CN111259494A - Health monitoring and analyzing method for heavy machine equipment - Google Patents
Health monitoring and analyzing method for heavy machine equipment Download PDFInfo
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- CN111259494A CN111259494A CN202010017887.0A CN202010017887A CN111259494A CN 111259494 A CN111259494 A CN 111259494A CN 202010017887 A CN202010017887 A CN 202010017887A CN 111259494 A CN111259494 A CN 111259494A
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- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
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Abstract
The invention relates to a health monitoring and analyzing method for heavy equipment, which comprises the following steps: determining layout position points of a real sensor according to the key degree and layout requirements of each component of heavy machine equipment; real data acquisition is carried out on the corresponding data acquisition nodes through real sensors, and the measured sensing data are stored in a data storage center; establishing an intelligent virtual main model of the heavy machine equipment based on a physical image model of the heavy machine equipment; determining layout position points of the virtual sensors in the intelligent virtual main model; inputting excitation data obtained by processing the actually measured sensing data into an intelligent virtual main model for virtual simulation, and acquiring real-time parameters corresponding to the layout position points of the virtual sensors; and storing the real-time parameters corresponding to the virtual sensors into a data storage center, carrying out data processing and data analysis on the actual measurement sensing data corresponding to the real sensors and the real-time parameters corresponding to the virtual sensors, and finally outputting analysis results of stress, fatigue life and estimated failure time of the heavy machine equipment structure.
Description
Technical Field
The invention relates to the technical field of health monitoring of heavy-duty machine equipment, in particular to a health monitoring and analyzing method of heavy-duty machine equipment.
Background
At present, the maintenance status of a heavy machine mostly depends on placing actual measurement sensor data of a detection sensor at a key part, data analysis processing and early warning monitoring are carried out through the actual measurement sensor data, the detection sensor is placed at the key part, and the reliability of the key part can be judged only by looking up the sensor data during maintenance and detection. Due to the installation requirement of the sensor, the part without the sensor still depends on manual work, and a plurality of key parts cannot be provided with the sensor; at present, accurate maintenance early warning cannot be carried out on a complete machine of the heavy machine due to the fact that a sensor technology cannot be used for installation and test and the like.
Disclosure of Invention
Aiming at the problems and the defects in the prior art, the invention provides a novel health monitoring and analyzing method for heavy equipment.
The invention solves the technical problems through the following technical scheme:
the invention provides a health monitoring and analyzing method for heavy equipment, which is characterized by comprising the following steps of:
s1, determining layout position points of the real sensors on each component as data acquisition nodes according to the key degree and layout requirements of each component of the heavy machine equipment;
s2, acquiring real data such as force, displacement or acceleration and the like of the corresponding data acquisition node through a real sensor, and storing the measured sensing data in a data storage center;
s3, establishing an intelligent virtual main model of the heavy machine equipment based on the physical image model of the heavy machine equipment;
s4, determining layout position points of the virtual sensors in the intelligent virtual main model;
s5, inputting excitation data obtained by processing the actual measurement sensing data into an intelligent virtual main model for virtual simulation, and acquiring real-time parameters corresponding to the layout position points of the virtual sensors;
and S6, storing the real-time parameters corresponding to the virtual sensors into a data storage center, performing data processing and data analysis on the actual measurement sensing data corresponding to the real sensors and the real-time parameters corresponding to the virtual sensors, and finally outputting analysis results of stress, fatigue life and estimated failure time of the heavy machine equipment structure.
Preferably, in step S3, the smart virtual master model includes a complete machine detail virtual model, a fatigue damage virtual model of a key component, and a device operation load history virtual model.
Preferably, in step S5, the excitation data corresponding to the measured sensing data of a certain key component is input into the fatigue damage virtual model of the key component for virtual simulation, and the real-time parameters corresponding to the layout position points of the virtual sensors on the key component are obtained.
Preferably, in step S5, the excitation data corresponding to the measured sensing data of a certain complete machine detail is input into the complete machine detail virtual model of the complete machine detail for virtual simulation, and the real-time parameters corresponding to the layout position points of the virtual sensors on the complete machine detail are obtained.
Preferably, in step S5, the excitation data corresponding to the actual measurement load data of the device operation is input into the device operation load history virtual model for virtual simulation, and the real-time parameters corresponding to the layout position points of the virtual sensors on the device operation load history virtual model are obtained.
Preferably, the data storage center adopts a cloud data storage center.
On the basis of the common knowledge in the field, the above preferred conditions can be combined randomly to obtain the preferred embodiments of the invention.
The positive progress effects of the invention are as follows:
the invention utilizes the main model construction technology, combines the actual measurement sensing data of the heavy machine equipment structure with the virtual simulation technology, associates the actual measurement sensing data to realize the stress and fatigue life analysis of the heavy machine equipment structure, and evaluates and maintains the health condition of the heavy machine equipment structure through the main model association.
The invention aims at the limitation of the traditional physical sensor monitoring method, utilizes the monitoring method of the associated intelligent main model and the virtual simulation modeling technology, establishes a virtual performance prediction system of the heavy machine structure by processing the measured data, evaluates the fatigue residual life and other technical indexes of the heavy machine equipment, and further researches the design, manufacture and technical transformation of the heavy machine equipment structure.
Drawings
Fig. 1 is a flowchart illustrating a health monitoring and analyzing method for a heavy equipment according to a preferred embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
As shown in fig. 1, the present embodiment provides a method for monitoring and analyzing health of heavy equipment, which includes the following steps:
step 101, determining layout position points of real sensors on each component as data acquisition nodes according to the key degree and layout requirements of each component of heavy machine equipment (such as a port machine, a crane and the like).
And 102, acquiring real data such as force, displacement or acceleration and the like of the corresponding data acquisition node through a real sensor, and storing the actually measured sensing data in a cloud data storage center.
And 103, establishing an intelligent virtual main model of the heavy machine equipment based on the physical image model of the heavy machine equipment.
In step 103, the intelligent virtual main model comprises a complete machine detail virtual model, a fatigue damage virtual model of a key component, and a device operation load history virtual model.
And step 104, determining layout position points of the virtual sensors in the intelligent virtual main model.
And 105, inputting excitation data obtained by processing the actual measurement sensing data into the intelligent virtual main model for virtual simulation, and acquiring real-time parameters corresponding to the layout position points of the virtual sensors.
Specifically, excitation data corresponding to measured sensing data of a certain key component is input into a fatigue damage virtual model of the key component for virtual simulation, and real-time parameters corresponding to layout position points of virtual sensors on the key component are obtained.
And inputting excitation data corresponding to the actually measured sensing data of a certain complete machine detail into a complete machine detail virtual model of the complete machine detail for virtual simulation, and acquiring real-time parameters corresponding to the layout position points of the virtual sensors on the complete machine detail.
And inputting excitation data corresponding to the actual measurement load data of the equipment operation into the equipment operation load process virtual model for virtual simulation, and acquiring real-time parameters corresponding to the layout position points of the virtual sensors on the equipment operation load process virtual model.
And 106, storing the real-time parameters corresponding to the virtual sensors into a cloud data storage center, performing data processing (such as data cleaning and denoising) and data analysis on the actual measurement sensing data corresponding to the real sensors and the real-time parameters corresponding to the virtual sensors, and finally outputting analysis results of stress, fatigue life and predicted failure time of the heavy machine equipment structure, wherein the analysis results are used for guiding design improvement and maintenance service decision.
The invention utilizes the main model construction technology, combines the actual measurement sensing data of the heavy machine equipment structure with the virtual simulation technology, associates the actual measurement sensing data to realize the stress and fatigue life analysis of the heavy machine equipment structure, and evaluates and maintains the health condition of the heavy machine equipment structure through the main model association.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that these are by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.
Claims (6)
1. A health monitoring and analyzing method for heavy machine equipment is characterized by comprising the following steps:
s1, determining layout position points of the real sensors on each component as data acquisition nodes according to the key degree and layout requirements of each component of the heavy machine equipment;
s2, acquiring real data such as force, displacement or acceleration and the like of the corresponding data acquisition node through a real sensor, and storing the measured sensing data in a data storage center;
s3, establishing an intelligent virtual main model of the heavy machine equipment based on the physical image model of the heavy machine equipment;
s4, determining layout position points of the virtual sensors in the intelligent virtual main model;
s5, inputting excitation data obtained by processing the actual measurement sensing data into an intelligent virtual main model for virtual simulation, and acquiring real-time parameters corresponding to the layout position points of the virtual sensors;
and S6, storing the real-time parameters corresponding to the virtual sensors into a data storage center, performing data processing and data analysis on the actual measurement sensing data corresponding to the real sensors and the real-time parameters corresponding to the virtual sensors, and finally outputting analysis results of stress, fatigue life and estimated failure time of the heavy machine equipment structure.
2. The method for health monitoring and analysis of heavy equipment according to claim 1, wherein in step S3, the intelligent virtual main models include a complete machine detail virtual model, a fatigue damage virtual model of key components, and a device operation load history virtual model.
3. The method for monitoring and analyzing the health of heavy machinery equipment according to claim 2, wherein in step S5, the excitation data corresponding to the measured sensing data of a certain key component is input into the virtual fatigue damage model of the key component for virtual simulation, and the real-time parameters corresponding to the layout position points of the virtual sensors on the key component are obtained.
4. The method for monitoring and analyzing the health of the heavy equipment as claimed in claim 2, wherein in step S5, the excitation data corresponding to the measured sensing data of a specific complete machine detail is inputted into the virtual model of the complete machine detail for virtual simulation, and the real-time parameters corresponding to the layout position points of the virtual sensors on the complete machine detail are obtained.
5. The method for monitoring and analyzing the health of heavy equipment as claimed in claim 2, wherein in step S5, the excitation data corresponding to the measured load data of the equipment operation is input into the virtual model of the equipment operation load process for virtual simulation, and the real-time parameters corresponding to the layout position points of the virtual sensors on the virtual model of the equipment operation load process are obtained.
6. The heavy equipment health monitoring and analyzing method of claim 1, wherein the data storage center is a cloud data storage center.
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| CN113405590A (en) * | 2021-05-06 | 2021-09-17 | 中车青岛四方机车车辆股份有限公司 | Device, system and method for testing states of key components of railway vehicle |
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