CN115809577A - Method and system for evaluating fatigue life of metal structure of bridge crane - Google Patents
Method and system for evaluating fatigue life of metal structure of bridge crane Download PDFInfo
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Abstract
The invention relates to a fatigue life assessment technology of equipment, and provides a method and a system for assessing the fatigue life of a metal structure of a bridge crane. The method comprises the following steps: determining the position of a dangerous point of the bridge crane, wherein the dangerous point is a measuring point; arranging a sensor network according to the number and the positions of the measuring points; obtaining measurement data of each sensor in the sensor network; obtaining the stress-time course of the bridge crane according to the measurement data; carrying out statistical analysis on the stress-time history of the bridge crane to obtain the statistical characteristics of the load data; compiling a real-time equivalent load spectrum of the bridge crane according to the statistical characteristics; and evaluating the residual fatigue life of the bridge crane according to the real-time equivalent load spectrum. By adopting the embodiment of the invention, the residual fatigue life of the bridge crane can be monitored in real time, and the healthy and safe operation of equipment can be ensured.
Description
Technical Field
The invention relates to a device fatigue life evaluation technology, in particular to a method and a system for evaluating the fatigue life of a metal structure of a bridge crane.
Background
The bridge crane is an important material handling device and is widely applied to industrial enterprises. The bridge crane has the advantages of severe working environment, frequent starting, large hoisting weight and high failure rate, so that the number of safety accidents caused by the failure is large, and great potential safety hazards are brought to life and property of people and enterprise production. However, the welded structure is one of the most important features of modern bridge cranes, the metal structure and the welding seam bear alternating load for a long time, fatigue damage of the metal structure of the bridge crane gradually accumulates, and macroscopic plastic deformation occurs when the damage limit is reached, so that structural damage is caused, and catastrophic accidents are caused. Therefore, the research on the fatigue failure rule of the metal structure of the bridge crane is developed, the residual fatigue life and the reliability of the metal structure are evaluated, and the method has important practical significance and engineering value for preventing and stopping the occurrence of the fatigue fracture accident of the bridge crane.
In the prior art, a finite element method is usually adopted to research the static strength of a bridge crane structure, analyze the change condition of the stress/strain value of a metal structure of the bridge crane under different load levels, and invite experts to judge the residual life and the safety state of the bridge crane by combining engineering experience and test data. In the prior art, the change of stress/strain of the bridge crane under different load levels is calculated by adopting a theoretical simulation method under the condition of static load, the time-varying property of the load of the bridge crane is not considered, and the residual life of the bridge crane cannot be quantitatively evaluated, so that the analysis result of the prior art has poor engineering practicability, and the evaluation accuracy and precision of the residual life are low.
Disclosure of Invention
The invention provides a method and a system for automatically evaluating the fatigue life of a metal structure of a bridge crane.
In one aspect, embodiments of the present invention provide a method for assessing fatigue life of a metal structure of a bridge crane, the method comprising:
determining the position of a dangerous point of the bridge crane, wherein the dangerous point is a measuring point;
arranging a sensor network according to the number and the positions of the measuring points;
obtaining measurement data of each sensor in the sensor network;
obtaining the stress-time history of the bridge crane according to the measurement data;
carrying out statistical analysis on the stress-time history of the bridge crane to obtain the statistical characteristics of load data;
compiling a real-time equivalent load spectrum of the bridge crane according to the statistical characteristics;
and evaluating the residual fatigue life of the bridge crane according to the real-time equivalent load spectrum.
In some embodiments, the determining the location of the hazard point for the bridge crane comprises:
establishing a three-dimensional model of a bridge crane structure;
generating a finite element model according to the three-dimensional model of the bridge crane structure;
solving and calculating the finite element model to obtain a stress cloud picture, a displacement cloud picture and a dangerous section position of the bridge crane;
and determining the position of the dangerous point of the bridge crane according to the working principle and the safety margin of the bridge crane and by combining the stress cloud picture, the displacement cloud picture and the position of the dangerous section.
In some embodiments, the stress-time history of the bridge crane is statistically analyzed by rain flow counting to obtain statistical characteristics of the load data.
In some embodiments, compiling the real-time equivalent load spectrum of the bridge crane from the statistical features comprises:
drawing a frequency-amplitude histogram of the stress according to the statistical characteristics of the obtained load data;
fitting the frequency-amplitude histogram of the stress by adopting a two-parameter Weibull distribution formula to obtain a Weibull approximate expression of the load spectrum of the bridge crane; wherein, the two parameters have the following Weibull distribution formula:
where α is a scale parameter of the weibull distribution, β is a shape parameter of the weibull distribution, and SA is a variable range cycle value obtained by the rain flow counting method.
In some embodiments, the method further comprises:
judging that the estimated value of the residual service life of the bridge crane is greater than a service life threshold value, sending alarm information and stopping the bridge crane,
and if the evaluation value of the residual service life of the bridge crane is judged to be smaller than the service life threshold value, continuously acquiring the measurement data and evaluating the residual fatigue life of the bridge crane according to the measurement data.
In another aspect, embodiments of the present invention also provide a system for evaluating fatigue life of a metal structure of a bridge crane, including:
the data acquisition module is used for acquiring the measurement data of each sensor arranged at each measuring point of the bridge crane;
a data processing module for performing the following operations: obtaining the stress-time course of the bridge crane according to the measurement data; carrying out statistical analysis on the stress-time history of the bridge crane to obtain the statistical characteristics of the load data; compiling a real-time equivalent load spectrum of the bridge crane according to the statistical characteristics; and evaluating the residual fatigue life of the bridge crane according to the real-time equivalent load spectrum.
In some embodiments, the system further comprises a station determination module for determining a station of the bridge crane according to the following method:
establishing a three-dimensional model of a bridge crane structure;
generating a finite element model according to the three-dimensional model of the bridge crane structure;
solving and calculating the finite element model to obtain a stress cloud picture, a displacement cloud picture and a dangerous section position of the bridge crane;
and determining the position of a dangerous point of the bridge crane according to the working principle and the safety margin of the bridge crane and by combining the stress cloud picture, the displacement cloud picture and the position of the dangerous section, wherein the dangerous point is a measuring point.
In some embodiments, the data processing module performs statistical analysis on the stress-time history of the bridge crane by a rain flow counting method to obtain statistical characteristics of the load data.
In some embodiments, the data processing module compiles a real-time equivalent load spectrum of the bridge crane according to the statistical characteristics, comprising:
drawing a frequency-amplitude histogram of the stress according to the statistical characteristics of the obtained load data;
fitting the frequency-amplitude histogram of the stress by adopting a two-parameter Weibull distribution formula to obtain a Weibull approximate expression of the load spectrum of the bridge crane; wherein, the two parameters have the following Weibull distribution formula:
wherein α is a scale parameter of the Weibull distribution, β is a shape parameter of the Weibull distribution, and SA is a range cycle value obtained by the rain flow counting method.
In some embodiments, the data processing module further performs the following operations:
judging that the estimated value of the residual service life of the bridge crane is greater than a service life threshold value, sending alarm information and stopping the bridge crane,
and if the evaluation value of the residual service life of the bridge crane is judged to be smaller than the service life threshold value, continuously acquiring the measurement data through a data acquisition module, and evaluating the processing of the residual fatigue life of the bridge crane according to the measurement data.
According to the embodiment of the invention, the real-time monitoring of the residual fatigue life of the bridge crane can be realized, and the method has the following advantages: (1) the selection of dangerous points (measuring points) of the bridge crane is accurate, and the arrangement of the measuring points is accurate; (2) the real-time online state monitoring and early warning of the bridge crane can be realized; (3) the service life threshold value can be automatically adjusted, and the safety of equipment and personnel is ensured to the maximum extent.
Various aspects, features, advantages, etc. of embodiments of the invention are described in detail below with reference to the accompanying drawings. The above aspects, features, advantages, etc. of the present invention will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings.
Drawings
Fig. 1 is a flow chart of a method for assessing fatigue life of a metal structure of a bridge crane according to an embodiment of the present invention.
Fig. 2 is a flowchart illustrating an exemplary process procedure of step S100 in fig. 1.
Fig. 3 is a histogram showing a real-time equivalent load spectrum of a bridge crane according to an embodiment of the present invention.
Fig. 4 is a schematic diagram illustrating Miner's theory.
FIG. 5 is a logic block diagram of a system for assessing fatigue life of a metallic structure of a bridge crane according to an embodiment of the present invention.
Fig. 6 is an example of a hardware structure of a system for evaluating fatigue life of a metal structure of a bridge crane according to an embodiment of the present invention.
Fig. 7 shows an example of a sensor arrangement of an embodiment of the present invention.
Detailed Description
Hereinafter, exemplary embodiments will be described in more detail with reference to the accompanying drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided by way of example so that this disclosure will be thorough and complete, and will fully convey the aspects and features of the invention to those skilled in the art. Accordingly, processes, elements, and techniques that are not necessary for a person of ordinary skill in the art to fully appreciate the aspects and features of the present invention may not be described. Unless otherwise indicated, like reference numerals refer to like elements throughout the drawings and the description, and thus, the description thereof may not be repeated. Furthermore, features or aspects within each exemplary embodiment should generally be considered other similar features or aspects that may be used in other exemplary embodiments.
Certain terminology may be used in the following description for reference only and is therefore not intended to be limiting. For example, terms such as "top," bottom, "" upper, "" lower, "" above, "" at 82303030, and "below" at 8230, may be used to refer to directions in the drawings to which reference is made. Terms such as "front," "back," "rear," "side," "outer," and "inner" may be used to describe the orientation and/or position of portions of the component within a consistent but arbitrary frame of reference as may be clearly understood by reference to the text and the associated drawings describing the component in question. Such terms may include the words specifically mentioned above, derivatives thereof, and words of similar import. Similarly, the terms "first," "second," and other such numerical terms referring to structures do not imply a sequence or order unless clearly indicated by the context.
It will be understood that when an element or feature is referred to as being "on," "connected to" or "coupled to" another element or layer, it can be directly on, connected or coupled to the other element or feature or one or more intervening elements or features may be present. In addition, it will also be understood that when an element or feature is referred to as being "between" two elements or features, it can be the only element or feature between the two elements or features, or one or more intervening elements or features may also be present.
The terminology used herein is for the purpose of describing particular embodiments and is not intended to be limiting of the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items. Expressions such as at least one of "\8230". Before the list of elements, modify the entire list of elements rather than modifying individual elements of the list.
As used herein, the terms "substantially," "about," and the like are used as approximate terms and not as degree terms, and are intended to take into account the inherent variation in measured or calculated values that would be recognized by one of ordinary skill in the art. Furthermore, the use of "may" in describing embodiments of the invention is meant to be "one or more embodiments of the invention". As used herein, the terms "using," "using," and "used" may be considered synonymous with the terms "utilizing," "utilizing," and "utilized," respectively.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and/or the present specification and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The invention provides a scheme for evaluating the fatigue life of a metal structure of a bridge crane. Determining structural danger points of the bridge crane according to a finite element stress analysis result of the bridge crane, and arranging measuring points according to the structural danger points of the bridge crane so as to arrange a sensor network; collecting structural stress data of the bridge crane in real time in an operating state by adopting intelligent data online collecting and processing equipment; and (3) evaluating the residual fatigue life of the bridge crane by adopting an equivalent stress method and a fatigue damage accumulation criterion and combining real-time operation data of equipment.
Fig. 1 shows a flow of a method for assessing fatigue life of a metal structure of a bridge crane according to an embodiment of the invention. In an embodiment, the method for evaluating the fatigue life of a metal structure of a bridge crane comprises:
s100, determining the position of a dangerous point of the bridge crane, wherein the dangerous point is a measuring point;
s200, arranging a sensor network according to the number and the positions of the measuring points;
s300, obtaining measurement data of each sensor in the sensor network;
s400, obtaining a stress-time process of the bridge crane according to the measurement data;
s500, carrying out statistical analysis on the stress-time history of the bridge crane to obtain the statistical characteristics of load data;
s600, compiling a real-time equivalent load spectrum of the bridge crane according to the statistical characteristics;
and S700, evaluating the residual fatigue life of the bridge crane according to the real-time equivalent load spectrum.
In some embodiments, in S100, the determining the dangerous point position of the bridge crane includes:
s101, establishing a three-dimensional model of the bridge crane structure. In some embodiments, geometric shapes in the three-dimensional model that do not affect the analysis results (e.g., chamfers, geometric designs that are not related to function, etc.) are eliminated.
And S102, generating a finite element model according to the three-dimensional model of the bridge crane structure. In some embodiments, the bridge crane three-dimensional model is imported into a finite element preprocessing software ANSA, and geometric patching, meshing (surface/body), material property setting, constraint condition setting, boundary condition loading (displacement, force, etc.), and analysis step setting are performed, so as to obtain a finite element model. The ANSA (Automatic Net-generation for Structural Analysis) is a finite element preprocessing software and is mainly applied to the modeling process of finite element Analysis in the fields of collision, fatigue and the like.
And S103, solving and calculating the finite element model to obtain a stress cloud picture, a displacement cloud picture and a dangerous section position of the bridge crane. In some embodiments, the finite element model (preprocessed file) generated by the ANSA is imported into finite element analysis software (ANSYS/ABAQUS), and solution calculation is performed to obtain a stress cloud picture, a displacement cloud picture and a dangerous section position of the bridge crane.
And S104, determining the position of the dangerous point of the bridge crane according to the working principle and the safety margin of the bridge crane and by combining the stress cloud picture, the displacement cloud picture and the position of the dangerous section. In some embodiments, the position of the dangerous point of the bridge crane is determined according to the working principle and the safety margin of the bridge crane by using a fatigue analysis software (FE-SAFE: a software for fatigue durability analysis and signal processing).
In some embodiments, in S200, a sensor network is arranged according to the number and position of the measuring points, for example, as shown in fig. 7, strain gauges 3, 4, 5, 8, 9, 10 are respectively mounted on a cart metal structure of an overhead crane. And then checking the working condition and the precision of the installed related data acquisition and processing equipment, and correcting the signal. In S300-S400, the equipment is operated, and real-time data acquisition and processing are performed to obtain the stress-time history of the bridge crane.
In some embodiments, in S500, the stress-time history of the bridge crane is statistically analyzed by a rain flow counting method to obtain statistical characteristics such as amplitude, frequency, and the like of data.
In some embodiments, the rain flow counting method statistical processing is implemented as follows:
(1) Initializing and preprocessing initial data of the stress-time process of the bridge crane, and calculating a data extreme value X max And X min And group spacing;
(2) And (5) grading treatment. The stress-time history is graded as follows:
wherein C is the inter-group spacing, L is the number of hierarchical levels, X max 、X min The maximum and minimum values of the measurement data, i.e. the stress values, are respectively.
(3) And (5) counting the variable range cycle value SA. And (3) carrying out statistical treatment on the variable range circulating value SA, wherein the group median is treated according to the following formula:
in the formula, SA J Is a variation of group JMean value of round trip group, SA i Is the ith cycle value of the variation of the J-th group, and N is the cycle times of all the variations of the J-th group.
(4) And (5) counting the variation range mean value SM. When the variable range cycle mean value is processed, the group mean value is taken according to the following formula:
in the formula, SM J Average of all variation cycle means, SM, of group J i The average value of the ith cycle of the variation of the J-th group.
(5) Statistics of the Total mean SU
Where SU is the average of the averages of all variation cycles, SM I Is the average of the I-th cycle and Z is the number of cycles of all cycles.
Through the processing, the statistical characteristics such as the amplitude, the mean value and the frequency of data are extracted from the stress-time history data of the initial bridge crane, wherein SM is the mean value of variable-stroke cycle data, SA is the amplitude of the variable-stroke cycle data, and N is the frequency of the variable-stroke cycle.
In some embodiments, at S600, compiling a real-time equivalent load spectrum of the bridge crane according to the statistical features includes:
and drawing a frequency-amplitude histogram of the stress according to the statistical processing result of the initial data. Fitting the frequency-amplitude histogram of the stress by adopting a two-parameter Weibull distribution formula, and carrying out correlation test to obtain a Weibull approximate expression of the load spectrum of the bridge crane, namely the two-dimensional load spectrum of the bridge crane. Wherein, the two parameters have the following Weibull distribution formula:
where α is a scale parameter of the weibull distribution, β is a shape parameter of the weibull distribution, and SA is a variable range cycle value obtained by the rain flow counting method.
The real-time equivalent load spectrum of the bridge crane obtained by the above processing is shown in fig. 3.
In some embodiments, in S700, the fatigue life of the bridge crane is estimated by calculating the fatigue life of the bridge crane at different stress levels using a fatigue damage accumulation rule (e.g., miner' S rule, etc.) in combination with a metal structure fatigue S-N curve or P-S-N curve. The S-N curve is a curve representing the relationship between fatigue strength and fatigue life of a standard test piece under a certain cycle characteristic, which is also called a stress-life curve, with fatigue strength (i.e., stress) of the material standard test piece as a vertical coordinate and a logarithmic value lgN of the fatigue life (i.e., cycle number) as a horizontal coordinate. The P-S-N curve refers to S-N curves corresponding to different survival rates P, which are plotted in consideration of the dispersion of fatigue life.
The Miner theory diagram is shown in FIG. 4. In Miner's theory, n is passed i The fatigue damage at the second cycle was:
in the formula: n is i Number of cycles for the i-th order stress level in each run, N i Fatigue life at the i-th order stress level.
Stress σ i When the damage to the metal structure reaches a critical value D, the material is damaged, namely
After simplification, the following is obtained:
in the formula: r is the number of circulating species.
In some embodiments, the method for assessing fatigue life of a metal structure of a bridge crane further comprises:
judging that the estimated value of the residual service life of the bridge crane is greater than the service life threshold value, sending alarm information (such as text information, voice prompt, light prompt and the like) and stopping the crane,
and if the evaluation value of the residual service life of the bridge crane is judged to be smaller than the service life threshold value, continuously acquiring the measurement data and evaluating the residual fatigue life of the bridge crane according to the measurement data.
The method for evaluating the fatigue life of the metal structure of the bridge crane of the present invention has been explained above. Accordingly, an embodiment of the present invention also provides a system for evaluating fatigue life of a metal structure of a bridge crane, as shown in fig. 5, the system comprising:
a data acquisition module 1100, configured to acquire measurement data of each sensor arranged at each measurement point of the bridge crane;
a data processing module 2100 configured to perform the following operations: obtaining the stress-time course of the bridge crane according to the measurement data; carrying out statistical analysis on the stress-time history of the bridge crane to obtain the statistical characteristics of the load data; compiling a real-time equivalent load spectrum of the bridge crane according to the statistical characteristics; and evaluating the residual fatigue life of the bridge crane according to the real-time equivalent load spectrum.
In some embodiments, the system further comprises a station determination module (not shown) for determining a station of the bridge crane according to the following method: establishing a three-dimensional model of a bridge crane structure; generating a finite element model according to the three-dimensional model of the bridge crane structure; solving and calculating the finite element model to obtain a stress cloud picture, a displacement cloud picture and a dangerous section position of the bridge crane; and determining the position of a dangerous point of the bridge crane according to the working principle and the safety margin of the bridge crane and by combining the stress cloud picture, the displacement cloud picture and the position of the dangerous section, wherein the dangerous point is a measuring point.
In some embodiments, the data processing module 2100 statistically analyzes the stress-time history of the bridge crane by rain flow counting to obtain statistical characteristics of the load data (amplitude, frequency, etc. of the data).
In some embodiments, the data processing module 2100 compiles a real-time equivalent load spectrum of the bridge crane from the statistical features, including:
drawing a frequency-amplitude histogram of the stress according to the statistical characteristics of the obtained load data;
fitting the frequency-amplitude histogram of the stress by adopting a two-parameter Weibull distribution formula to obtain a Weibull approximate expression of the load spectrum of the bridge crane; wherein, the two parameters have the following Weibull distribution formula:
where α is a scale parameter of the weibull distribution, β is a shape parameter of the weibull distribution, and SA is a variable range cycle value obtained by the rain flow counting method.
In some embodiments, the data processing module 2100 further performs the following operations:
judging whether the evaluation value of the residual service life of the bridge crane is greater than a service life threshold value, sending alarm information and stopping the bridge crane,
and if the evaluation value of the residual service life of the bridge crane is judged to be smaller than the service life threshold value, continuously acquiring the measurement data through a data acquisition module, and evaluating the processing of the residual fatigue life of the bridge crane according to the measurement data.
In some embodiments, the system for assessing fatigue life of a metal structure of a bridge crane may be a computer device comprising a memory and a processor, wherein the memory has stored thereon computer readable instructions or programs that the processor executes to perform operations performed by the data acquisition module, the data processing module, or the station determination module. In some embodiments, as shown in fig. 6, the computer device comprises an industrial personal computer 1, which is in communication with sensors, including strain gauges 3, 4, 5, 8, 9, 10 and vibration sensors 6, 7, 11, 12, through an industrial control board 2. As shown in fig. 7, the strain gauges 3, 4, 5, 8, 9, 10 are respectively mounted on the cart metal structure. Vibration sensors 6, 7, 11, 12 (not shown in fig. 7) are respectively installed at four pulley bearing seats of the cart. The measurement data of each sensor is transmitted to the industrial personal computer 1 through the industrial personal computer 2, and the industrial personal computer 1 processes the obtained measurement data according to the method of each embodiment of the invention, so that the estimation of the residual fatigue life of the bridge crane is completed. The equipment and the device are arranged on the bridge crane and used as a health state monitoring system of the bridge crane, so that the health state of the bridge crane can be monitored in real time, and the healthy and safe operation of the equipment is guaranteed.
In some embodiments, data on displacement, stress, strain, etc. of critical locations are obtained by the condition monitoring system installed on the bridge crane; the method comprises the steps of establishing a virtual model of the bridge crane, establishing a twin model of the bridge crane by combining real-time operation data, driving the twin model to operate through the real-time data of a physical model, monitoring the operation state of the bridge crane from the twin model, and ensuring the safe operation of equipment.
It should be understood by those skilled in the art that the foregoing is only illustrative of the embodiments of the present invention, and is not intended to limit the scope of the invention as claimed.
Claims (10)
1. A method for assessing fatigue life of a metallic structure of a bridge crane, the method comprising:
determining the position of a dangerous point of the bridge crane, wherein the dangerous point is a measuring point;
arranging a sensor network according to the number and the positions of the measuring points;
obtaining measurement data of each sensor in the sensor network;
obtaining the stress-time history of the bridge crane according to the measurement data;
carrying out statistical analysis on the stress-time history of the bridge crane to obtain the statistical characteristics of the load data;
compiling a real-time equivalent load spectrum of the bridge crane according to the statistical characteristics;
and evaluating the residual fatigue life of the bridge crane according to the real-time equivalent load spectrum.
2. The method of claim 1, wherein the determining the location of the hazard point for the bridge crane comprises:
establishing a three-dimensional model of a bridge crane structure;
generating a finite element model according to the three-dimensional model of the bridge crane structure;
solving and calculating the finite element model to obtain a stress cloud picture, a displacement cloud picture and a dangerous section position of the bridge crane;
and determining the position of the dangerous point of the bridge crane according to the working principle and the safety margin of the bridge crane and by combining the stress cloud picture, the displacement cloud picture and the position of the dangerous section.
3. Method according to claim 1, characterized in that the stress-time history of the bridge crane is statistically analyzed by means of rain flow counting to obtain statistical characteristics of the load data.
4. The method according to claim 3, wherein compiling a real-time equivalent load spectrum of the bridge crane from the statistical features comprises:
drawing a frequency-amplitude histogram of the stress according to the statistical characteristics of the obtained load data;
fitting the frequency-amplitude histogram of the stress by adopting a two-parameter Weibull distribution formula to obtain a Weibull approximate expression of a load spectrum of the bridge crane; wherein, the two parameters have the following Weibull distribution formula:
wherein α is a scale parameter of the Weibull distribution, β is a shape parameter of the Weibull distribution, and SA is a range cycle value obtained by the rain flow counting method.
5. The method of claim 1, further comprising:
judging that the estimated value of the residual service life of the bridge crane is greater than a service life threshold value, sending alarm information and stopping the bridge crane,
and if the evaluation value of the residual service life of the bridge crane is judged to be smaller than the service life threshold value, continuously acquiring the measurement data and evaluating the residual fatigue life of the bridge crane according to the measurement data.
6. A system for assessing fatigue life of a metallic structure of a bridge crane, comprising:
the data acquisition module is used for acquiring the measurement data of each sensor arranged at each measuring point of the bridge crane;
a data processing module for performing the following operations:
obtaining the stress-time history of the bridge crane according to the measurement data;
carrying out statistical analysis on the stress-time history of the bridge crane to obtain the statistical characteristics of load data;
compiling a real-time equivalent load spectrum of the bridge crane according to the statistical characteristics;
and evaluating the residual fatigue life of the bridge crane according to the real-time equivalent load spectrum.
7. The system of claim 6, further comprising a station determination module for determining the stations of the bridge crane according to the following method:
establishing a three-dimensional model of a bridge crane structure;
generating a finite element model according to the three-dimensional model of the bridge crane structure;
solving and calculating the finite element model to obtain a stress cloud picture, a displacement cloud picture and a dangerous section position of the bridge crane;
and determining the position of a dangerous point of the bridge crane according to the working principle and the safety margin of the bridge crane and by combining the stress cloud picture, the displacement cloud picture and the position of the dangerous section, wherein the dangerous point is a measuring point.
8. The system of claim 6, wherein the data processing module statistically analyzes the stress-time history of the bridge crane by rain flow counting to obtain statistical characteristics of the load data.
9. The system of claim 8, wherein the data processing module compiles a real-time equivalent load spectrum of the bridge crane according to the statistical characteristics, comprising:
drawing a frequency-amplitude histogram of the stress according to the statistical characteristics of the obtained load data;
fitting the frequency-amplitude histogram of the stress by adopting a two-parameter Weibull distribution formula to obtain a Weibull approximate expression of the load spectrum of the bridge crane; wherein, the two parameters have the following Weibull distribution formula:
wherein α is a scale parameter of the Weibull distribution, β is a shape parameter of the Weibull distribution, and SA is a range cycle value obtained by the rain flow counting method.
10. The system of claim 6, wherein the data processing module further performs the following operations:
judging that the estimated value of the residual service life of the bridge crane is greater than a service life threshold value, sending alarm information and stopping the bridge crane,
and if the evaluation value of the residual service life of the bridge crane is judged to be smaller than the service life threshold value, continuously acquiring the measurement data through a data acquisition module, and evaluating the processing of the residual fatigue life of the bridge crane according to the measurement data.
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| CN118152756A (en) * | 2024-03-21 | 2024-06-07 | 重庆市特种设备检测研究院(重庆市特种设备事故应急调查处理中心) | Crane metal structure safety evaluation method and state monitoring system |
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2022
- 2022-11-23 CN CN202211479071.5A patent/CN115809577A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118152756A (en) * | 2024-03-21 | 2024-06-07 | 重庆市特种设备检测研究院(重庆市特种设备事故应急调查处理中心) | Crane metal structure safety evaluation method and state monitoring system |
| CN118152756B (en) * | 2024-03-21 | 2024-10-18 | 重庆市特种设备检测研究院(重庆市特种设备事故应急调查处理中心) | Crane metal structure safety evaluation method and state monitoring system |
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