CN115034117A - Shore bridge metal structure service life prediction system and method based on big data driving - Google Patents
Shore bridge metal structure service life prediction system and method based on big data driving Download PDFInfo
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Abstract
The invention relates to the technical field of life prediction of a shore bridge metal structure, and discloses a life prediction system of a shore bridge metal structure based on big data driving, which comprises: the system comprises a data acquisition module for acquiring state parameter data and structural parameter data of the shore bridge, a numerical model establishing and correcting module for establishing a finite element model of the shore bridge and carrying out training and correction, a data analysis module for processing data and acquiring a load spectrum, a service life prediction module for estimating the residual service life of a metal structure of the shore bridge, and a human-computer interaction module for displaying the finite element model, various parameters and calculation results. This bank bridge metal structure life-span prediction system based on big data drive can solve present bank bridge metal structure's health monitoring and have the characteristics of difficult installation, difficult maintenance, fragile and difficult seizure, considers bank bridge's life cycle health monitoring also less problem simultaneously.
Description
Technical Field
The invention relates to the technical field of life prediction of a shore bridge metal structure, in particular to a life prediction system and method of a shore bridge metal structure based on big data driving.
Background
A shore container crane (shore bridge for short) is a core device for port loading and unloading, and the health state of a metal structure of the shore container crane determines whether the shore bridge can work safely. Because the shore bridge is positioned at the river side or the sea side for a long time, the working environment is complex, the metal structure of the shore bridge is positioned in a salt spray corrosion environment and often bears the effect of alternating load, and the structure is easy to be damaged due to fatigue. Therefore, the estimation of the residual safe service life of the metal structure of the shore bridge is the key for maintaining the healthy and stable operation of the shore bridge equipment and ensuring the safe operation of the port.
At present, health monitoring of a metal structure of a shore bridge has the characteristics of difficulty in installation, maintenance, damage and capture, and less health monitoring of the whole life cycle of the shore bridge is considered.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a shore bridge metal structure service life prediction system and method based on big data driving, which have the advantages of providing a basis for shore bridge metal structure fault diagnosis and health state assessment, and solve the problems that the existing shore bridge metal structure health monitoring has the characteristics of difficult installation, difficult maintenance, easy damage and difficult capture, and the shore bridge whole life cycle health monitoring is less.
(II) technical scheme
In order to achieve the purpose of providing basis for improving the operation safety and reliability, the invention provides the following technical scheme: shore bridge metal structure life prediction system based on big data drive includes: a data acquisition module for acquiring state parameter data and structure parameter data of the shore bridge, a numerical model establishing and correcting module for establishing a finite element model of the shore bridge and carrying out training and correction, a data analysis module for processing data and acquiring a load spectrum, a life prediction module for estimating the residual life of a metal structure of the shore bridge, a human-computer interaction module for displaying the finite element model, various parameters and calculation results, the output end of the data acquisition module is respectively connected with the input ends of the numerical model establishing and correcting module and the human-computer interaction module through leads, the output end of the numerical model establishing and correcting module is connected with the input end of the data analysis module through a lead, the output end of the data analysis module is connected with the input end of the service life prediction module through a lead, the output end of the service life prediction module is connected with the input end of the man-machine interaction module through a wire.
Preferably, the data acquisition module comprises a force sensor, a position sensor, a speed sensor, a motor torque sensor, a current sensor, an accumulated work timer, a wind speed sensor, a vibration sensor and a strain sensor; the position sensor, the speed sensor, the motor torque sensor, the current sensor and the accumulated working timer are used for acquiring operation parameter data of each mechanism of the shore bridge; the force sensor, the vibration sensor and the strain sensor are used for acquiring state monitoring data of the shore bridge structure; the wind speed sensor is used for measuring a working environment; and the vibration sensor and the strain sensor are used for acquiring state parameters of the shore bridge structure.
Preferably, the numerical model establishing and correcting module comprises finite element analysis software and a response surface method training algorithm and is used for establishing a shore bridge finite element model and carrying out training correction, firstly, the finite element analysis software is used for establishing the shore bridge finite element model, secondly, acceleration data acquired by a vibration sensor in the data acquisition module is acquired, the shore bridge finite element model is corrected by using monitoring data and the response surface algorithm, and the corrected accurate shore bridge finite element model is obtained.
Preferably, the data analysis module includes a load spectrum analysis algorithm, and is configured to process data, obtain a load spectrum, obtain strain monitoring data collected by a strain sensor in the data collection module, and process the strain data by using the load spectrum analysis algorithm to obtain the load spectrum.
Preferably, the service life prediction module comprises a CPU processor and a shore bridge metal structure remaining life estimation algorithm, and is configured to estimate the remaining life of the shore bridge metal structure, establish a precise finite element model obtained by the data model and the correction module, calculate the remaining life of the shore bridge structure by using the load spectrum obtained in the data analysis module, and predict the remaining life of the shore bridge by using the structure remaining life estimation algorithm to obtain a remaining life prediction result.
Preferably, the human-computer interaction module comprises a display screen for displaying the finite element model, various parameters and calculation results.
Preferably, the force sensor is used for recording the tensile force of four lifting steel wire ropes, the position sensor is used for recording the lifting height, the working amplitude and the trolley position, the speed sensor is used for recording the lifting speed of the trolley and the travelling speed of the trolley, the motor torque sensor and the current sensor are used for recording the rotating speed and the steering direction of the motor, and the accumulated working timer is used for recording the working history data of the shore bridge.
The method of the shore bridge metal structure service life prediction system based on big data driving comprises the following steps:
s1: the numerical model establishing and correcting module firstly establishes a finite element initial model according to a shore bridge drawing, and when a shore bridge starts to work, the data acquisition module periodically records parameters in the operation process of the shore bridge through a sensor and simultaneously transmits acquired data to the numerical model establishing and correcting module and the data analysis module;
s2: the force sensor records the tension of the four lifting steel wire ropes; the position sensor records the lifting height, the working amplitude and the trolley position; the speed sensor records the lifting speed of the trolley and the travelling speed of the trolley; the motor torque sensor and the current sensor record the rotating speed and the rotating direction of the motor; the accumulated working timer records the working time of the shore bridge; the wind speed sensor measures the working environment; recording working structure parameters of the shore bridge by using a vibration sensor and a strain sensor;
s3: training and correcting the finite element model by adopting a response surface method in combination with historical big data, and simultaneously calculating the stress distribution of the shore bridge; the data analysis module is used for counting the complete cycle of the shore bridge work according to the rotating speed, the rotating direction and the current of the motor in cooperation with the vibration sensor; according to the working cycle of the shore bridge, combining a hoisting load sensor, a torque sensor and a speed sensor, and counting a hoisting load spectrum and load cycle times; estimating/identifying (if a structural vibration sensor is available) working dynamic load and load circulation according to the working circulation of the shore bridge by combining a speed sensor, a load sensor, a torque sensor and a reducer vibration sensor;
s4: according to the working cycle of the shore bridge, a lifting height and a trolley amplitude position sensor are combined to determine the lifting load and the position cycle period of the trolley on the shore bridge; according to the dynamic load, the working position of the trolley and the speed of the trolley, calculating the stress of the whole structure by adopting finite element analysis and combining historical big data analysis, and counting the stress and the cycle times thereof by using a rain flow counting method to obtain a load spectrum;
s5: the information of the data analysis module is transmitted to a life prediction module for predicting the residual life of the shore bridge structure and evaluating the health condition of the shore bridge; and information and data of the numerical model establishing and correcting module and the service life predicting module are transmitted to the man-machine interaction module, and results of the shore bridge model, the stress state, the load spectrum historical data, the record, the residual service life estimation and the like are displayed to a user in a graphical interface mode.
(III) advantageous effects
Compared with the prior art, the invention provides a shore bridge metal structure service life prediction system and method based on big data driving, and the system and method have the following beneficial effects:
1. according to the shore bridge metal structure service life prediction system and method based on the big data drive, the shore bridge finite element model is corrected based on the historical big data drive, and the whole fatigue aging process of the shore bridge can be tracked.
2. According to the shore bridge metal structure service life prediction system and method based on big data driving, the data analysis module analyzes the load spectrum based on historical data and data measured in real time during working of the shore bridge, and the fatigue life of the shore bridge can be estimated more accurately.
3. According to the shore bridge metal structure service life prediction system and method based on big data driving, the human-computer interaction module adopts a graphic display method, the system is very specific and visual, the automation degree is high, and the convenience degree of shore bridge maintenance is improved
Drawings
FIG. 1 is a general scheme diagram of a shore bridge metal structure life prediction system based on big data driving, which is provided by the invention;
FIG. 2 is a schematic diagram of a sensor arrangement of a shore bridge metal structure life prediction system based on big data driving, which is provided by the invention;
fig. 3 is a load spectrum acquisition flow chart of the shore bridge metal structure life prediction system based on big data driving provided by the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to fig. 1-3, a life prediction system for a shore bridge metal structure based on big data driving includes: the system comprises a data acquisition module, a numerical model establishing and correcting module, a data analysis module, a service life prediction module and a man-machine interaction module; the data acquisition module comprises a force sensor, a position sensor, a speed sensor, a motor torque sensor, a current sensor, an accumulated work timer, a wind speed sensor, a vibration sensor and a strain sensor; the position sensor, the speed sensor, the motor torque sensor, the current sensor and the accumulated working timer are used for acquiring operation parameter data of each mechanism of the shore bridge; the force sensor, the vibration sensor and the strain sensor are used for acquiring state monitoring data of the shore bridge structure; the wind speed sensor is used for measuring a working environment; the vibration sensor and the strain sensor are used for acquiring state parameters of the shore bridge structure; the force sensor is used for recording the tension of four lifting steel wire ropes, the position sensor is used for recording the lifting height and the working amplitude-trolley position, the speed sensor is used for recording the lifting speed and the walking speed of the trolley, the motor torque sensor and the current sensor are used for recording the rotating speed and the steering direction of the motor, and the accumulated working timer is used for recording the historical data of the shore bridge.
The numerical model establishing and correcting module comprises finite element analysis software and a response surface method training algorithm and is used for establishing a shore bridge finite element model and carrying out training correction, firstly, the finite element analysis software is used for establishing the finite element model of the shore bridge, secondly, acceleration data acquired by a vibration sensor in the data acquisition module is acquired, the shore bridge finite element model is corrected by combining monitoring data and the response surface algorithm, and the corrected accurate shore bridge finite element model is obtained.
The data analysis module comprises a load spectrum analysis algorithm and is used for processing data, acquiring a load spectrum, acquiring strain monitoring data acquired by a strain sensor in the data acquisition module, and processing the strain data by using the load spectrum analysis algorithm to obtain a load spectrum; the service life prediction module comprises a CPU (central processing unit) and a shore bridge metal structure residual life estimation algorithm and is used for estimating the residual life of the shore bridge metal structure, establishing and correcting a precise finite element model obtained by the module by using a data model, calculating the residual life of the shore bridge structure by combining a load spectrum obtained in the data analysis module, and predicting the residual life of the shore bridge by using the structure residual life estimation algorithm to obtain a residual life prediction result; the man-machine interaction module comprises a display screen and is used for displaying the finite element model, various parameters and a calculation result.
The output end of the data acquisition module is respectively connected with the input ends of the numerical model establishing and correcting module and the human-computer interaction module through leads, the output end of the numerical model establishing and correcting module is connected with the input end of the data analysis module through leads, the output end of the data analysis module is connected with the input end of the service life prediction module through leads, and the output end of the service life prediction module is connected with the input end of the human-computer interaction module through leads.
The method of the shore bridge metal structure service life prediction system based on big data driving comprises the following steps:
s1: the numerical model establishing and correcting module firstly establishes a finite element initial model according to a shore bridge drawing, and when a shore bridge starts to work, the data acquisition module periodically records parameters in the operation process of the shore bridge through a sensor and simultaneously transmits acquired data to the numerical model establishing and correcting module and the data analysis module;
s2: the force sensor records the tension of the four lifting steel wire ropes; the position sensor records the lifting height, the working amplitude and the trolley position; the speed sensor records the lifting speed of the trolley and the travelling speed of the trolley; the motor torque sensor and the current sensor record the rotating speed and the steering of the motor; the accumulated working timer records the working time of the shore bridge; the wind speed sensor measures the working environment; recording the working structure parameters of the shore bridge by using the vibration sensor and the strain sensor;
s3: training and correcting the finite element model by adopting a response surface method in combination with historical big data, and simultaneously calculating the stress distribution of the shore bridge; the data analysis module is matched with a vibration sensor according to the rotating speed, the rotating direction and the current of the motor to count the complete cycle of the shore bridge work; according to the working cycle of the shore bridge, combining hoisting load, torque and speed sensors, and counting a hoisting load spectrum and load cycle times; estimating/identifying (if a structural vibration sensor is available) working dynamic load and load circulation according to the working circulation of the shore bridge by combining a speed sensor, a load sensor, a torque sensor and a reducer vibration sensor;
s4: according to the working cycle of the shore bridge, a lifting height and a trolley amplitude position sensor are combined to determine the lifting load and the position cycle period of the trolley on the shore bridge; according to the dynamic load, the working position of the trolley and the speed of the trolley, calculating the stress of the whole structure by adopting finite element analysis and combining historical big data analysis, and counting the stress and the cycle times thereof by using a rain flow counting method to obtain a load spectrum;
s5: the information of the data analysis module is transmitted to a life prediction module for predicting the residual life of the shore bridge structure and evaluating the health condition of the shore bridge; and information and data of the numerical model establishing and correcting module and the service life predicting module are transmitted to the man-machine interaction module, and results of the shore bridge model, the stress state, the load spectrum historical data, the record, the residual service life estimation and the like are displayed to a user in a graphical interface mode.
Finally, the service life of the shore bridge metal structure based on big data is predicted, and the service life is visually displayed to an operator, so that safe and reliable operation of the shore bridge is guaranteed.
In conclusion, the shore bridge metal structure service life prediction system and method based on big data drive can correct a shore bridge finite element model based on historical big data drive, and can track the whole fatigue aging process of the shore bridge; the data analysis module analyzes the load spectrum based on historical data and data measured in real time during the operation of the shore bridge, so that the fatigue life of the shore bridge can be estimated more accurately; the man-machine interaction module adopts a graphic display method, is very specific and visual, has high automation degree, and improves the convenience degree of shore bridge maintenance.
It is to be noted that the term "comprises," "comprising," or any other variation thereof is intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. Shore bridge metal structure life prediction system based on big data drive, its characterized in that: the method comprises the following steps: a data acquisition module for acquiring state parameter data and structure parameter data of the quayside container crane, a numerical model establishing and correcting module for establishing a finite element model of the quayside container crane and carrying out training and correction, a data analysis module for processing data and acquiring a load spectrum, a service life prediction module for estimating the residual service life of the metal structure of the quayside container crane, a man-machine interaction module for displaying the finite element model, various parameters and calculation results, the output end of the data acquisition module is respectively connected with the input ends of the numerical model establishing and correcting module and the human-computer interaction module through leads, the output end of the numerical model establishing and correcting module is connected with the input end of the data analysis module through a lead, the output end of the data analysis module is connected with the input end of the service life prediction module through a lead, and the output end of the service life prediction module is connected with the input end of the human-computer interaction module through a lead.
2. The big data drive-based shore bridge metal structure life prediction system according to claim 1, wherein: the data acquisition module comprises a force sensor, a position sensor, a speed sensor, a motor torque sensor, a current sensor, an accumulated work timer, a wind speed sensor, a vibration sensor and a strain sensor;
the position sensor, the speed sensor, the motor torque sensor, the current sensor and the accumulated working timer are used for acquiring operation parameter data of each mechanism of the shore bridge;
the force sensor, the vibration sensor and the strain sensor are used for acquiring state monitoring data of the shore bridge structure;
the wind speed sensor is used for measuring a working environment;
the vibration sensor and the strain sensor are used for acquiring state parameters of the shore bridge structure.
3. The big data drive-based shore bridge metal structure life prediction system according to claim 1 or 2, wherein: the numerical model establishing and correcting module comprises finite element analysis software and a response surface method training algorithm and is used for establishing a shore bridge finite element model and carrying out training correction, firstly, the finite element analysis software is used for establishing the finite element model of the shore bridge, secondly, acceleration data acquired by a vibration sensor in the data acquisition module is acquired, the shore bridge finite element model is corrected by combining monitoring data and the response surface algorithm, and the corrected accurate shore bridge finite element model is obtained.
4. The big data drive-based shore bridge metal structure life prediction system according to claim 1 or 2, wherein: the data analysis module comprises a load spectrum analysis algorithm and is used for processing data, acquiring a load spectrum, acquiring strain monitoring data acquired by a strain sensor in the data acquisition module, and processing the strain data by using the load spectrum analysis algorithm to obtain a load spectrum.
5. The big data drive-based shore bridge metal structure life prediction system according to claim 1, wherein: the service life prediction module comprises a CPU (central processing unit) and a shore bridge metal structure residual life estimation algorithm and is used for estimating the residual life of the shore bridge metal structure, establishing and correcting an accurate finite element model obtained by the module by using a data model, calculating the residual life of the shore bridge structure by combining a load spectrum obtained in the data analysis module, and predicting the residual life of the shore bridge by using the structure residual life estimation algorithm to obtain a residual life prediction result.
6. The big data drive-based shore bridge metal structure life prediction system according to claim 1, wherein: the man-machine interaction module comprises a display screen and is used for displaying the finite element model, various parameters and calculation results.
7. The big data drive-based shore bridge metal structure life prediction system according to claim 1 or 2, wherein: the force sensor is used for recording the tensile force of four lifting steel wire ropes, the position sensor is used for recording the lifting height, the working amplitude and the trolley position, the speed sensor is used for recording the lifting speed of the trolley and the travelling speed of the trolley, the motor torque sensor and the current sensor are used for recording the rotating speed and the steering direction of the motor, and the accumulated working timer is used for recording the working history data of the shore bridge.
8. A method provided with the big data drive based shore bridge metal structure life prediction system of any one of claims 1 to 7, characterized by comprising the following steps:
s1: the numerical model establishing and correcting module firstly establishes a finite element initial model according to a shore bridge drawing, and when a shore bridge starts to work, the data acquisition module periodically records parameters in the operation process of the shore bridge through a sensor and simultaneously transmits acquired data to the numerical model establishing and correcting module and the data analysis module;
s2: the force sensor records the tension of the four lifting steel wire ropes; the position sensor records the lifting height, the working amplitude and the trolley position; the speed sensor records the lifting speed and the walking speed of the trolley; the motor torque sensor and the current sensor record the rotating speed and the steering of the motor; the accumulated working timer records the working time of the shore bridge; the wind speed sensor measures the working environment; recording working structure parameters of the shore bridge by using a vibration sensor and a strain sensor;
s3: training and correcting the finite element model by adopting a response surface method in combination with historical big data, and simultaneously calculating the stress distribution of the shore bridge; the data analysis module is used for counting the complete cycle of the shore bridge work according to the rotating speed, the rotating direction and the current of the motor in cooperation with the vibration sensor; according to the working cycle of the shore bridge, combining hoisting load, torque and speed sensors, and counting a hoisting load spectrum and load cycle times; estimating/identifying (if a structural vibration sensor is available) working dynamic load and load circulation according to the working circulation of the shore bridge by combining a speed sensor, a load sensor, a torque sensor and a reducer vibration sensor;
s4: according to the working cycle of the shore bridge, a lifting height and a trolley amplitude position sensor are combined to determine the lifting load and the position cycle period of the trolley on the shore bridge; according to the dynamic load, the working position of the trolley and the speed of the trolley, adopting finite element analysis and combining historical big data analysis to calculate the stress of the whole structure, and counting the stress and the cycle times thereof by a rain flow counting method to obtain a load spectrum;
s5: the information of the data analysis module is transmitted to a life prediction module for predicting the residual life of the shore bridge structure and evaluating the health condition of the shore bridge; and information and data of the numerical model establishing and correcting module and the service life predicting module are transmitted to the man-machine interaction module, and results of the shore bridge model, the stress state, the load spectrum historical data, the record, the residual service life estimation and the like are displayed to a user in a graphical interface mode.
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