CN112733379A - Portal crane metal structure risk assessment method - Google Patents
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Abstract
The invention provides a portal crane metal structure risk assessment method, which comprises the following steps: step 1, selecting factors such as strength, rigidity and cracks as evaluation indexes according to the characteristics of a metal structure of a gantry crane, and determining the weight of the evaluation indexes by adopting an extensive analytic hierarchy process; step 2, dividing the index risk level to obtain a cloud model of a risk level evaluation standard; step 3, calculating cloud parameters according to measured values of the evaluation indexes, and calculating upwards layer by combining with the weight of the bottom layer indexes to obtain a portal crane metal structure risk evaluation comprehensive cloud model; and 4, calculating comprehensive certainty degree through a normal cloud model, and determining the final risk grade according to the maximum membership degree principle. The risk assessment method provided by the invention comprehensively considers the characteristics of fuzziness and randomness of the assessment result, improves the reliability of risk assessment, and provides a high-efficiency visual new method for the uncertainty of the risk assessment.
Description
Technical Field
The invention belongs to the field of intelligent risk assessment, and particularly relates to a portal crane metal structure risk assessment method.
Background
The portal crane is used as common equipment for hoisting operation in ports, power plants and shipyards, plays an extremely important role in operation, and the metal structure of the portal crane is used as a main bearing part of the crane and is directly related to the safe operation and the service life of the crane. The basis of the portal crane metal structure risk assessment is structure condition diagnosis, namely, the physical conditions of the crane structure, such as integral or local deformation, plate thickness change, crack length, corrosion condition, structural strain time history and the like, are obtained through field actual measurement, and safety evaluation and residual life prediction are carried out on the measured structure from strength, rigidity and the like. Because the field test conditions are limited more, the obtained measurement data are limited, and a large amount of uncertainty exists in the influence factors such as the environment, the actual working condition and the like. Aiming at the uncertain risk factors, the risk level of the metal structure of the gantry crane is determined by an efficient, accurate and practical method, the safe operation of the crane is guaranteed, and the method becomes an extremely important research content in the field safety risk management.
At present, the hoisting machinery structure risk assessment method includes a fuzzy level comprehensive assessment method, a support vector machine, an assessment method based on a grey theory, a risk assessment method based on a combined weighting method, an assessment method based on entropy and accumulated prospect theory and the like. The methods play an important role in the aspects of crane structural damage and risk analysis, but the existing methods focus on considering the fuzziness of crane system risk factors, and lack the research on randomness and discreteness. In addition, many factors are considered when judging results, and the method is easily influenced by subjective uncertainty.
The cloud theory is used as a mathematical tool for describing the uncertainty relation conversion between object qualitative and quantitative, the problems of ambiguity and randomness existing in the evaluation process can be comprehensively considered, and the influence of random uncertainty on the evaluation result is obviously reduced. At present, the theory is widely applied to the fields of environmental quality assessment, power system assessment, shield tunnel damage assessment and the like, and has a good development prospect. Based on the method, the crane metal structure is used as a fuzzy system, risk factors influencing the safety performance of the structure are decomposed step by step, a portal crane metal structure risk comprehensive evaluation model based on the cloud theory is provided, and a new method is provided for the safety evaluation of the portal crane metal structure.
Disclosure of Invention
The invention aims to provide a method for evaluating the risk of a metal structure of a gantry crane, which at least solves the problems that in the prior art, the ambiguity of risk factors of a crane system is emphasized, the study on randomness and discreteness is lacked, and the evaluation result is influenced by numerous considered factors and is easily influenced by subjective uncertainty.
In order to achieve the above purpose, the invention provides the following technical scheme:
a portal crane metal structure risk assessment method preferably comprises the following steps:
and 4, calculating comprehensive certainty degree through a normal cloud model, and determining the final risk grade according to the maximum membership degree principle.
Preferably, the method for evaluating the risk of the metal structure of the gantry crane in step 1 further includes the following steps:
step 11, realizing a normal cloud model;
step 12, establishing a portal crane metal structure risk assessment index system;
and step 13, determining the weight of the evaluation index.
Preferably, the step 11 further includes the following steps:
step 111, generating 1 normal random number En' with En as an expected value and He as a standard deviation;
step 112, generating 1 normal random number x with Ex as an expected value and En' as a standard deviation, wherein x is a cloud droplet in a discourse space;
step 113, calculating y ═ exp [ - (x-Ex) from En' and x2/2(En′)2]Y is the certainty that x belongs to the qualitative concept T, and (x, y) is taken as a cloud droplet;
and step 114, repeating the steps 11 to 13 until N cloud droplets are generated, wherein N is a determined positive integer.
Preferably, the step 12 further includes the following steps:
step 121, evaluating the risk evaluation process of the whole crane metal structure according to three levels of single factors, subsystems and the whole crane metal structure according to the safety regulations of the port crane;
step 122, dividing the metal structure of the whole machine into a boom system, a gantry system and a propeller strut system;
and step 123, comprehensively evaluating the structural state of the whole machine by combining the evaluation results of the subsystems.
Preferably, in the method for evaluating the risk of the metal structure of the gantry crane, step 13 further includes the following steps:
step 131, constructing an extension judgment matrix
After the hierarchical structure is established, all indexes in the same level are compared pairwise to construct an extension interval number judgment matrix A [ a ]ij]n×n(ii) a A is a positive and inverse matrix, element
Is an extension number whose value is quantified by the 1-9 scale proposed by SATTY;
step 132, calculating a weight vector of the extension judgment matrix;
the extension decision matrix A is first expressed as a left-right matrix form, i.e.<A-,A+>Calculating the matrix A-,A+Maximum eigenvalue of
And obtaining the normalized feature direction corresponding to the maximum feature valueQuantity x-,x+. Then according to
And (3) calculating correction parameters k and m by adopting a root method:
if the obtained correction parameters k and m satisfy 0-1, it indicates that the extension judgment matrix satisfies the consistency condition, otherwise, the original judgment matrix needs to be corrected until the requirement is satisfied.
Let S be the weight vector of the extension decision matrix, i.e.
Step 133 determines single layer index weights using relative importance
Is provided with
(i,j＝1,2,…,nkI ≠ j) represents the weight of the ith index and the jth index in the t-th layer respectively, then
Likelihood degree matrix of
P＝[pij]n×n
Calculating to obtain single-layer index weight according to fuzzy complementary judgment matrix
Preferably, the step 2 further includes the following steps:
step 21, judging the risk state of the whole machine and the subsystem layer structure by adopting an expert scoring method according to the safety regulations of the metal structure of the gantry crane;
step 22, obtaining the evaluation grade division result of the safety state of the whole machine and subsystem layer structure;
step 23, converting all the evaluation results of the language evaluation set into corresponding cloud models;
and 24, grading each evaluation index of the single factor layer by combining the risk factors of the metal structure of the gantry crane and the expert experience, and calculating the characteristic parameters of the corresponding cloud model.
Preferably, the step 3 further includes the following steps:
step 31, evaluating the boom system, the gantry system, the propeller strut system and other subsystems to establish a portal crane metal structure risk evaluation index system;
step 32, comprehensively evaluating the structural state of the whole machine by combining the evaluation result of the subsystem;
step 33, considering the importance and difference degree of each influence factor in the whole evaluation system;
step 34, generating a large number of cloud droplets according to the evaluation index cloud parameters by using a forward cloud transformation algorithm, and substituting the cloud droplets into an evaluation standard cloud model to calculate the average certainty degree of the evaluation index corresponding to the evaluation grade;
step 35, calculating the comprehensive certainty of the metal structure risk level of the gantry crane according to the following formula by using the average certainty of the indexes and the corresponding weights
Where rhojIs the integrated certainty of the corresponding evaluation level j; w is aiIs the weight corresponding to the ith index; mu.sijIs the average certainty that the index i corresponds to the evaluation level j, mayIs obtained from the formula
In the formula xkTaking N as 3000 as index evaluation value, and N as cloud drop number;
step 36, according to the principle of maximum membership, determining the risk level K of the whole metal structure from the following
K＝max{ρ1,ρ2,…,ρn}。
Compared with the closest prior art, the technical scheme provided by the invention has the following excellent effects:
according to the risk assessment method provided by the invention, the comprehensive certainty degree is calculated through a normal cloud model, and the final risk grade is determined according to the maximum membership degree principle; the method can comprehensively consider the problems of ambiguity and randomness in the evaluation process, and obviously reduce the influence of random uncertainty on the evaluation result.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. Wherein:
FIG. 1 is a block diagram of a gantry crane metallic structure risk assessment indicator architecture according to an embodiment of the present invention;
fig. 2 is a cloud chart of the comprehensive risk assessment result of the gantry crane metal structure in embodiment 2 of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
In the description of the present invention, the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, which are for convenience of description of the present invention only and do not require that the present invention must be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. The terms "connected" and "connected" used herein should be interpreted broadly, and may include, for example, a fixed connection or a detachable connection; they may be directly connected or indirectly connected through intermediate members, and specific meanings of the above terms will be understood by those skilled in the art as appropriate.
Example 1
According to the specific embodiment of the invention, the cloud theory is proposed based on the traditional fuzzy set theory and probability statistics, the natural language is taken as an entry point to research uncertain artificial intelligence, and the uncertainty of quantity is grasped by a conceptual method, so that the cloud theory is more real and universal than mathematical expression.
And setting T as a qualitative concept on a certain deterministic numerical value domain U, mapping the qualitative concept T to a closed interval [0,1] by an operator to obtain a certainty factor mu, wherein the certainty factor is a random number which exists in the qualitative concept and has a stable tendency for a certain quantity of values x in the domain. The distribution of x over the domain of discourse U forms a definite cloud c (x), while x, μ constitutes a cloud droplet. The cloud is composed of a plurality of cloud droplets, each cloud droplet is a quantitative embodiment of a qualitative concept, and the cloud droplets also represent an uncertainty mapping relation between the qualitative concept and a quantitative value. Cloud theory represents the digital characteristics of the cloud by introducing the expectation Ex, entropy En and super-entropy He. Expectation Ex refers to the mathematical expectation of cloud droplets, the point in the domain space that is most representative of the qualitative concept, and the center of gravity of the entire cloud droplet population. The entropy En reflects the randomness and ambiguity of qualitative concepts, and is used to represent the dispersion degree of cloud droplets and the range of values of cloud droplets acceptable by the concepts in the universe of discourse. The super-entropy He is a measure of uncertainty of entropy, reflects the degree of dispersion and thickness of cloud droplets, and is determined by the ambiguity and randomness of entropy.
According to the specific embodiment of the invention, at present, triangular clouds, rectangular clouds, trapezoidal clouds, normal clouds and the like have been developed from the distribution form of the cloud theory, wherein the normal cloud model is widely applied due to the unique mathematical characteristics and universality thereof, therefore, the normal cloud distribution model is adopted as the basic cloud model of the evaluation index and the evaluation standard, and the specific implementation steps are as follows:
(1) generating a normal random number En' with 1 En as an expected value and He as a standard deviation;
(2) generating 1 normal random number x with Ex as an expected value and En' as a standard deviation, wherein x is a cloud droplet in a universe of discourse;
(3) calculating y ═ exp [ - (x-Ex) from En' and x2/2(En′)2]Y is the certainty that x belongs to the qualitative concept T, and (x, y) is taken as a cloud droplet;
(4) repeating the steps (1) to (3) until N cloud drops are generated.
For bearing bilateral constraint [ Bmin,Bmax]The three characteristic parameters of the normal cloud obtained by using the conversion relation between the interval number and the cloud model are as follows:
Ex＝(Bmax+Bmin)/2 (1)
En＝(Bmax-Bmin)/6 (2)
He＝c (3)
wherein c is a constant and represents the fuzzy degree of qualitative concepts, and can be determined by combining specific problems, and the c is 0.01. When the index has only one-sided constraint BmaxOr BminWhen the method is used, default parameters or default expected values of the method are determined, characteristic parameters of the method are calculated according to the formulas (1) to (3), and the characteristic parameters are described by using a semi-cloud model.
According to the embodiment of the invention, as shown in fig. 1, the primary task of the portal crane metal structure risk assessment is to establish a risk index assessment system, and since the considered risk factors are multifaceted, a hierarchical comprehensive evaluation system is required to be established. According to the safety regulations of the port crane, the risk assessment process of the whole crane metal structure is carried out according to three levels of single factors, subsystems and the whole crane metal structure. The single-factor evaluation layer is an initial layer for risk evaluation of the metal structure of the crane, and strength, rigidity, cracks, deformation and corrosion are selected as evaluation indexes in the layer. As the metal structure part of the whole machine is quite complex and has a plurality of influence factors, the whole machine is divided into a boom system, a gantry system, a propeller strut system and other subsystems for evaluation. The whole machine metal structure system is a final identification and evaluation object and also is the topmost layer of the multi-stage comprehensive evaluation system, and the structural state of the whole machine can be comprehensively evaluated by combining the evaluation results of the subsystems. Therefore, a portal crane metal structure risk assessment index system is established according to the scientific, comprehensive, principal component and operability principles.
According to the specific embodiment of the invention, in risk assessment, aiming at the problems that layering and quantification of various parameter indexes are difficult to highlight in a complex structure of a gantry crane, uncertainty caused by judgment of experts is considered, the invention adopts an extensible analytic hierarchy process to calculate and obtain the weight of each layer of index. The specific calculation steps are as follows:
(1) constructing an extension decision matrix
After the hierarchical structure is established, all indexes in the same level are compared pairwise to construct an extension interval number judgment matrix A ═ aij]n×n. A is a positive and inverse matrix, element
Is an extension number whose value is quantified by the 1-9 scale proposed by SATTY.
(2) Calculating weight vectors of extension judgment matrix
The extension decision matrix A is first expressed as a left-right matrix form, i.e.<A-,A+>Calculating the matrix A-,A+Maximum eigenvalue of
And obtaining the normalized eigenvector x corresponding to the maximum eigenvalue-,x+. Then according to
And (3) calculating correction parameters k and m by adopting a root method:
if the obtained correction parameters k and m satisfy 0-1, it indicates that the extension judgment matrix satisfies the consistency condition, otherwise, the original judgment matrix needs to be corrected until the requirement is satisfied.
Let S be the weight vector of the extension decision matrix, i.e.
(3) Determining single layer index weights using relative importance
Is provided with
(i,j＝1,2,…,nkI ≠ j) represents the weight of the ith index and the jth index in the t-th layer respectively, then
Likelihood degree matrix of
P＝[pij]n×n
Calculating to obtain single-layer index weight according to fuzzy complementary judgment matrix
According to the embodiment of the invention, the risk state of the whole machine and the subsystem layer structure is judged by adopting an expert scoring method according to the safety regulation of the metal structure of the gantry crane. The evaluation grade division results of the safety states of the whole machine and subsystem layer structures are shown in table 1, and all the evaluation results of the language evaluation set are converted into corresponding cloud models by using the formula. And grading each evaluation index of the single factor layer by combining the risk factors of the metal structure of the gantry crane and the expert experience, and calculating the characteristic parameters of the corresponding cloud model. Tables 2 to 6 list all the evaluation index grades of the single factor layer.
Table 1 cloud model corresponding to evaluation standard
Table 2 evaluation index strength C1 corresponding cloud model
Table 3 evaluation index rigidity C2 corresponding cloud model
Table 4 evaluation index crack C3 corresponding cloud model
Table 5 evaluation index deformation C4 corresponding cloud model
Table 6 evaluation index cloud model corresponding to rust C5
According to the specific embodiment of the invention, the metal structure state of the whole gantry crane is used as a target layer of risk assessment, and the influence factors are numerous and need to be assessed level by level. The evaluation of the whole machine metal structure state is converted into a comprehensive cloud model which is formed by cloud aggregation converted by various influencing factors. In addition, since the importance of each influencing factor varies in the whole evaluation system, it is also necessary to consider the relative importance degree between the influencing factors, i.e., the weight of each influencing factor. The comprehensive judgment cloud model in consideration of the weight may be represented by the following formula:
in the formula Exi,Eni,HeiRespectively representing the cloud model corresponding to each evaluation index; w is aiIs the weight of the cloud model corresponding to the ith index, and n is the number of indexes for the same level.
A cloud model graph of the evaluation index and the evaluation standard is generated through a forward cloud transformation algorithm, a final evaluation grade is determined according to the relative position of the evaluation index and the evaluation standard, however, the evaluation grade determined by subjective judgment of people is easy to deviate, and the judgment is difficult when cloud droplets are poor in agglomeration. According to the cloud evaluation method, a forward cloud transformation algorithm is utilized, a large number of cloud droplets are generated according to the cloud parameters of the evaluation indexes, and the cloud droplets are substituted into the evaluation standard cloud model to calculate the average certainty degree of the evaluation indexes corresponding to the evaluation grades. And then, the average certainty degree of the indexes and the corresponding weight are utilized to obtain the comprehensive certainty degree of the metal structure risk level of the gantry crane according to the following formula
Where rhojIs the integrated certainty of the corresponding evaluation level j; w is aiIs the weight corresponding to the ith index; mu.sijIs the average degree of certainty that the index i corresponds to the evaluation level j, and can be obtained by the following formula
In the formula xkFor index evaluation, N is the cloud drop number, where N is 3000.
According to the principle of maximum membership degree, determining the risk level K of the metal structure of the whole machine by the formula (12)
K＝max{ρ1,ρ2,…,ρn}
Example 2
According to the specific embodiment of the invention, a 150t shipbuilding portal crane is taken as a research object, the portal crane is a single-boom portal crane, is put into operation for years, is used for shipbuilding enterprises on a certain island in a navicular mountain, and is mainly used for hoisting ship body sections and structural members, and from the hoisting statistical result, 80t-120t structural members are abundant, the working level is low, but the amplitude of fluctuation and rotation work are carried normally. The metal structure material is Q345, and the appearance inspection has no serious damage condition
According to the characteristics and the working environment of the metal structure of the shipbuilding portal crane, indexes capable of being analyzed quantitatively, such as strength, rigidity, cracks, deformation, corrosion and the like, are selected as evaluation indexes, but because the dimensions of the evaluation indexes are different, the evaluation indexes cannot be directly used for comprehensive evaluation, the evaluation indexes need to be subjected to non-quantitative dimensionalization treatment, and meanwhile, the value intervals of the indexes are unified.
The method for processing the strength and rigidity indexes comprises the following steps
In the formula XiRepresents a test value of the corresponding index, and 0<Xi≤[Xi]；[Xi]Indicating an allowable value of the corresponding index; m isiIndicating the evaluation value of the corresponding index after normalization processing.
The crack index is treated by
In the formula TiRepresenting the inspection period of the crack index; 10 denotes the critical value for crack repair [19 ]]When the inspection period is less than 10 days, the cracks need to be repaired. t is tiAnd the evaluation value of the crack index after normalization processing is shown.
For the deformation index, the pressed plate with local waviness is mainly considered, and the treatment method is that
In the formula PuRepresents the ultimate load value of the panel; p is a radical ofyRepresenting a nominal load value of the panel; q. q.siAnd the evaluation value of the deformation index of the plate after normalization processing is represented.
The treatment method for the corrosion index comprises the following steps
Zeta in the formulaiExpressing the ratio of the corrosion thickness of the plate to the original thickness of the plate; 0.1 represents a corrosion index safety threshold value, and when the ratio of the corrosion thickness of the plate to the original thickness of the plate exceeds 0.1, the plate is regarded as unqualified; u. ofiAnd showing the evaluation value of the normalized plate corrosion index.
The state values of the strength, rigidity, crack, deformation and corrosion indexes are obtained through field tests, and the calculation of corresponding evaluation values and cloud parameters is completed, and the results are shown in table 7.
TABLE 7 cloud model for evaluation indexes of single factor layer
And determining the weight of the index layer by adopting an extensive analytic hierarchy process. The extension judgment matrix is constructed through expert group discussion, the weight vector is calculated, and finally the index weight is determined by utilizing the relative importance.
According to the embodiment of the invention, as shown in fig. 2, after the weights of the evaluation indexes are calculated and obtained, cloud models of a boom system, a gantry system, a propeller strut system, a turntable rotating column system and other structural systems in a subsystem evaluation layer can be obtained according to the formula (10), and then a cloud model for the risk evaluation of the whole metal structure is obtained on the basis. Table 8 lists the indexes of the subsystem evaluation layer and the cloud model corresponding to the overall comprehensive evaluation result.
TABLE 8 subsystem layer evaluation index and complete machine comprehensive evaluation result cloud model
According to the specific embodiment of the invention, as can be seen from fig. 2, the risk level of the whole metal structure of the portal crane is between v and vi, and most of the risk level is within the range of vi. The degree of certainty of the comprehensive evaluation result to each evaluation standard is calculated according to the formula (11): rho1＝0,ρ2＝0,ρ3＝0,ρ4＝0,ρ5＝0.073,ρ6＝0.507,ρ7＝0.002,ρ 80. According to the maximum membership principle, the comprehensive evaluation result shown in the formula (12) has the maximum certainty degree on the evaluation grade VI, and indicates that the whole machine metal structure is in a fault operation state, and the damaged parts need to be repaired after periodical important inspection.
In conclusion; the invention provides a comprehensive risk evaluation model of a gantry crane metal structure based on a cloud theory, which well considers the characteristics of fuzziness and randomness of crane risk evaluation.
The invention adopts the language evaluation value to reflect the importance degree of the index, not only reflects the fuzziness and uncertainty of human thinking on the evaluation of things, but also eliminates the randomness problem of the subjective and objective weighting method. Indexes in the crane metal structure risk assessment are qualitative concepts, the assessment indexes are described by a language assessment set, and then the language assessment set is converted into a normal cloud chart by utilizing a cloud theory, so that the fuzziness of the qualitative indexes and the objectivity of the assessment results are well reserved.
According to the portal crane metal structure risk assessment index system established by the invention, risk assessment results of different parts of the crane metal structure can be obtained, and dynamic guidance is provided for on-site operation risk control. The risk assessment result is a cloud model consisting of 3 digital features of expected value, entropy and super entropy. The cloud model comprehensively considers the characteristics of fuzziness and randomness of an evaluation result, improves the reliability of risk evaluation, and provides a high-efficiency visual new method for the uncertainty of the risk evaluation of the metal structure of the gantry crane.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. A portal crane metal structure risk assessment method is characterized by comprising the following steps:
step 1, selecting factors such as strength, rigidity and cracks as evaluation indexes according to the characteristics of a metal structure of a gantry crane, and determining the weight of the evaluation indexes by adopting an extensive analytic hierarchy process;
step 2, dividing the index risk level to obtain a cloud model of a risk level evaluation standard;
step 3, calculating cloud parameters according to measured values of the evaluation indexes, and calculating upwards layer by combining with the weight of the bottom layer indexes to obtain a portal crane metal structure risk evaluation comprehensive cloud model;
and 4, calculating comprehensive certainty degree through a normal cloud model, and determining the final risk grade according to the maximum membership degree principle.
2. The method for evaluating the risk of the metal structure of the gantry crane according to claim 1, wherein the step 1 specifically comprises the following steps:
step 11, realizing a normal cloud model;
step 12, establishing a portal crane metal structure risk assessment index system;
and step 13, determining the weight of the evaluation index.
3. The method for assessing the risk of the metal structure of the gantry crane according to claim 2, wherein the step 11 further comprises the following steps:
step 111, generating 1 normal random number En' with En as an expected value and He as a standard deviation;
step 112, generating 1 normal random number x with Ex as an expected value and En' as a standard deviation, wherein x is a cloud droplet in a discourse space;
step 113, calculate y ═ exp [ - (x-Ex) from En' and x2/2(En′)2]Y is the certainty that x belongs to the qualitative concept T, and (x, y) is taken as a cloud droplet;
and step 114, repeating the steps 11 to 13 until N cloud droplets are generated, wherein N is a determined positive integer.
4. The method for evaluating the risk of the metal structure of the gantry crane according to claim 2, wherein the step 12 further comprises the following steps:
step 121, evaluating the risk evaluation process of the whole crane metal structure according to three levels of single factors, subsystems and the whole crane metal structure according to the safety regulations of the port crane;
step 122, dividing the metal structure of the whole machine into a boom system, a gantry system and a propeller strut system;
and step 123, comprehensively evaluating the structural state of the whole machine by combining the evaluation results of the subsystems.
5. The method for assessing the risk of the metal structure of the gantry crane according to claim 2, wherein the step 13 further comprises the following steps:
step 131, constructing an extension judgment matrix;
after the hierarchical structure is established, all indexes in the same level are compared pairwise to construct an extension interval number judgment matrix A [ a ]ij]n×n(ii) a A is a positive and inverse matrix, element
Is an extension number whose value is quantified by the 1-9 scale proposed by SATTY;
step 132, calculating a weight vector of the extension judgment matrix;
the extension decision matrix A is first expressed as a left-right matrix form, i.e.<A-,A+>Calculating the matrix A-,A+Maximum eigenvalue of
And obtaining the normalized eigenvector x corresponding to the maximum eigenvalue-,x+(ii) a Then according to
And (3) calculating correction parameters k and m by adopting a root method:
if the obtained correction parameters k and m satisfy 0-1, it indicates that the extension judgment matrix satisfies the consistency condition, otherwise, the original judgment matrix needs to be corrected until the requirement is satisfied;
let S be the weight vector of the extension decision matrix, i.e.
S＝(S1,S2,…,Snk)＝<kx-,mx+>
Step 133, determining the weight of the single-layer index by using the relative importance;
is provided with
Respectively represent the weight of the ith index and the jth index in the t layer, then
The probability degree matrix of (c):
P＝[pij]n×n
calculating to obtain single-layer index weight according to the fuzzy complementary judgment matrix;
6. the method for evaluating the risk of the metal structure of the gantry crane according to claim 1, wherein the step 2 further comprises the following steps:
step 21, judging the risk state of the whole machine and the subsystem layer structure by adopting an expert scoring method according to the safety regulations of the metal structure of the gantry crane;
step 22, obtaining the evaluation grade division result of the safety state of the whole machine and subsystem layer structure;
step 23, converting all the evaluation results of the language evaluation set into corresponding cloud models;
and 24, grading each evaluation index of the single factor layer by combining the risk factors of the metal structure of the gantry crane and the expert experience, and calculating the characteristic parameters of the corresponding cloud model.
7. The method for evaluating the risk of the metal structure of the gantry crane according to claim 1, wherein the step 3 further comprises the following steps:
step 31, evaluating the boom system, the gantry system, the propeller strut system and other subsystems to establish a portal crane metal structure risk evaluation index system;
step 32, comprehensively evaluating the structural state of the whole machine by combining the evaluation result of the subsystem;
step 33, considering the importance and difference degree of each influence factor in the whole evaluation system;
step 34, generating a large number of cloud droplets according to the evaluation index cloud parameters by using a forward cloud transformation algorithm, and substituting the cloud droplets into an evaluation standard cloud model to calculate the average certainty degree of the evaluation index corresponding to the evaluation grade;
step 35, calculating the comprehensive certainty of the metal structure risk level of the gantry crane according to the following formula by using the average certainty of the indexes and the corresponding weight;
where rhojIs the integrated certainty of the corresponding evaluation level j; w is aiIs the weight corresponding to the ith index; mu.sijIs the average degree of certainty that the index i corresponds to the evaluation level j, and can be obtained by the following formula
In the formula xkTaking N as 3000 as index evaluation value, and N as cloud drop number;
step 36, according to the maximum membership rule K ═ max { ρ ═1,ρ2,…,ρnAnd determining the risk grade K of the metal structure of the whole machine.
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| WO2017008180A1 (en) * | 2015-07-16 | 2017-01-19 | 广东产品质量监督检验研究院 | Photovoltaic module failure risk determination method |
| CN111882238A (en) * | 2020-08-04 | 2020-11-03 | 上海海事大学 | Gantry crane structure health assessment method based on cloud model and EAHP |
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| WO2017008180A1 (en) * | 2015-07-16 | 2017-01-19 | 广东产品质量监督检验研究院 | Photovoltaic module failure risk determination method |
| CN111882238A (en) * | 2020-08-04 | 2020-11-03 | 上海海事大学 | Gantry crane structure health assessment method based on cloud model and EAHP |
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