auxiliary maximum lifting height；When the operating speed includes single rope maximum speed, crane arm plays the width time, crane arm falls width
Between, crane arm stretch time, crane arm full reduced time, rotational speed entirely；The shell system structural parameters include material, arm support
The parameters of structural dimension of type, arm joint number, principal arm section form and each arm joint.
4. the autocrane jib structure methods of risk assessment based on potential failure mode as described in claim 1, feature
Be: in step (2), the improvement case illation technology is built by quadratic search strategy and improved Euclidean distance algorithm
The case retrieval prioritization scheme during in-service autocrane jib structure potential failure mode prediction is found, is determined typically used as
The best similar case of each evaluation index under operating condition.
5. the autocrane jib structure methods of risk assessment based on potential failure mode as described in claim 1, feature
Be: autocrane jib structure parameter finite element model and simulation calculation platform in step (2), be with
Microsoft.Net Framework is platform, using Visual studio2013 as developing instrument, using C# language to ANSYS
The encapsulation and calling for parameterizing file, develop under different applying working conditions autocrane jib structure parameter finite element model and
Simulation calculation platform, by inputting primitive character parameter, the typically used as operating condition of autocrane jib structure to be assessed, quantization
The evaluation mode of its failure mode, is modified potential failure mode.
6. the autocrane jib structure methods of risk assessment based on potential failure mode as described in claim 1, feature
Be: in step (4), the fuzzy improvement DEMATEL method, be by construct between risk assessment indexs at different levels it is direct,
Influence fuzzy relation matrix is connect, the disturbance degree matrix and degree of being affected matrix of evaluation indexes at different levels are derived, to obtain at different levels comment
Estimate the centrad and reason degree of index, the relevance between evaluation index and influence property is analyzed, according to correlation degree and influence degree
Distribute weight.
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| CN115983838A (en) * | 2023-03-21 | 2023-04-18 | 江苏苏港智能装备产业创新中心有限公司 | Method, device and equipment for evaluating health of steel wire rope of crane hoisting mechanism and storage medium |
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