CN116629002A - Load spectrum compression and life prediction method and system for structural member of excavator - Google Patents
Load spectrum compression and life prediction method and system for structural member of excavator Download PDFInfo
- Publication number
- CN116629002A CN116629002A CN202310624705.XA CN202310624705A CN116629002A CN 116629002 A CN116629002 A CN 116629002A CN 202310624705 A CN202310624705 A CN 202310624705A CN 116629002 A CN116629002 A CN 116629002A
- Authority
- CN
- China
- Prior art keywords
- data
- excavator
- curve
- damage
- load spectrum
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 49
- 238000001228 spectrum Methods 0.000 title claims abstract description 47
- 238000007906 compression Methods 0.000 title claims abstract description 27
- 230000006835 compression Effects 0.000 title claims abstract description 27
- 238000001914 filtration Methods 0.000 claims abstract description 9
- 238000005336 cracking Methods 0.000 claims description 27
- 238000003860 storage Methods 0.000 claims description 11
- 238000012937 correction Methods 0.000 claims description 10
- 238000012423 maintenance Methods 0.000 claims description 9
- 238000012360 testing method Methods 0.000 claims description 9
- 238000005070 sampling Methods 0.000 claims description 8
- 230000001186 cumulative effect Effects 0.000 claims description 3
- 238000009661 fatigue test Methods 0.000 claims description 3
- 230000005540 biological transmission Effects 0.000 abstract description 6
- 238000005516 engineering process Methods 0.000 abstract description 4
- 238000005259 measurement Methods 0.000 abstract description 4
- 238000010586 diagram Methods 0.000 description 10
- 238000004590 computer program Methods 0.000 description 7
- 238000000605 extraction Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000013144 data compression Methods 0.000 description 2
- 238000013213 extrapolation Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000001066 destructive effect Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000011089 mechanical engineering Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- 230000008054 signal transmission Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M17/00—Testing of vehicles
- G01M17/007—Wheeled or endless-tracked vehicles
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- H—ELECTRICITY
- H03—ELECTRONIC CIRCUITRY
- H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
- H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same, similar or subset of information is represented by a different sequence or number of digits
- H03M7/30—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/04—Ageing analysis or optimisation against ageing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Geometry (AREA)
- Computer Hardware Design (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
The application provides a load spectrum compression and life prediction method and a system for an excavator structural member, which are used for realizing life prediction of the excavator structural member. A load spectrum compression method for an excavator structural part comprises the following steps: acquiring load spectrum original data; removing burrs of the acquired signals and small jitter signals through low-pass filtering according to the load spectrum original data to obtain low-pass filtered data; and extracting the characteristic value as compressed characteristic data based on the low-pass filtered data. The application provides an actual measurement load spectrum compression technology of an excavator structural part, which can realize large-amplitude compression of load spectrum data and is convenient for data transmission through GPS.
Description
Technical Field
The application relates to the technical field of mechanical engineering, in particular to a load spectrum compression and service life prediction method and system for an excavator structural part.
Background
The excavator is used as engineering machinery, most of the excavators have severe working environments, fatigue loss of structural members can be accelerated due to high-strength work, and damage to the structural members can be accelerated due to misoperation of operators. The structural member of the excavator breaks down mostly, especially the structural member breaks down more than 90% of the large-scale key structural members such as a working device, a turntable, a frame and the like, the breaking position is also relatively fixed, and once the structural member breaks down, construction is interrupted, and casualties are also possibly caused. However, how to accurately predict the life of an excavator structural member is a problem to be solved in the industry. ( Fatigue life: the number of stress cycles a material experiences before fatigue failure is referred to as the fatigue life. )
In the prior art, the fatigue life of the structural member of the excavator is predicted by a method combining simulation and test, and the load spectrum is obtained by a method of partial test and reverse thrust. The existing load spectrum acquisition is to acquire load spectrum of a specific working condition for a period of time, and load spectrum data of the whole life cycle is obtained through a load spectrum extrapolation method. Because the structural parts of the excavator are mostly welded structures, the fatigue characteristic curves of S-N curves of the welded structures are obtained through standard sample tests and are different from actual structures.
The prior art has the following disadvantages:
(1) In the prior art, most of data transmission of the excavator is to transmit signal transmission data through a GPS, and a large amount of data transmission cannot be performed;
(2) The prior art is mainly a method for predicting the fatigue life of the structural member of the excavator by combining simulation and test, and the load spectrum is obtained by a partial test and reverse thrust method and is different from the real load spectrum of a real vehicle.
(3) The existing load spectrum acquisition is to acquire load spectrum under a specific working condition for a period of time, and the service life prediction is carried out by a load spectrum extrapolation method, so that great errors exist.
(4) The fatigue characteristic curve of the S-N curve of the structural member of the excavator is obtained through a standard sample test and is different from the actual structure.
Disclosure of Invention
The application aims to overcome the defects in the prior art and provides a load spectrum compression and life prediction method and a system for an excavator structural member, which are used for predicting the life of the excavator structural member.
In order to achieve the above purpose, the application is realized by adopting the following technical scheme:
in a first aspect, the application provides a load spectrum compression method for an excavator structural member, comprising the following steps:
acquiring load spectrum original data;
removing burrs of the acquired signals and small jitter signals through low-pass filtering according to the load spectrum original data to obtain low-pass filtered data;
and extracting the characteristic value as compressed characteristic data based on the low-pass filtered data.
Further, extracting the feature value as compressed feature data based on the low-pass filtered data includes:
the data after low-pass filtering is a continuous broken line formed by connecting a series of sampling points, and turning points with positive and negative changes of slopes in the broken line are extracted as characteristic points;
the amplitude difference of two adjacent characteristic points in the extracted characteristic points is smaller than sigma min Point removal of (i) i.e. |sigma k-2 -σ k-3 |＜σ min When delete sigma k-2 Sum sigma k-2 . Wherein sigma min Is the minimum nondestructive stress value defined according to the material fatigue characteristic curve.
Further, the method for extracting turning points with positive and negative changes of slopes in the broken lines as characteristic points comprises the following steps:
the amplitudes of the adjacent three sampling points satisfy (σ j+1 -σ j )×(σ j+2 -σ j+1 ) When < 0, i.e. line sigma j σ j+1 Sum sigma j+1 σ j+2 When the slopes are opposite (when the slopes of the first two points are opposite to those of the second two points), the second point sigma is reserved j+1 And synchronously storing stress data corresponding to the ordinate and time data corresponding to the abscissa at the feature points as the feature points.
In a second aspect, the present application provides a method for predicting the life of an excavator structural member, comprising the steps of:
step S10: and obtaining stress information of the structural member of the excavator, and taking the stress information as load spectrum original data of life prediction.
Step S20: according to the load spectrum original data, executing the load spectrum compression method of the structural member of the excavator according to the first aspect to obtain compression characteristic data required by life prediction of the structural member of the excavator;
step S30: transmitting the compressed characteristic data to a total server through a GPS;
step S40: performing rain flow counting on the extracted compression characteristic data to obtain stress cycle counting;
step S50: defining an S-N curve of a structure of a part to be detected;
step S60: calculating a damage value of the part to be detected based on an S-N curve of the structure of the part to be detected by calculating an accumulated damage theory, and marking the damage value as DA;
step S70: acquiring and correcting the S-N curve according to the historical fault data of the part to be detected to obtain a corrected S-N curve;
step S80: and calculating the damage value of the to-be-measured part according to the corrected S-N curve of the to-be-measured part and the accumulated damage theory, and predicting the service life of the to-be-measured part to obtain the residual service life of the to-be-measured part.
Further, in step S50, the S-N curve is obtained by performing a fatigue test on the welded joint having the same structure as the cut-out portion to be measured.
Further, in step S60, calculating a damage value DA of the part to be detected by using the accumulated damage theory, including; according to the Miner cumulative damage theory, under the action of a single constant stress load, the damage D is defined as:
D＝n/N
wherein n is the cycle number of the constant amplitude load; n is the fatigue life corresponding to stress level S.
Assuming stress amplitude sigma i Action n i Second, the number of cycles to failure of the material at this stress level is N i The fatigue damage to the structure caused by the partial stress cycle is n i /N i The total damage D is the damage sum of stress amplitude of each stage, namely:
wherein n is i Is the actual number of cycles at the i-th level stress magnitude; n (N) i The allowable cycle times when the fatigue failure is achieved under the ith stress amplitude are shown and are checked by an S-N curve; and calculating the damage value DA of the part to be measured according to the accumulated damage theory calculated by the formula.
Further, in step S70, the S-N curve is corrected according to the historical fault data of the part to be measured, so as to obtain a corrected S-N curve, which includes:
acquiring feedback market cracking faults and fault time of a part to be detected, retrieving all compression characteristic data corresponding to test data of the part, intercepting all compression characteristic data before the cracking time, repeating the steps S40, S50 and S60, and marking the calculated damage value as DB;
correction coefficient k 1 ＝1/DB；
Counting all cracking faults of the market at the position, and repeating the steps to calculate a correction coefficient k of the position of the nth trolley i ；
S-N curve correction coefficient of the part
Correcting three parameters of the S-N curve, S' 1 ＝k×S 1 ，S′ 2 ＝k×S 2 ，S′ 3 ＝k×S 3 The S-N curve is re-fitted.
Further, in step S80, calculating a damage value of the to-be-measured part according to the corrected S-N curve of the to-be-measured part and the accumulated damage theory, and performing life prediction, including the following steps:
predicting the residual life of a part to be tested: t= (1-DA) ×t acg
Wherein T is the residual life of the part to be measured, T acg Is the average cracking failure time for that location.
Further, the method further comprises: step S90: predicting risks of the structural member according to the following judging method through fatigue life prediction in the real-time predicting process of the life of the structural member of the excavator;
the damage value is 0.7 as a yellow early warning limit value, and when DA is more than 0.7 and less than 0.9, the early warning state is yellow early warning;
the damage value is 0.9 as a red early warning limit value, and when DA is more than 0.9, the early warning state is red early warning;
when the early warning state is red early warning, pushing maintenance prompts to product service personnel through WeChat, short message and mobile phone APP, checking the part by the service personnel on site, judging whether the structure has cracking or not through checking, and if the structure has cracking, maintaining or replacing accessories of the part according to maintenance protocol;
if no cracking fault occurs, changing the early warning state into red early warning when the damage value DA is increased by 0.1, repeating the steps to perform field maintenance until the part is cracked, registering the occurrence time of the cracking fault, and updating and recording the cracking fault time.
In a third aspect, the present application provides a life prediction system for an excavator structural member, comprising a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to the second aspect.
Compared with the prior art, the application has the beneficial effects that:
(1) The application provides an actual measurement load spectrum compression technology for an excavator structural part, which can realize large-amplitude compression of load spectrum data, is convenient for data transmission, storage, analysis and distributed prediction, and is convenient for sending to a server for distributed computation.
(2) The application provides a method for predicting the service life of an excavator structural member, which comprises an excavator structural member actual measurement load spectrum compression technology and an excavator structural member service life prediction method, and can realize accurate prediction of the service life of the excavator structural member. Meanwhile, the service life of the structural member can be early warned, and service personnel can conveniently maintain the structural member on site.
(3) The application revises the S-N curve of the structural member of the excavator and can be used for specifying new product design.
Drawings
FIG. 1 is a diagram of a load spectrum compression and life prediction method for an excavator structural member;
FIG. 2 is a load spectrum compression flow diagram;
FIG. 3 is a diagram of a feature value extraction step;
FIG. 4 is a second diagram of a feature value extraction step;
FIG. 5 is a feature value extraction flow chart;
FIG. 6 is an S-N graph;
FIG. 7 is a graph of S-N curve correction;
fig. 8 is a structural member life warning chart.
Detailed Description
The application is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present application, and are not intended to limit the scope of the present application.
Embodiment one:
the embodiment provides a load spectrum compression method for an excavator structural part, which comprises the following steps:
acquiring load spectrum original data;
removing burrs of the acquired signals and small jitter signals through low-pass filtering according to the load spectrum original data to obtain low-pass filtered data;
and extracting the characteristic value as compressed characteristic data based on the low-pass filtered data.
Extracting the feature value as compressed feature data based on the low-pass filtered data, comprising:
the data after low-pass filtering is a continuous broken line formed by connecting a series of sampling points, and turning points with positive and negative changes of slopes in the broken line are extracted as characteristic points;
the amplitude difference of two adjacent characteristic points in the extracted characteristic points is smaller than sigma min Point removal of (i) i.e. |sigma k-2 -σ k-3 |＜σ min When delete sigma k-2 Sum sigma k-3 . Wherein sigma min Is the minimum nondestructive stress value defined according to the material fatigue characteristic curve.
Extracting turning points of positive and negative changes of slopes in the broken lines as characteristic points, wherein the method comprises the following steps:
the amplitudes of the adjacent three sampling points satisfy (σ j+1 -σ j )×(σ j+2 -σ j+1 ) When < 0, i.e. line sigma j σ j+1 Sum sigma j+1 σ j+2 When the slopes are opposite (when the slopes of the first two points are opposite to those of the second two points), the second point sigma is reserved j+1 And synchronously storing stress data corresponding to the ordinate and time data corresponding to the abscissa at the feature points as the feature points.
According to the actual measurement load spectrum compression technology for the structural part of the excavator, the load spectrum data can be compressed greatly, data transmission, storage, analysis and distributed prediction are facilitated, and the data is conveniently sent to a server for distributed calculation.
Embodiment two:
the embodiment provides a service life prediction method for an excavator structural member, which comprises the following steps:
step S10: strain signals of at least one measuring point of the structural member of the excavator in three directions are collected, and stress at the measuring point is obtained through calculation of strain data; the stress signal is used as load spectrum original data of life prediction.
Because most of data transmission of the excavator is that data is transmitted by a GPS (global positioning system) transmitting signal, a large amount of data cannot be transmitted, the application provides a load spectrum data compression method for predicting the service life of an excavator structural member, which aims at the problem:
step S20: the data compression flow is shown in fig. 2, firstly, burrs of the collected signals and small shaking signals are removed through low-pass filtering, and further characteristic values are extracted to be used as data required for predicting the service life of the structural part of the excavator.
The feature value extraction method is shown in fig. 3 and 4.
The first step, as shown in fig. 3, the data after low-pass filtering is a continuous broken line formed by connecting a series of sampling points, and turning points with positive and negative changes of slopes in the broken line are extracted as characteristic points; the amplitudes of the adjacent three sampling points satisfy (σ j+1 -σ j )×(σ j+2 -σ j+1 ) When < 0, i.e. line sigma j σ j+1 Sum sigma j+1 σ j+2 When the slopes are opposite, then sigma is retained j+1 As feature points. Stress data corresponding to the ordinate at the characteristic point and time data corresponding to the abscissa are synchronously stored.
Second, as shown in FIG. 5, the amplitude difference < sigma between two adjacent feature points in the extracted feature points min Point removal of (i) i.e. |sigma k-2 -σ k-3 |＜σ min Delete sigma k-2 Sum sigma k-3 . Wherein sigma min Is the minimum non-destructive stress value defined from the material fatigue characteristic curve, as shown in fig. 6.
Step S30: and transmitting the extracted characteristic values to a total server through a GPS.
Step S40: and performing rain flow counting on the extracted characteristic data. And taking the compressed characteristic data as a load spectrum of the time period, and counting the rain flow to obtain a stress cycle count for fatigue analysis.
Step S50: defining an S-N curve of the structure of the part to be tested, wherein the S-N curve is obtained by performing fatigue test on a welded joint with the same structure as the intercepted part to be tested.
Step S60: and calculating the damage value of the part to be detected by adopting the accumulated damage theory, and marking the damage value as DA.
Step S70: and correcting the S-N curve.
According to the feedback market cracking fault and fault time of the part to be detected, all characteristic value data corresponding to the test data of the part are fetched, all characteristic data before the cracking time are intercepted, the steps S40, S50 and S60 are repeated, and the calculated damage value is recorded as DB;
correction coefficient k 1 ＝1/DB；
Counting all cracking faults of the market at the position, and repeating the steps to calculate a correction coefficient k of the position of the nth trolley i ；
S-N curve correction coefficient of the part
Correcting three parameters of the S-N curve, S' 1 ＝k×S 1 ，S′ 2 ＝k×S 2 ，S′ 3 ＝k×S 3 The S-N curve is re-fitted as shown in FIG. 7.
Step S80: life prediction of a part to be measured
Predicting the residual life of a part to be tested: t= (1-DA) ×t acg
Wherein T is the residual life of the part to be measured, T acg Is the average cracking failure time for that location.
Step S90: predicting risks of the structural member according to the following judging method through fatigue life prediction in the real-time predicting process of the life of the structural member of the excavator;
the damage value is 0.7 as a yellow early warning limit value, and when DA is more than 0.7 and less than 0.9, the early warning state is yellow early warning;
the damage value is 0.9 as a red early warning limit value, and when DA is more than 0.9, the early warning state is red early warning;
when the early warning state is red early warning, pushing maintenance prompts to product service personnel through WeChat, short message and mobile phone APP, checking the part by the service personnel on site, judging whether the structure has cracking or not through checking, and if the structure has cracking, maintaining or replacing accessories of the part according to maintenance protocol; if no cracking fault occurs, changing the early warning state to red early warning when the damage value DA is increased by 0.1, repeating the steps to perform field maintenance until the part is cracked, registering the occurrence time of the cracking fault, and updating the cracking fault time to the system.
Embodiment III:
the embodiment provides a life prediction system of an excavator structural part, which comprises a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to embodiment two.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is merely a preferred embodiment of the present application, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present application, and such modifications and variations should also be regarded as being within the scope of the application.
Claims (10)
1. The load spectrum compression method for the structural part of the excavator is characterized by comprising the following steps of:
acquiring load spectrum original data;
removing burrs of the acquired signals and small jitter signals through low-pass filtering according to the load spectrum original data to obtain low-pass filtered data;
and extracting the characteristic value as compressed characteristic data based on the low-pass filtered data.
2. The method of compressing a load spectrum of an excavator structural member according to claim 1, wherein extracting a characteristic value as compressed characteristic data based on low-pass filtered data comprises:
the data after low-pass filtering is a continuous broken line formed by connecting a series of sampling points, and turning points with positive and negative changes of slopes in the broken line are extracted as characteristic points;
the amplitude difference of two adjacent characteristic points in the extracted characteristic points is smaller than sigma min Point removal of (i) i.e. |sigma k-2 -σ k-3 |＜σ min When delete sigma k-2 Sum sigma k-3 The method comprises the steps of carrying out a first treatment on the surface of the Wherein sigma min Is the minimum nondestructive stress value defined according to the material fatigue characteristic curve.
3. The method of compressing a load spectrum of an excavator structural member according to claim 2, wherein extracting turning points of positive and negative changes of slopes in a fold line as characteristic points comprises:
the amplitudes of the adjacent three sampling points satisfy (σ j+1 -σ j )×(σ j+2 -σ j+1 ) When < 0, i.e. the slope of the connecting line of the first two points is opposite to that of the second two points, the second point sigma is reserved j+1 And synchronously storing stress data corresponding to the ordinate and time data corresponding to the abscissa at the feature points as the feature points.
4. The method for predicting the service life of the structural part of the excavator is characterized by comprising the following steps of:
step S10: stress information of the structural part of the excavator is obtained, and the stress information is used as load spectrum original data of life prediction;
step S20: according to the load spectrum original data, executing the load spectrum compression method of the excavator structural part according to any one of claims 1-3 to obtain compression characteristic data required by the life prediction of the excavator structural part;
step S30: transmitting the compressed characteristic data to a total server through a GPS;
step S40: performing rain flow counting on the extracted compression characteristic data to obtain stress cycle counting;
step S50: defining an S-N curve of a structure of a part to be detected;
step S60: calculating a damage value of the part to be detected based on an S-N curve of the structure of the part to be detected by calculating an accumulated damage theory, and marking the damage value as DA;
step S70: acquiring and correcting the S-N curve according to the historical fault data of the part to be detected to obtain a corrected S-N curve;
step S80: and calculating the damage value of the to-be-measured part according to the corrected S-N curve of the to-be-measured part and the accumulated damage theory, and predicting the service life of the to-be-measured part to obtain the residual service life of the to-be-measured part.
5. The method according to claim 4, wherein in step S50, the S-N curve is obtained by performing a fatigue test on a welded joint having the same structure as the cut-out portion to be measured.
6. The method according to claim 4, wherein in step S60, the damage value DA of the part to be measured is calculated using the accumulated damage theory, including;
according to the Miner cumulative damage theory, under the action of a single constant stress load, the damage D is defined as:
D＝n/N
wherein n is the cycle number of the constant amplitude load; n is the fatigue life corresponding to stress level S;
assuming stress amplitude sigma i Action n i Second, the number of cycles to failure of the material at this stress level is N i The fatigue damage to the structure caused by the partial stress cycle is n i /N i The total damage D is the damage sum of stress amplitude of each stage, namely:
wherein n is i Is the actual number of cycles at the i-th level stress magnitude; n (N) i Represents the number of cycles allowed to reach fatigue failure at the i-th stress magnitude, D i Representing damage under the i-th level stress amplitude, and checking by an S-N curve; and calculating the damage value DA of the part to be detected according to the value of D calculated by the formula as a cumulative damage theory.
7. The method according to claim 4, wherein in step S70, the S-N curve is corrected according to the historical fault data of the part to be measured, and the corrected S-N curve is obtained, including:
acquiring feedback market cracking faults and fault time of a part to be detected, retrieving all compression characteristic data corresponding to test data of the part, intercepting all compression characteristic data before the cracking time, repeating the steps S40, S50 and S60, and marking the calculated damage value as DB;
correction coefficient k 1 ＝1/DB；
Counting all cracking faults of the market at the position, and repeating the steps to calculate a correction coefficient k of the position of the nth trolley i ；
S-N curve correction coefficient of the part
Correcting three parameters of the S-N curve, S' 1 ＝k×S 1 ，S′ 2 ＝k×S 2 ，S′ 3 ＝k×S 3 The S-N curve is re-fitted.
8. The method according to claim 4, wherein in step S80, the damage value of the part to be measured is calculated according to the corrected S-N curve of the part to be measured and the accumulated damage theory to predict the life, and the method comprises the steps of:
predicting the residual life of a part to be tested: t= (1-DA) ×t acg
Wherein T is the residual life of the part to be measured, T acg Is the average cracking failure time for that location.
9. The method for predicting the life of an excavator structural member of claim 4 wherein the method further comprises: step S90: predicting risks of the structural member according to the following judging method through fatigue life prediction in the real-time predicting process of the life of the structural member of the excavator;
the damage value is 0.7 as a yellow early warning limit value, and when DA is more than 0.7 and less than 0.9, the early warning state is yellow early warning;
the damage value is 0.9 as a red early warning limit value, and when DA is more than 0.9, the early warning state is red early warning;
when the early warning state is red early warning, pushing maintenance prompts to product service personnel through WeChat, short message and mobile phone APP, checking the part by the service personnel on site, judging whether the structure has cracking or not through checking, and if the structure has cracking, maintaining or replacing accessories of the part according to maintenance protocol;
if no cracking fault occurs, changing the early warning state into red early warning when the damage value DA is increased by 0.1, repeating the steps to perform field maintenance until the part is cracked, registering the occurrence time of the cracking fault, and updating and recording the cracking fault time.
10. The life prediction system for the excavator structural part is characterized by comprising a processor and a storage medium;
the storage medium is used for storing instructions;
the processor being operative according to the instructions to perform the steps of the method according to any one of claims 4-9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310624705.XA CN116629002A (en) | 2023-05-30 | 2023-05-30 | Load spectrum compression and life prediction method and system for structural member of excavator |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310624705.XA CN116629002A (en) | 2023-05-30 | 2023-05-30 | Load spectrum compression and life prediction method and system for structural member of excavator |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116629002A true CN116629002A (en) | 2023-08-22 |
Family
ID=87591732
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310624705.XA Pending CN116629002A (en) | 2023-05-30 | 2023-05-30 | Load spectrum compression and life prediction method and system for structural member of excavator |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116629002A (en) |
-
2023
- 2023-05-30 CN CN202310624705.XA patent/CN116629002A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Asgarian et al. | Damage detection of jacket type offshore platforms using rate of signal energy using wavelet packet transform | |
| JP6949060B2 (en) | Systems and methods for rapid prediction of hydrogen-induced cracking (HIC) in pipelines, pressure vessels and piping systems and for taking action on it. | |
| US9704266B2 (en) | Non-contacting monitor for bridges and civil structures | |
| CN111435557B (en) | Fault detection device for detecting machine part problems | |
| TWI449883B (en) | Method for analyzing structure safety | |
| US7840376B2 (en) | Risk-based design and maintenance systems and methods | |
| CN110210149B (en) | System and method for acquiring dynamic response information of stress and strain in road | |
| CN117629549B (en) | Bridge building health monitoring and safety early warning system | |
| CN113255579B (en) | Method for automatically identifying and processing construction monitoring abnormal acquisition data | |
| CN116306031A (en) | Tunnel mainframe monitoring and analyzing method based on automatic acquisition of big data | |
| CN116090347A (en) | Intelligent monitoring and early warning system for historical building structure under steady load | |
| CN110726542A (en) | Analysis method for fatigue life of spring | |
| CN107506832B (en) | Hidden danger mining method for assisting monitoring tour | |
| CN112801199B (en) | Excavator movable arm service life prediction method and device, electronic equipment and storage medium | |
| CN116629002A (en) | Load spectrum compression and life prediction method and system for structural member of excavator | |
| KR101946631B1 (en) | System and method for monitoring state of structure based compressive sensing | |
| RU2515130C1 (en) | Seismometric method to monitor technical condition of buildings and / or structures | |
| CN111259494B (en) | Health monitoring and analyzing method for heavy machine equipment | |
| CN112182863A (en) | Engineering machinery health monitoring method, residual life estimation method and system | |
| CN117056686B (en) | Alarming method and system for detecting surface defects of pressure container | |
| CN112782236A (en) | Material state monitoring method, system and device of converter cabinet and storage medium | |
| CN111397822A (en) | Bridge safety monitoring device and method based on laser gyroscope | |
| CN217331456U (en) | Ground pressure monitoring system and equipment | |
| CN110532116B (en) | System reliability modeling method and device | |
| CN117516778B (en) | Monitoring and early warning method and system based on ultralow frequency anchor rod tension sensor |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |