Crane multi-working-condition safety prediction method based on non-probability reliability and application thereof

Abstract

The invention discloses a crane multi-working-condition safety prediction method based on non-probability reliability and application thereof. The method comprises the following steps: S01, acquiring a characteristic parameter interval of a bridge crane target structure; s02, based on the characteristic parameter interval, selecting a design point from the characteristic parameter interval; s03, establishing a limit state function; s04, performing partial derivative solving on the limit state function at the design point, and calculating a gradient norm value after normalization; s05, calculating a limit state function value at the design point, and then solving a non-probability reliability index at the design point in combination with a gradient norm value; and S06, comparing the calculated non-probability reliability index with a preset safety threshold value, and judging the working condition safety of the crane according to a comparison result. According to the scheme, the strong dependence of traditional probability reliability on statistical distribution can be overcome, the crane structure reliability threshold value can be rapidly and accurately evaluated under the multiple working conditions of different lifting capacities, dynamic load coefficients and the like, and clear safety early warning and regulation and control suggestions are provided for field operation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of crane safety technology and crane operation management, and particularly relates to a crane multi-condition safety prediction method based on non-probabilistic reliability and application thereof. BACKGROUND

[0002] With the rapid development of modern manufacturing and logistics industry, the bridge crane has become the key equipment for large-tonnage material handling, heavy cargo transportation and heavy load operation. The existing bridge crane has various structural forms, and the cross-section of the main beam in the middle of the span is prone to produce large bending moment and stress concentration when bearing lifting load and dynamic load. In long-term operation, yield, fatigue or damage accidents may occur, which seriously threatens the safety of the equipment and the production efficiency. Therefore, improving the structural safety of the crane and realizing reliable operation prediction under multiple conditions have become the focus of the engineering and scientific research fields.

[0003] The existing crane reliability evaluation method is mostly based on the probabilistic reliability theory, which needs to make statistical distribution assumptions on material performance, load action and environmental variables and a large number of Monte Carlo simulations. Although this method can accurately reflect the overall failure probability of the system, it has the following shortcomings:

[0004] (1) Strong dependence on distribution assumption: In actual engineering, the distribution of equipment material performance and load condition often lacks sufficient samples or does not meet the common distribution assumption, making it difficult to obtain a reliable statistical model;

[0005] (2) High calculation cost: Monte Carlo simulation needs to perform a large number of random sampling and finite element analysis, which is time-consuming and not conducive to rapid evaluation and real-time warning in engineering field;

[0006] (3) Poor scalability for multiple conditions: For different load conditions, such as dynamic load coefficient under different lifting weights and braking conditions, it is necessary to repeatedly construct a probabilistic model and re-simulate, which is complex and not convenient for online monitoring.

[0007] In recent years, as a reliability analysis method without distribution assumption, the non-probabilistic reliability theory (NPR) has been applied in the fields of structural safety evaluation and pressure vessel integrity analysis. By constructing the interval of uncertain parameters and defining the limit state function, the reliability index is calculated by using the obtained design point and normalized gradient, which greatly reduces the dependence on statistical information and simplifies the calculation process. However, the existing NPR researches mostly focus on the reliability evaluation of single condition or local substructure, and there is no systematic prediction method for multiple load-multiple dynamic load coefficient conditions. There is also a lack of complete solution combining the range of engineering field parameters, quickly generating safety contour maps and providing control suggestions. SUMMARY

[0008] Therefore, the present application aims to provide a crane multi-condition safety prediction method based on non-probabilistic reliability, which can overcome the strong dependence of traditional probabilistic reliability on statistical distribution, and quickly and accurately evaluate the crane structure reliability threshold under different hoisting weights, dynamic load coefficients and other multi-condition conditions, and provide clear safety warning and control suggestions for field operation.

[0009] In order to achieve the above technical purpose, the technical scheme adopted by the present application is:

[0010] A crane multi-condition safety prediction method based on non-probabilistic reliability, comprising:

[0011] S01, obtaining the characteristic parameter interval of the target structure of the bridge crane;

[0012] S02, selecting a design point based on the characteristic parameter interval;

[0013] S03, establishing a limit state function according to the load capacity and span L of the bridge crane;

[0014] S04, performing partial derivative on the limit state function at the design point, and calculating the gradient norm value after normalization;

[0015] S05, calculating the limit state function value at the design point based on the limit state function, and then combining the gradient norm value to solve the non-probabilistic reliability index at the design point;

[0016] S06, comparing the calculated non-probabilistic reliability index with the preset safety threshold, and determining the working condition safety of the crane according to the comparison result.

[0017] As a possible implementation, further in the present scheme S01, the target structure is the metal main beam of the bridge crane, which is the bending and shearing region of the bridge crane when bearing the hoisting load.

[0018] Among them, the characteristic parameters include material yield strength interval, cross-section plastic modulus interval, hoisting weight interval and dynamic load coefficient interval; wherein the material yield strength interval is

[0019] f y , The lower limit and upper limit of the material yield strength are respectively

[0020] Z, The lower limit and upper limit of the cross-section plastic modulus are respectively

[0021] W, respectively are lower limit, upper limit of dead load; dynamic load coefficient interval is

[0022] φ respectively are lower limit, upper limit of dynamic load coefficient.

[0023] As a possible implementation, the further scheme S01 further comprises: calculating half amplitude of each characteristic parameter according to characteristic parameter interval, the half amplitude corresponding to parameter interval of material yield strength, cross-section plastic modulus, dead load and dynamic load coefficient is respectively represented as δf y , δZ, δW, δφ.

[0024] As a better implementation selection of determining design point, preferably, in the scheme S02, the design point is the midspan of the metal girder of the bridge crane, and the design point vector is represented as X0=(f y0 , Z0, W0, φ0), which is the midpoint of the characteristic parameter interval of the target structure, and the formula is represented as follows:

[0025]

[0026] wherein, X0 is the characteristic parameter of the design point, f y0 , Z0, W0, φ0 are respectively the material yield strength, cross-section plastic modulus, dead load and dynamic load coefficient corresponding to the design point; f y respectively are lower limit, upper limit of material yield strength; Z respectively are lower limit, upper limit of cross-section plastic modulus; W respectively are lower limit, upper limit of dead load; φ respectively are lower limit, upper limit of dynamic load coefficient.

[0027] As another implementation selection of determining design point, preferably, in the scheme S02, the design point is a non-midpoint combination in the characteristic parameter interval of the metal girder of the bridge crane; the design point is determined by minimizing the normalized distance, iterative linearization by gradient projection method, vertex enumeration method or sampling method.

[0028] As a better implementation selection, preferably, the scheme S03 comprises:

[0029] S031, a function model of the dead load of the bridge crane and the static load bending moment and the dynamic load bending moment of the metal girder is established;

[0030] When the bridge crane is lifting, the static load bending moment formula is defined as follows: ​​​​​

[0031]

[0032] wherein M stat is the static bending moment, W and L are the lifting capacity and span of the bridge crane respectively;

[0033] In combination with the dynamic load coefficient, the dynamic bending moment of the bridge crane during lifting is defined by the following formula:

[0034]

[0035] M d is the dynamic bending moment, and φ is the dynamic load coefficient;

[0036] In combination with the yield strength of the material of the metal girder and the plastic modulus of the cross section, the bending resistance of the metal girder is defined by the following formula:

[0037] R = f y × Z

[0038] wherein R is the bending resistance, f y is the yield strength of the material of the metal girder, and Z is the plastic modulus of the cross section of the metal girder;

[0039] S032、Based on the bending resistance and the dynamic bending moment, the limit state function about the metal girder is established, and the definition is as follows:

[0040]

[0041] wherein f y is the yield strength of the material of the metal girder, Z is the plastic modulus of the cross section of the metal girder, W and L are the lifting capacity and span of the bridge crane respectively, and φ is the dynamic load coefficient;

[0042] When the solution value of the limit state function is equal to 0, it is the limit state, less than 0, then the equipment fails, and greater than or equal to 0, it is the working condition safety.

[0043] As a relatively optimal implementation option, preferably, the present scheme S04 comprises:

[0044] S041、At the design point, the partial derivative of the limit state function G is taken, and the partial derivative is defined by the following formula:

[0045]

[0046] wherein f y is the yield strength of the material of the metal girder, Z is the plastic modulus of the cross section of the metal girder, W and L are the lifting capacity and span of the bridge crane respectively, and φ is the dynamic load coefficient;

[0047] S041、the partial derivative is multiplied by the half amplitude of the corresponding material yield strength, the section plastic modulus, the lifting weight and the dynamic load coefficient, and then the two norm combinations, which are defined as follows:

[0048]

[0049] wherein, is the gradient norm value, f y0 , Z0, W0, φ0 are the material yield strength, the section plastic modulus, the lifting weight and the dynamic load coefficient of the design point at the midspan, and L is the span; δf y , δZ, δW, δφ are the half amplitude of the parameter interval of the material yield strength, the section plastic modulus, the lifting weight and the dynamic load coefficient of the metal girder.

[0050] As a preferred implementation option, preferably, the scheme S05 comprises:

[0051] Based on the limit state function, the parameters of the metal girder at the midspan are substituted into the design point to calculate the limit state value at the design point, which is represented as follows:

[0052]

[0053] wherein, G0 is the limit state function value at the design point, f y0 , Z0, W0, φ0 are the material yield strength, the section plastic modulus, the lifting weight and the dynamic load coefficient of the design point, and L is the span;

[0054] The formula of the non-probabilistic reliability index is defined as follows:

[0055]

[0056] wherein, β NPR is the non-probabilistic reliability index, G0 is the limit state function value at the design point, is the gradient norm value, which represents the minimum normalized distance of the metal girder from the failure surface in the uncertainty space.

[0057] As a preferred implementation option, preferably, the scheme S06 comprises:

[0058] The calculated non-probabilistic reliability index β NPR is compared with the preset safety threshold β 阈值 , and according to the comparison result, the working condition safety of the crane is determined, and the specific comparison logic is:

[0059] When β NPR ≥ β 阈值 , it is determined that the working condition is safe, and when β NPR < β 阈值 , it is determined that the working condition is dangerous.

[0060] Based on the above, the present scheme also proposes a kind of bridge crane multi-working condition management method, it includes: in response to the power-on signal of bridge crane, the crane multi-working condition safety prediction method based on non-probability reliability described above is executed.

[0061] As a kind of more optimal implementation selection, preferably, the present scheme also includes:

[0062] The working condition safety result of bridge crane is obtained S06 determination, when it points to dangerous working condition, by reducing the upper limit of lifting weight limit of bridge crane and / or reducing the upper limit of dynamic load coefficient, to reduce the working risk of bridge crane;

[0063] When the working condition safety result of bridge crane is determined S06 and points to dangerous working condition, the formula for reducing the upper limit of lifting weight limit of bridge crane is as follows:

[0064]

[0065] Wherein, W sf It is the adjusted allowable lifting weight limit, f y0 ,Z0 It is the yield strength of design point material, section plastic modulus, L is span, β 阈值 It is the preset safety threshold of non-probability reliability index, It is the gradient norm value, φ k It is the dynamic load coefficient of current working condition;

[0066] The formula for reducing the upper limit of dynamic load coefficient is as follows:

[0067]

[0068] Wherein, φ sf It is the adjusted dynamic load coefficient limit upper limit, f y0 ,Z0 It is the yield strength of design point material, section plastic modulus, L is span, β 阈值 It is the preset safety threshold of non-probability reliability index, It is the gradient norm value, w j It is the load of current working condition.

[0069] Based on the above, the present scheme also proposes a kind of bridge crane multi-working condition management system, it includes:

[0070] Data acquisition module, for responding to the work start signal of bridge crane, the working parameters of bridge crane are monitored in real time;

[0071] A working condition evaluation module loaded with the multi-working condition working management method of the overhead crane and used for evaluating the real-time working condition of the overhead crane to generate a working condition safety evaluation result;

[0072] A working intervention module used for obtaining the working condition safety evaluation result of the overhead crane and reducing the working risk of the overhead crane by reducing the upper limit of the lifting capacity limit and / or reducing the upper limit of the dynamic load coefficient according to preset conditions.

[0073] Compared with the prior art, the present application has the beneficial effects that the present application ingeniously establishes a crane multi-working condition safety prediction model based on non-probabilistic reliability, which can overcome the strong dependence of traditional probabilistic reliability on statistical distribution, quickly and accurately evaluate the crane structure reliability threshold under different multi-working condition conditions such as different lifting capacities and dynamic load coefficients, and provide clear safety warning and control suggestions for field operation, has wide adaptability to uncertain parameter intervals, comprehensive coverage of multi-working condition load-dynamic load coefficient combinations, and low calculation cost engineering implementability, to meet the actual needs of modern crane online monitoring and intelligent operation and maintenance. BRIEF DESCRIPTION OF DRAWINGS

[0074] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0075] Figure 1 is a brief implementation flowchart of the prediction method of the present application;

[0076] Figure 2 is a unit module connection diagram of the multi-working condition working management system of the overhead crane of the present application. DETAILED DESCRIPTION

[0077] The present application will be further described in detail below in combination with the drawings and embodiments. It is particularly pointed out that the following embodiments are only used to illustrate the present application, but do not limit the scope of the present application. Similarly, the following embodiments are only some embodiments of the present application, not all embodiments, and all other embodiments obtained by those skilled in the art without creative labor are within the scope of the present application.

[0078] As shown in Figure 1 , the present embodiment is a crane multi-working condition safety prediction method based on non-probabilistic reliability, which comprises:

[0079] S01, obtain a characteristic parameter interval of a target structure of a bridge crane;

[0080] S02, select a design point from the characteristic parameter interval based on the characteristic parameter interval;

[0081] S03, establish a limit state function according to a load capacity and a span L of the bridge crane;

[0082] S04, perform partial derivation on the limit state function at the design point, normalize the limit state function, and calculate a gradient norm value;

[0083] S05, calculate a limit state function value at the design point based on the limit state function, and then solve a non-probabilistic reliability index at the design point in combination with the gradient norm value;

[0084] S06, compare the calculated non-probabilistic reliability index with a preset safety threshold, and determine the working condition safety of the crane according to a comparison result.

[0085] Specifically, in the scheme S01, the target structure is a metal main beam of the bridge crane, which is a bending and shearing region of the bridge crane when the bridge crane bears a lifting load.

[0086] The characteristic parameters include a material yield strength interval, a cross-section plastic modulus interval, a lifting weight interval, and a dynamic load coefficient interval.

[0087] f y , The lower limit and the upper limit of the material yield strength are f

[0088] Z , The lower limit and the upper limit of the cross-section plastic modulus are Z

[0089] W , The lower limit and the upper limit of the lifting weight are W

[0090] φ, The lower limit and the upper limit of the dynamic load coefficient are φ. On this basis, as a possible implementation manner, the scheme S01 further includes: calculating half amplitudes of the characteristic parameters according to the characteristic parameter interval, the half amplitudes of the material yield strength, the cross-section plastic modulus, the lifting weight, and the dynamic load coefficient corresponding to the parameter intervals of the material yield strength, the cross-section plastic modulus, the lifting weight, and the dynamic load coefficient are respectively represented as δf y , δZ, δW, and δφ.

[0091] The half amplitude calculation formula is:

[0092]

[0093] δX=δf y , δZ, δW, or δφ

[0094] X=f y , Z, W or φ.

[0095] In this solution, the material yield strength range and cross-section plastic modulus range can be obtained from the material test report or design manual of the bridge crane's metal main beam; while the lifting capacity range and dynamic load coefficient range can be obtained from the bridge crane's operating specifications or on-site testing. In addition to the aforementioned channels, the material yield strength range, cross-section plastic modulus range, lifting capacity range, and dynamic load coefficient range for the bridge crane's metal main beam in this solution can also be obtained through other existing channels.

[0096] In this scheme, the determination of the design point is helpful for the stress analysis and evaluation of the metal main beam during operation. Under normal circumstances, the mid-span of the metal main beam is the area with the greatest stress risk. It usually bears the maximum bending moment and shear force, and is the part where deflection and yielding are most likely to occur. At the same time, taking the mid-span as the design point can reduce the calculation complexity. In this scheme, the midpoint of each parameter interval under each working condition is regarded as the design point, which is used to approximate the most unfavorable point, so that the reliability index can be obtained at the "linear function gradient" level.

[0097] Based on this, as a better implementation choice for determining the design point, preferably, in this solution S02, the design point is the mid-span of the metal main beam of the bridge crane, and the design point vector is expressed as X0=(f y0 , Z0, W0, φ0), which is the midpoint of the characteristic parameter interval of the target structure, and the formula is as follows:

[0098]

[0099] Among them, X0 is the characteristic parameter of the design point, f y0 , Z0W0, φ0 are the material yield strength, section plastic modulus, lifting capacity and dynamic load coefficient corresponding to the design point respectively; f y , are the lower and upper limits of the material yield strength respectively; Z , are the lower and upper limits of the section plastic modulus respectively; W , They are the lower and upper limits of the lifting weight respectively; φ , are the lower and upper limits of the dynamic load coefficient respectively.

[0100] As another implementation selection of determining the design point, preferably, in the scheme S02, the design point can also be a non-middle point combination in the characteristic parameter interval of the metal girder of the bridge crane.

[0101] In this case, the design point is determined by minimizing the normalized distance, iterative linearization by gradient projection method, vertex enumeration method or sampling method.

[0102] For example, when the design point is a non-middle point combination in the characteristic parameter interval, a multi-design point evaluation system can be established, taking the load (lifting capacity) W and the dynamic load coefficient φ as the horizontal and vertical coordinates, and setting a nominal design point for each pair of working conditions (W j , φ k ).

[0103] Then, the non-probabilistic reliability of each working condition is calculated in turn to realize one-time prediction of safety in the range of working conditions (W j , φ k ). This multi-point method is more suitable for safety prediction under multiple working conditions compared with the traditional single global design point or Monte-Carlo sampling method.

[0104] Compared with the multi-point evaluation scheme, the advantage of using the midpoint of the characteristic parameter interval as the design point is more prominent, which only needs to be weighted averaged once, without the need to solve optimization or iterative process, and is suitable for real-time and rapid evaluation in engineering field. This method does not depend on the assumption of probability distribution and does not require prior statistical data, only the upper and lower limits of the interval are needed. When evaluating non-probabilistic reliability, the precision of this method is slightly weaker than that of the multi-point nominal design point. Although the midpoint is not the strict most unfavorable point, it can often give similar reliability indicators as the most unfavorable point in the first-order sensitivity approximation.

[0105] In the establishment of the limit state function, as a preferred implementation selection, the scheme S03 comprises:

[0106] S031, establishing a function model of the lifting capacity of the bridge crane and the static load bending moment and the dynamic load bending moment of the metal girder;

[0107] When the bridge crane is lifting, the static load bending moment formula is defined as follows:

[0108]

[0109] Where M stat is the static load bending moment, W and L are the lifting capacity and span of the bridge crane, respectively;

[0110] Combined with the dynamic load coefficient, when the bridge crane is lifting, the dynamic load bending moment formula is defined as follows:

[0111]

[0112] M d is the dynamic load coefficient;

[0113] The bending resistance of the metal girder is defined as follows in combination with the material yield strength and the cross-sectional plastic modulus of the metal girder:

[0114] R = f y × Z

[0115] wherein R is the bending resistance, f y is the material yield strength of the metal girder, and Z is the cross-sectional plastic modulus of the metal girder;

[0116] S032, based on the bending resistance and the dynamic load bending moment, a limit state function about the metal girder is established, which is defined as follows:

[0117]

[0118] wherein f y is the material yield strength of the metal girder, Z is the cross-sectional plastic modulus of the metal girder, W and L are the lifting capacity and span of the bridge crane respectively, and φ is the dynamic load coefficient;

[0119] When the solution value of the limit state function is equal to 0, the surface is the limit state, less than 0, then the device fails, and greater than or equal to 0, then it is safe for the working condition.

[0120] On the basis of the above, the scheme S04 comprises:

[0121] S041, the partial derivative of the limit state function G at the design point is calculated, and the partial derivative is defined as follows:

[0122]

[0123] wherein f y is the material yield strength of the metal girder, Z is the cross-sectional plastic modulus of the metal girder, W and L are the lifting capacity and span of the bridge crane respectively, and φ is the dynamic load coefficient;

[0124] S041, the partial derivative is multiplied by the half amplitude of the corresponding material yield strength, cross-sectional plastic modulus, lifting capacity and dynamic load coefficient, and then the two norm combinations are calculated, which are defined as follows:

[0125]

[0126] wherein, is the gradient norm value, f y0Z0, W0, φ0 are respectively the material yield strength, the cross-section plastic modulus, the lifting weight and the dynamic load coefficient of the design point, and L is the span; δf y , δZ, δW, δφ are the half amplitudes of the parameter intervals of the material yield strength, the cross-section plastic modulus, the lifting weight and the dynamic load coefficient of the metal girder.

[0127] For the evaluation of the non-probabilistic reliability index, as a preferred implementation option, the present scheme S05 comprises:

[0128] Based on the limit state function, the parameters of the metal girder at the design point are substituted to calculate the limit state value at the design point, which is expressed as follows:

[0129]

[0130] Wherein, G0 is the limit state function value at the design point, f y0 Z0, W0, φ0 are respectively the material yield strength, the cross-section plastic modulus, the lifting weight and the dynamic load coefficient of the design point, and L is the span;

[0131] The formula definition of the non-probabilistic reliability index is as follows:

[0132]

[0133] Wherein, β NPR is the non-probabilistic reliability index, G0 is the limit state function value at the design point, is the gradient norm value, which represents the minimum normalized distance of the metal girder from the failure surface in the uncertainty space.

[0134] As a preferred implementation option, the present scheme S06 comprises:

[0135] The calculated non-probabilistic reliability index β NPR is compared with the preset safety threshold β 阈值 , and according to the comparison result, the working condition safety of the crane is determined, and the specific comparison logic is:

[0136] When β NPR ≥ β 阈值 , it is determined that the working condition is safe, and when β NPR < β 阈值 , it is determined that the working condition is dangerous.

[0137] As a calculation example, the present scheme takes a single-span bridge crane in a factory as an example to illustrate the practical application of the non-probabilistic reliability model in multi-working condition safety prediction.

[0138] Wherein, the engineering parameters of the metal girder of the bridge crane include the following:

[0139] Span L = 20m;

[0140] The steel yield strength interval is f y ∈ [235, 265] units of Mpa, half amplitude δf y = 15Mpa;

[0141] The cross section plastic model interval is Z ∈ [8000, 9000] units of cm 3 , half amplitude δZ = 500cm 3 ;

[0142] The lifting weight working condition interval is W ∈ [50, 150] units of kN, half amplitude δW = 50kN;

[0143] The dynamic load coefficient interval is φ ∈ [1.0, 1.3] units of dimensionless, half amplitude δφ = 0.15;

[0144] Taking the midspan of the metal girder as the design point, the material yield strength f y0 = 250Mpa at the design point, the cross section plastic modulus Z0 = 8500cm 3 ;

[0145] Working condition A is: W = 100kN, φ = 1.2;

[0146] Under the above basic parameters, the related parameters of the bridge crane during lifting are calculated as follows:

[0147] The static load bending moment is

[0148] The dynamic load bending moment

[0149] The limit state function of the metal girder is calculated as:

[0150]

[0151] The gradient norm is calculated as:

[0152]

[0153] The non-probabilistic reliability preparation is calculated as:

[0154] Suppose β 阈值 = 1, then under working condition A, β NPR ≈ 11.9 ≥ 1, under this working condition, it is a working condition safety.

[0155] Based on the above, the scheme further proposes a bridge crane multi-working condition working management method, which comprises: in response to the power-on starting signal of the bridge crane, executing the crane multi-working condition safety prediction method based on non-probabilistic reliability described above.

[0156] As a relatively optimal implementation option, preferably, the scheme further comprises:

[0157] When the bridge crane is in a dangerous working condition, the working risk of the bridge crane is reduced by lowering the upper limit of the lifting weight limit and / or reducing the upper limit of the dynamic load coefficient based on the safety assessment result of the bridge crane obtained in S06.

[0158] When the bridge crane is in a dangerous working condition, the working risk of the bridge crane is reduced by lowering the upper limit of the lifting weight limit and / or reducing the upper limit of the dynamic load coefficient based on the safety assessment result of the bridge crane obtained in S06.

[0159]

[0160] wherein, W sf is the adjusted upper limit of the allowable lifting weight limit, f y0 , Z0 are the yield strength of the design point material and the plastic modulus of the cross section respectively, L is the span, β 阈值 is a preset safety threshold of the non-probabilistic reliability index, is a gradient norm value, and φ k is the dynamic load coefficient of the current working condition.

[0161] The formula for reducing the upper limit of the dynamic load coefficient is as follows:

[0162]

[0163] wherein, φ sf is the adjusted upper limit of the dynamic load coefficient limit, f y0 , Z0 are the yield strength of the design point material and the plastic modulus of the cross section respectively, L is the span, β 阈值 is a preset safety threshold of the non-probabilistic reliability index, is a gradient norm value, and w j is the load weight of the current working condition.

[0164] In combination with Figure 2 , based on the above, the scheme further proposes a bridge crane multi-working condition management system, which comprises:

[0165] A data acquisition module is configured to monitor the working parameters of the bridge crane in real time in response to a working start signal of the bridge crane.

[0166] A working condition evaluation module loaded with the bridge crane multi-working condition management method described above is configured to evaluate the safety of the real-time working condition of the bridge crane and generate a working condition safety evaluation result.

[0167] The working intervention module is configured to acquire the working condition safety assessment result of the bridge crane, and reduce the working risk of the bridge crane by reducing the upper limit of the lifting weight limit and / or reducing the upper limit of the dynamic load coefficient according to a preset condition.

[0168] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0169] If the integrated unit is realized in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application, essentially or the part that contributes to the prior art, or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods in the various embodiments of the present application. The aforementioned storage medium includes various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk.

[0170] The above only describes some embodiments of the present application, and does not limit the protection scope of the present application, and any equivalent device or equivalent process transformation made by using the content of the present application specification and drawings, or directly or indirectly applied to other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A crane multi-operating condition safety prediction method based on non-probabilistic reliability, characterized by: It includes: S01. Obtaining characteristic parameter intervals of a target structure of a bridge crane; S02. Based on the characteristic parameter interval, select the design point; S03. Establish a limit state function based on the load capacity and span L of the bridge crane; S04. Calculate the partial derivative of the limit state function at the design point, normalize it, and then calculate the gradient norm value; S05. Based on the limit state function, the limit state function value at the design point is calculated, and then the non-probabilistic reliability index at the design point is solved in combination with the gradient norm value; S06. Compare the calculated non-probabilistic reliability index with a preset safety threshold, and determine the safety of the crane's operating condition based on the comparison result.

2. The crane multi-operating condition safety prediction method based on non-probabilistic reliability according to claim 1 is characterized in that: In S01, the target structure is a metal main beam of a bridge crane, which is the area subject to bending and shearing when the bridge crane bears a lifting load; The characteristic parameters include the material yield strength interval, the section plastic modulus interval, the lifting capacity interval and the dynamic load coefficient interval; wherein, the material yield strength interval is f y , are the lower and upper limits of the material yield strength respectively; the plastic modulus range of the section is Z , are the lower and upper limits of the section plastic modulus respectively; the lifting capacity range is W , are the lower and upper limits of the lifting capacity respectively; the dynamic load coefficient range is φ , are the lower and upper limits of the dynamic load coefficient respectively; S01 also includes: calculating the half-width of each characteristic parameter according to the characteristic parameter interval. The half-width corresponding to the parameter interval of material yield strength, section plastic modulus, lifting weight and dynamic load coefficient is expressed as δf y , δZ, δW, δφ.

3. The crane multi-operating condition safety prediction method based on non-probabilistic reliability according to claim 2 is characterized in that: In S02, the design point is the mid-span of the metal main beam of the bridge crane, and the design point vector is represented by X0=(f y0 ,Z0,W0,φ0), which is the midpoint of the characteristic parameter interval of the target structure, and the formula is as follows: Among them, X0 is the characteristic parameter of the design point, f y0 , Z0, W0, φ0 are the material yield strength, section plastic modulus, lifting capacity and dynamic load coefficient corresponding to the design point respectively; f y , are the lower and upper limits of the material yield strength respectively; Z , are the lower and upper limits of the section plastic modulus respectively; W , They are the lower and upper limits of the lifting weight respectively; φ , are the lower and upper limits of the dynamic load coefficient respectively.

4. The crane multi-operating condition safety prediction method based on non-probabilistic reliability according to claim 2, characterized in that: In S02, the design point is a non-midpoint combination in the characteristic parameter interval of the metal main beam of the bridge crane; The design point is determined by minimizing the normalized distance, performing iterative linearization solution using a gradient projection method, performing a vertex enumeration method, or performing a sampling method.

5. The crane multi-operating condition safety prediction method based on non-probabilistic reliability according to claim 3 is characterized in that: S03 includes: S031. Establish a functional model of the lifting capacity of a bridge crane and the static and dynamic bending moments of the metal main beam; When a bridge crane is lifting, the static load bending moment formula is defined as follows: Among them, M stat is the static load bending moment, W and L are the lifting capacity and span of the bridge crane respectively; Combined with the dynamic load coefficient, the formula for the dynamic load bending moment of a bridge crane during lifting is defined as follows: M d is the dynamic load bending moment, φ is the dynamic load coefficient; Combining the material yield strength and section plastic modulus of the metal main beam, its bending bearing capacity is defined as follows: R=f y ×Z Where R is the bending bearing capacity, f y is the material yield strength of the metal main beam, and Z is the section plastic modulus of the metal main beam; S032. Based on the bending capacity and dynamic load bending moment, establish the limit state function of the metal main beam, which is defined as follows: Among them, f y is the material yield strength of the metal main beam, Z is the section plastic modulus of the metal main beam, W and L are the lifting capacity and span of the bridge crane respectively, and φ is the dynamic load coefficient; When the solution value of the limit state function is equal to 0, the surface is in the limit state. When it is less than 0, the equipment fails. When it is greater than or equal to 0, the working condition is safe.

6. The crane multi-operating condition safety prediction method based on non-probabilistic reliability according to claim 5 is characterized in that: S04 includes: S041. Calculate the partial derivative of the limit state function G at the design point. The partial derivative is defined as follows: Among them, f y is the material yield strength of the metal main beam, Z is the section plastic modulus of the metal main beam, W and L are the lifting capacity and span of the bridge crane respectively, and φ is the dynamic load coefficient; S041. Multiply the partial derivative with the corresponding material yield strength, section plastic modulus, lifting weight, and half-width of the dynamic load coefficient, and then perform the two-norm combination. The definition is as follows: in, is the gradient norm value, f y0 , Z0, W0, φ0 are the corresponding material yield strength, section plastic modulus, lifting weight and dynamic load coefficient when the design point is taken as the mid-span, L is the span; δf y , δZ, δW, and δφ are the half-width of the parameter interval corresponding to the material yield strength, section plastic modulus, lifting capacity, and dynamic load coefficient of the metal main beam.

7. The crane multi-operating condition safety prediction method based on non-probabilistic reliability according to claim 6 is characterized in that: S05 includes: Based on the limit state function, the parameters of the mid-span of the metal main beam are substituted as the design point to calculate the limit state value at the design point, which is expressed as follows: Among them, G0 is the limit state function value at the design point, f y0 , Z0, W0, φ0 are the material yield strength, section plastic modulus, lifting weight and dynamic load coefficient corresponding to the design point, and L is the span; The formula of non-probabilistic reliability index is defined as follows: Among them, β NPR is the non-probabilistic reliability index, G0 is the limit state function value at the design point, is the gradient norm value, which represents the minimum normalized distance of the metal main beam from the failure surface in the uncertainty space; S06 includes: The calculated non-probabilistic reliability index β NPR and the preset safety threshold β 阔值 Compare and determine the safety of the crane's working condition based on the comparison results. The specific comparison logic is: β NPR ≥β 阈值 When the working condition is determined to be safe, β NPR <β 阈值 , judge whether the working condition is dangerous.

8. A multi-working mode management method for a bridge crane, characterized in that: It includes: In response to the power-on start signal of the bridge crane, the crane multi-operating condition safety prediction method based on non-probabilistic reliability as described in one of claims 1 to 7 is executed.

9. The multi-operation management method for a bridge crane according to claim 8, characterized in that: It includes: Obtaining the result of the operating condition safety of the bridge crane determined in S06, and if it indicates a dangerous operating condition, reducing the upper limit of the bridge crane's lifting capacity and / or the upper limit of the dynamic load coefficient to reduce the operating risk of the bridge crane; When the result of the working condition safety of the bridge crane determined by S06 indicates that the working condition is dangerous, the formula for lowering the upper limit of the lifting capacity of the bridge crane is as follows: Among them, W sf f is the upper limit of the allowed lifting weight after adjustment, y0 , Z0 are the material yield strength and section plastic modulus at the design point, L is the span, β 阈值 is the preset safety threshold of the non-probabilistic reliability index, is the gradient norm, φ k is the dynamic load coefficient of the current working condition; The formula for reducing the upper limit of the dynamic load coefficient is as follows: Among them, φ sf is the upper limit of the dynamic load coefficient after adjustment, f y0 , Z0 are the material yield strength and section plastic modulus at the design point, L is the span, β 阈值 is the preset safety threshold of the non-probabilistic reliability index, is the gradient norm value, w j is the load capacity of the current working condition.

10. A multi-working condition management system for a bridge crane, characterized in that: It includes: The data acquisition module is used to respond to the working start signal of the bridge crane and monitor the working parameters of the bridge crane in real time; a working condition assessment module, which is loaded with the multi-working condition management method of the bridge crane according to claim 9 and is used to perform working condition safety assessment on the real-time working condition of the bridge crane and generate working condition safety assessment results; The work intervention module is used to obtain the working safety assessment results of the bridge crane and reduce the working risk of the bridge crane by lowering the upper limit of the bridge crane's lifting weight limit and / or reducing the upper limit of the dynamic load coefficient according to preset conditions.

Images