CN111238427A - Method for monitoring damage of tower body steel structure of tower crane in real time - Google Patents

Method for monitoring damage of tower body steel structure of tower crane in real time Download PDF

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CN111238427A
CN111238427A CN202010028247.XA CN202010028247A CN111238427A CN 111238427 A CN111238427 A CN 111238427A CN 202010028247 A CN202010028247 A CN 202010028247A CN 111238427 A CN111238427 A CN 111238427A
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value
data
tower crane
mean
sequence data
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CN111238427B (en
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王胜春
安宏
宋世军
李文豪
王忠雷
牛山
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Shandong Jianzhu University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B21/00—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
    • G01B21/02—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring length, width, or thickness
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
    • B66C13/00—Other constructional features or details
    • B66C13/16—Applications of indicating, registering, or weighing devices
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
    • B66C15/00—Safety gear
    • B66C15/06—Arrangements or use of warning devices
    • B66C15/065—Arrangements or use of warning devices electrical
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
    • B66C23/00—Cranes comprising essentially a beam, boom, or triangular structure acting as a cantilever and mounted for translatory of swinging movements in vertical or horizontal planes or a combination of such movements, e.g. jib-cranes, derricks, tower cranes
    • B66C23/88—Safety gear
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M5/00—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings
    • G01M5/0033—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings by determining damage, crack or wear

Abstract

The utility model provides a tower crane tower body steel construction damage monitoring method, through the displacement change of monitoring tower machine, establishes tower machine displacement change mathematical model, establishes the database through a large amount of data, can provide multiple operating mode contrast classification, application method need not to shut down the tower machine, can realize that the tower machine is in the heavy work to the real-time evaluation and the monitoring of its tower body steel construction state, and whether the structure of swift learning tower machine is safe, whether early warning, whether damage, compensatied the not enough in current tower machine safety monitoring field.

Description

Method for monitoring damage of tower body steel structure of tower crane in real time
Technical Field
The invention relates to a method for monitoring damage of a tower crane body steel structure, and belongs to the technical field of tower crane steel structure safety monitoring.
Background
With the development of society, the building industry has leaped forward in recent years, all high-rise buildings can use the tower crane, with the increasing engineering quantity of buildings, the working time of the tower crane is gradually prolonged, and the potential safety hazards of some tower cranes cannot be discovered in time. Although the prior art has made corresponding research on tower cranes, the steel structure of the tower body is not monitored in real time, danger coefficients are judged, and safety information is not fed back, namely, the real-time monitoring of the steel structure state of the tower crane is still a blank.
Chinese patent CN106446384B discloses a damage identification method for a main beam structure of a bridge crane, which identifies the damage of the main beam structure of the bridge crane based on strain modal analysis, and includes the following steps: acquiring strain modal data of a main beam; calculating and fitting a strain mode differential curve; constructing a damage position sensitivity coefficient, and quantitatively analyzing and determining a damage position; step (4), establishing an ARMA model by using undamaged data adjacent to the damaged position; predicting through a model to obtain a damage degree coefficient; step (6), establishing a damage degree judgment equation; and (7) judging the position and the degree of the damage to finish the damage identification. This patent document is to determine structural damage using the relationship between damage to the main beam structure and its vibration mechanics characteristics.
Chinese patent CN110059440A discloses a crane fatigue analysis system and an analysis method, which are based on the combination of static test and random dynamic test of an operation flow, and establish an intelligent evaluation system for random fatigue life of a crane structure; carrying out crane structure fatigue source positioning and stress correction based on finite element simulation analysis; and (4) counting the load cycle below the fatigue limit into a load spectrum, and evaluating the fatigue life and safety of the structure by adopting a piecewise linear damage accumulation theory.
Chinese patent CN110489816A discloses an analysis method for quantifying the fatigue damage grade of a crane jib, firstly determining the fatigue damage division standard of the crane jib and obtaining the dynamic response of the crane jib of each grade; then, performing double-spectrum analysis respectively to extract main characteristic values; then constructing a normal cloud reference model according to the main characteristic values; and finally, comparing the main characteristic value of the crane jib required to be measured with a normal cloud reference model, and dividing the damage degree grade. The patent document integrates a bispectrum nonlinear analysis method and a normal cloud model, compared with the traditional damage identification technology, the introduction of the bispectrum nonlinear analysis method can more sensitively extract the nonlinear characteristics of the crane jib, and the introduction of the normal cloud model enables the identification efficiency to be higher.
The cranes corresponding to the chinese patent CN106446384B and the chinese patent CN110059440A are all bridge cranes, and the bridge cranes are mainly applied to work environments where work places such as workshops or ports are basically unchanged. Compared with a tower crane, the bridge crane is relatively simple in structure, the structure is basically unchanged in the using process, a large number of strain sensors need to be arranged in the method described in the Chinese patent CN106446384B, however, the tower crane is mostly used in construction sites, the working position is changed at any time along with the construction condition of the building, and the installation and the disassembly of the large number of sensors are not beneficial to the use of the tower crane.
The Chinese patent CN110059440A and the Chinese patent CN110489816A are analyzed according to finite element models, model databases are built, the bridge crane with a simple structure can be corrected easily, and for tower cranes with complex structures and complex working conditions, cloud models or databases built by using finite element simulation data are easy to generate large deviation when damage judgment is carried out.
The Chinese patent CN106446384B is a damage identification method for a main beam structure of a bridge crane, which is proposed according to strain data, and the Chinese patent CN110489816A is an analysis method for quantifying fatigue damage grade of a suspension arm of the crane, which is proposed according to power.
In conclusion, in the technical field, how to monitor the damage of the steel structure of the tower crane according to the dynamic displacement of the tower crane and the complex working condition environmental factors becomes a technical problem to be solved all the time, and further, how to install the sensor by the tower body as few as possible and realize accurate monitoring is also a technical problem which is difficult to solve in the field.
Disclosure of Invention
Aiming at the technical defects, the invention provides a method for monitoring damage of a tower body steel structure of a tower crane.
The technical scheme of the invention is as follows:
a method for monitoring damage of a tower crane body steel structure is characterized by comprising the following steps:
step 1: mounting a displacement sensor on a main limb of a standard joint at the highest position of the tower crane; the design here aims at: all sections of the tower body at the lower part of the displacement sensor can be monitored by being arranged on the main limb of the standard section at the highest position of the tower crane;
step 2: the displacement sensor respectively collects and records the dynamic horizontal displacement monitored by the displacement sensor within the time T of the installation position;
and step 3: dividing dynamic displacement recorded in T time into n sections of sequence data according to time, wherein the length of each section of sequence data is l, the group number of each section of sequence data is m, and m is 123 … n; the advantages of the design here are: the n-segment sequence data are used for carrying out comparison and analysis, and the damage condition of the tower body cannot be judged due to overhigh data dimension, so that the dimension of the comparison data is considered to be further reduced, the longer the data length is in a certain range, the truer the mathematical model built by the data length is, and the more the data segments are, the higher the analysis accuracy is;
and 4, step 4: and (3) establishing a mathematical model by using the sequence data equally divided into n sections:
Figure BDA0002363256270000031
calculating the coefficient of each segment of sequence data by using least square method
Figure BDA0002363256270000032
Figure BDA0002363256270000033
Figure BDA0002363256270000034
Figure BDA0002363256270000041
Figure BDA0002363256270000042
Y＝XA+ε
Figure BDA0002363256270000043
Figure BDA0002363256270000044
In the above formulas (i), (i), (V) and (V i), p is the model order,
Figure BDA0002363256270000045
is the coefficient of the model, εmIn order to be a deviation, the deviation,
Figure BDA0002363256270000046
for the sequence data with the group number m obtained in step 3, m is 123 … n, t is a subscript of sorting the data of each piece of sequence data according to time, t is 1234 … l, f is 1234 … p, and g is 1234 … p;
the coefficient of each segment of the sequence data obtained by the above formula
Figure BDA0002363256270000047
Performing comparative analysis, wherein the damage condition of the tower body still cannot be accurately analyzed, and considering that the damage is possibly small, the damage is dispersed in each coefficient, so that the characteristic is further considered to be condensed;
calculating the mean value of the model coefficients in the intact state:
Figure BDA0002363256270000048
a＝(a1a2…ap) Is the mean value of the coefficients of the model in an intact state;
and averaging each piece of sequence data with the sequence data:
Figure BDA0002363256270000049
and solving the mean sum of all the mean values of each segment of sequence data by using the mean value of each segment of sequence data:
Figure BDA0002363256270000051
wherein the content of the first and second substances,
Figure BDA0002363256270000052
is the average value of each segment of sequence data, l is the length of each segment of sequence data,
Figure BDA0002363256270000053
is the sum of the mean values;
and 5: calculating the deviation of each section of sequence data by using the mean value of the coefficients of the model in a good state, each section of sequence data and a deviation processing formula, calculating the independent value of each section of sequence data by using the deviation and independent value formula, calculating the independent value sum of all the independent values,
the deviation processing formula is as follows:
Figure BDA0002363256270000054
the independent value formula is as follows:
Figure BDA0002363256270000055
τsum＝τ1+τ2+τ3…+τn(XΙΙ)
in formula (X), formula (X i) and formula (X i), a is the mean value of the coefficients of the model in good condition, epsilonmIn order to be a deviation, the deviation,
Figure BDA0002363256270000056
the sequence data with the group number m obtained in the step 3 is m 123 … n, l is the length of each piece of sequence data, and taumIs an independent value, τsumT is the sum of independent values, t is the subscript of each piece of data sorted by time, t is 1234 … l;
step 6: under the conditions of different working conditions of normal and intact states of the same tower crane, respectively collecting, recording and calculating the mean value of each section of sequence data according to the steps 2-5
Figure BDA0002363256270000057
Independent value of taumMean, and
Figure BDA0002363256270000058
and independent value of andsumcounting to form a database;
and 7: and 6, after the same tower crane works for a period of time, substituting the mean value of the model in the intact state calculated in the step 4 according to the step 2, the step 3, the step 4 and the step 5 under the same working condition as the database in the step 6, and calculating the mean value of each section of sequence data of the tower crane to be evaluated at the moment
Figure BDA0002363256270000061
Independent value of taumMean, and
Figure BDA0002363256270000062
and independent value of andsumusing the average value of each section of sequence data of the tower crane in the good state of the database of the step 6
Figure BDA0002363256270000063
Independent value of taumMean, and
Figure BDA0002363256270000064
and independent value of andsumand the mean value of each section of sequence data of the tower crane to be evaluated
Figure BDA0002363256270000065
Independent value of taumMean, and
Figure BDA0002363256270000066
and independent value of andsumnormalizing by a normalization formula to obtain a mean characteristic value and an independent value characteristic value,
the normalization equation is:
Figure BDA0002363256270000067
Figure BDA0002363256270000068
in formulas (X I) and (IXV),
Figure BDA0002363256270000069
the method comprises the following steps of (1) obtaining an average characteristic value of the tower crane to be evaluated and the tower crane in the same working condition and in a good condition in a database:
when m is 123 … n,
Figure BDA00023632562700000610
the mean characteristic value of the tower cranes in the same working condition and in good condition in the database is obtained;
when m is n +1n +2n +3 … n + n,
Figure BDA00023632562700000611
the mean characteristic value of the tower crane to be evaluated is obtained;
when m is 123 … n, σmThe characteristic values are independent values of tower cranes in the same working condition and in good condition in a database;
when m is n +1n +2n +3 … n + n, σmThe characteristic value is an independent value characteristic value of the tower crane to be evaluated;
and 8: the mean characteristic value of the tower crane to be evaluated calculated in the step 7
Figure BDA00023632562700000612
And the eigenvalue σ of the independent valuemThe characteristic value of the mean value of the tower crane in the same working condition and in the intact state in the database in the step 7
Figure BDA00023632562700000613
And the eigenvalue σ of the independent valuemPerforming distance comparison analysis:
if the mean characteristic value of the tower crane to be evaluated is calculated in the step 7
Figure BDA00023632562700000614
And the eigenvalue σ of the independent valuemThe average characteristic value of the tower crane in the intact state under the same working condition as the database in the step 7
Figure BDA00023632562700000615
And the eigenvalue σ of the independent valuemIf the tower crane to be evaluated is judged to be of one type, the tower crane to be evaluated is not damaged in the state of the step 7;
if the mean characteristic value of the tower crane to be evaluated is calculated in the step 7
Figure BDA00023632562700000616
And the eigenvalue σ of the independent valuemThe average characteristic value of the tower crane in the intact state under the same working condition as the database in the step 7
Figure BDA00023632562700000617
And the eigenvalue σ of the independent valuemIf the tower crane to be evaluated is not judged as one type, the tower crane to be evaluated in the state of the step 7 is damaged.
According to the invention, the installation position of the displacement sensor in the step 1 is preferably unchanged. Namely, the installation position of the displacement sensor can not be changed under the condition that the tower crane is not integrally disassembled.
According to the invention, preferably, the length l of each piece of data of the n pieces of sequence data in the step 3 is not less than 100, and the length l and the number n of each piece of data of the sequence data are the same as those of each piece of data of the tower crane in the intact state of the database when the tower crane to be evaluated performs comparative analysis.
Preferably, the distance comparison analysis method in step 8 includes:
1) calculating Euclidean distance between data, wherein the characteristic value of the independent value is a horizontal axis and the characteristic value of the mean value is a vertical axis when the distance is calculated,
the formula for calculating the Euclidean distance between the data is as follows:
Figure BDA0002363256270000071
2) the method comprises the following steps that the adjacent radius of a set point and the minimum data number in a radius range are set, the minimum data number is (n/2) -1, the Euclidean distance formula is used for calculating the distance between n groups of complete characteristic value data sets, the distance between each group of complete characteristic value data sets and the distance is calculated, the (n/2) +1 data, which are arranged from small to large, are selected, the distance between each group of complete characteristic value data sets and the n groups of characteristic value data sets is selected, the maximum value in the n data is the adjacent radius, and the value can be automatically calculated through the method;
3) if the distance between the data is less than or equal to the specified radius, the data is within the radius; if the distance between the data is greater than the adjacent radius, the data is not within the radius,
4) if the number of the data in the radius range is larger than the minimum number of the data in the set radius range, the data are judged to be of one type, otherwise, the data are not of one type.
The technical advantages of the invention are as follows:
according to the invention, the displacement change of the tower crane is monitored, the mathematical model of the displacement change of the tower crane is established, and the database is established through a large amount of data, so that the comparison classification of various working conditions can be provided.
Drawings
FIG. 1 is a basic flow chart of a tower crane body steel structure damage monitoring method;
FIG. 2 is a diagram of the determination results of the tower crane in a good condition and the tower crane to be evaluated in embodiment 2;
fig. 3 is a diagram of the determination results of the tower crane in a good condition and the tower crane to be evaluated in embodiment 4.
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 technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs, and all other embodiments obtained by one of ordinary skill in the art based on the embodiments of the present invention without making any creative effort fall within the protection scope of the present invention.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise. The appearances of the phrases "upper," "lower," "left," and "right" in this specification are not necessarily all referring to the same drawing but are merely illustrative and non-limiting with respect to the structure, but rather are merely for convenience in describing and simplifying the specification and the appended claims, rather than indicating or implying that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and are therefore not to be construed as limiting the invention. Furthermore, the terms "first," "second," "third," "first," "second," and the like are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance. The terms "fixedly connected" and "fixed" in the present invention are only used for convenience of describing the present invention and simplifying the description, but do not indicate or imply the specific connection means such as "welded", "bolted", and the like.
Examples 1,
A method for monitoring damage of a tower body steel structure of a tower crane comprises the following steps: installing a displacement sensor, extracting dynamic displacement, establishing a tower crane mathematical model according to the dynamic displacement, establishing a database according to the tower crane mathematical model, performing contrast classification according to the mathematical model established by the dynamic displacement measured again and the tower crane mathematical model under the same working condition in the database, and evaluating whether the tower crane is damaged, wherein the specific steps are as follows:
step 1: mounting a displacement sensor on a main limb of a standard joint at the highest position of the QTZ315 tower crane;
step 2: collecting and recording the 120-second dynamic displacement X at the position where the displacement sensor is installed when the hoisting weight of the tower crane is 3400kg in the intact state1、X2、X3…XjWhen the time interval of the dynamic displacement data is 0.1s, j is 1200,
X1＝-0.57mm、X2＝-0.55mm、X3＝-0.52mm…X1200＝0.275mm；
and step 3: the dynamic displacement of 120 seconds is divided into 12 pieces of sequence data with the length of 100, wherein the 1 st piece of sequence data is
Figure BDA0002363256270000091
The 2 nd sequence data is
Figure BDA0002363256270000092
The 12 th sequence data is
Figure BDA0002363256270000093
And 4, step 4: a mathematical model is built by using the sequence data which are divided into n segments,
Figure BDA0002363256270000094
Figure BDA0002363256270000095
Figure BDA0002363256270000096
Figure BDA0002363256270000097
Figure BDA0002363256270000098
Y＝XA+ε
Figure BDA0002363256270000101
Figure BDA0002363256270000102
Figure BDA0002363256270000103
Figure BDA0002363256270000104
Figure BDA0002363256270000105
the coefficients for each piece of sequence data were calculated as:
a1＝[-5.68682 13.86336… -0.55482]、a2＝[-5.0542 9.6422,… -0.1089]…a12＝[-3.98262 4.884064 …-0.42359]
calculating the mean of the coefficients of 12 good condition models as
a＝[-4.50622 7.185643 -3.75086 -1.04173 0.324163 0.97473 0.85229 -0.81229 -1.2643 1.43267 -0.3938]
Calculation of the mean of 12 sets of sequence data is shown in table 1,
table 1 shows the mean value of each sequence data of the tower crane in an intact state in example 1
Figure BDA0002363256270000106
Figure BDA0002363256270000107
Figure BDA0002363256270000111
The sum of the mean values of 12 sets of sequence data was calculated as:
Figure BDA0002363256270000112
and 5: calculating the deviation of each section of sequence data by using the mean value of the coefficients of the model in a good state, each section of sequence data and a deviation processing formula, calculating the characteristic value of each section of sequence data by using the deviation and an independent value formula, calculating the sum of all independent values,
the deviation processing formula is as follows:
Figure BDA0002363256270000113
the independent value formula is as follows:
Figure BDA0002363256270000114
τsum＝τ1+τ2+τ3…+τn
in formula (V), formula (V I) and formula (V I), a is the mean value of the coefficients of the model in good condition, εmIn order to be a deviation, the deviation,
Figure BDA0002363256270000115
the sequence data with the group number m obtained in the step 3 is m 123 … n, l is the length of each piece of sequence data, and taumIs an independent value, τsumIs the sum of the independent values;
the independent values obtained are shown in table 2,
table 2 shows the independent value tau of the tower crane in the intact state in the embodiment 1m
Sequence data set number Independent values of sequence data (mm)2)
1 1.10698×10-9
2 3.32643×10-9
3 1.51831×10-9
4 7.58675×10-10
5 5.28808×10-10
6 5.43457×10-10
7 6.64528×10-10
8 3.25115×10-10
9 2.56696×10-10
10 5.74517×10-10
11 3.72826×10-10
12 2.96212×10-10
The sum of the independent values is obtained as:
Figure BDA0002363256270000121
step 6: under the conditions of different working conditions of normal and intact states of the same tower crane, respectively collecting, recording and calculating the mean value of each section of sequence data according to the steps 2-5
Figure BDA0002363256270000122
Independent value of taumMean, and
Figure BDA0002363256270000123
and independent value of andsumcounting to form a database;
and 7: 6, after the same tower crane works and is used for a period of time, the same tower crane works and uses the same tower crane in the step6, substituting the mean value of the model coefficient in the intact state calculated in the step 4 into the mean value of the model coefficient in the intact state calculated in the step 4 according to the step 2, the step 3, the step 4 and the step 5 under the same working condition in the database, and calculating the mean value of each section of sequence data of the tower crane to be evaluated at the moment
Figure BDA0002363256270000131
Independent value of taumMean, and
Figure BDA0002363256270000132
and independent value of andsumusing the average value of each section of sequence data of the tower crane in the good state of the database of the step 6
Figure BDA0002363256270000133
Independent value of taumMean, and
Figure BDA0002363256270000134
and independent value of andsumand the mean value of each section of sequence data of the tower crane to be evaluated
Figure BDA0002363256270000135
Independent value of taumMean, and
Figure BDA0002363256270000136
and independent value of andsumnormalizing by a normalization formula to obtain a mean characteristic value and an independent value characteristic value,
the normalization equation is:
Figure BDA0002363256270000137
Figure BDA0002363256270000138
the individual value characteristic and the mean value characteristic are shown in table 3,
table 3 shows the characteristic values of the independent values and the characteristic values of the mean value of the tower crane in the intact state and the tower crane to be evaluated in example 1
Figure BDA0002363256270000139
Figure BDA0002363256270000141
The group numbers 1-12 are tower cranes in the database under the same working condition as the tower crane to be evaluated, and the group numbers 13-24 are tower cranes to be evaluated;
and 8: the mean characteristic value of the tower crane to be evaluated calculated in the step 7
Figure BDA0002363256270000142
And the eigenvalue σ of the independent valuemThe characteristic value of the mean value of the tower crane in the same working condition and in the intact state in the database in the step 7
Figure BDA0002363256270000143
And the eigenvalue σ of the independent valuemCarrying out distance comparison analysis, and if the mean characteristic value of the tower crane to be evaluated is calculated in the step 7
Figure BDA0002363256270000151
And the eigenvalue σ of the independent valuemThe average characteristic value of the tower crane in the intact state under the same working condition as the database in the step 7
Figure BDA0002363256270000152
And the eigenvalue σ of the independent valuemIf the tower crane to be evaluated is judged to be of one type, the tower crane to be evaluated in the state of the step 7 has no damage, and if the mean characteristic value of the tower crane to be evaluated is calculated in the step 7
Figure BDA0002363256270000153
And the eigenvalue σ of the independent valuemThe average characteristic value of the tower crane in the intact state under the same working condition as the database in the step 7
Figure BDA0002363256270000154
And the eigenvalue σ of the independent valuemIf the tower crane to be evaluated is not judged as one type, the tower crane to be evaluated in the state of the step 7 is damaged.
Examples 2,
The specific application method for determining whether the tower crane is in the damage state in the step 8 in the embodiment 1 is as follows:
step 1: calculating the Euclidean distance of the 24 groups of data in the table 3, wherein the characteristic value of the independent value is a horizontal axis and the characteristic value of the mean value is a vertical axis when the distance is calculated;
step 2: the minimum data number is set to be 5, and the distance between 12 groups of characteristic values of the tower crane in a complete state is calculated as shown in Table 4
Table 4 shows the distances between 12 sets of characteristic values of the tower crane in a good condition in example 2
Figure BDA0002363256270000155
Figure BDA0002363256270000161
Selecting the distance between each group of characteristic values and the 12 groups of characteristic value data groups, wherein the distance comprises arranging 7 th data from small to large, the maximum value in the 12 data is the adjacent radius, and the adjacent radius is 0.003935;
step 3, if the distance between the two groups of data obtained in the step 2 is less than or equal to the adjacent radius 0.003935, the data are in the radius range, if the distance between the data is greater than the adjacent radius 0.003935, the data are not in the radius range, if the number of the data in the radius range is greater than the minimum number of data 5 in the set radius range, the data are judged as one type, otherwise, the data are not one type, and the judgment result is shown in table 5
Table 5 shows the determination results of the tower crane in good condition and the tower crane to be evaluated in example 2
Sequence data set number The result of the judgment Sequence data set number The result of the judgment
1 1 13 -1
2 1 14 -1
3 1 15 -1
4 1 16 -1
5 1 17 -1
6 1 18 -1
7 1 19 -1
8 1 20 -1
9 1 21 -1
10 1 22 -1
11 1 23 -1
12 1 24 -1
Knowing the mean characteristic value of 1-12 groups of tower cranes in good condition
Figure BDA0002363256270000171
And the eigenvalue σ of the independent valuemAll the 1-12 groups of data are judged to be 1, and the 13-24 groups of data are mean characteristic values of the tower cranes to be evaluated
Figure BDA0002363256270000172
And the eigenvalue σ of the independent valuemSaidThe determination code of all data in the 13-24 groups of data is-1, the analysis comparison graph with the characteristic value of the independent value as the horizontal axis and the characteristic value of the mean value as the vertical axis is shown in fig. 2, wherein '+' in fig. 2 is a mark of which the determination code is 1, and 'o' in fig. 2 is a mark of which the determination code is-1, and the groups of data 1-12 and 13-24 are not determined as one type, namely the tower crane to be evaluated is in a damage state.
Examples 3,
A method for monitoring damage of a tower body steel structure of a tower crane comprises the following steps: installing a displacement sensor, extracting dynamic displacement, establishing a tower crane mathematical model according to the dynamic displacement, establishing a database according to the tower crane mathematical model, performing contrast classification according to the mathematical model established by the dynamic displacement measured again and the tower crane mathematical model under the same working condition in the database, and evaluating whether the tower crane is damaged, wherein the specific steps are as follows:
step 1: mounting a displacement sensor on a main limb of a standard joint at the highest position of the QTZ80 tower crane;
step 2: collecting and recording the 120-second dynamic displacement of the position where the displacement sensor is installed as X when the lifting weight of the tower crane is 1100kg under the intact state1、X2、X3…XjWhen the time interval of the dynamic displacement data is 0.1s, j is 1200,
X1＝-7.46mm、X2＝-3.51mm、X3＝1.35mm…X1200＝8.09mm；
and step 3: the dynamic displacement of 120 seconds is divided into 12 pieces of sequence data with the length of 100, wherein the 1 st piece of sequence data is
Figure BDA0002363256270000181
The 2 nd sequence data is
Figure BDA0002363256270000182
The 12 th sequence data is
Figure BDA0002363256270000183
And 4, step 4: a mathematical model is built by using the sequence data which are divided into n segments,
Figure BDA0002363256270000184
Figure BDA0002363256270000185
Figure BDA0002363256270000186
Figure BDA0002363256270000187
Figure BDA0002363256270000191
Y＝XA+ε
Figure BDA0002363256270000192
Figure BDA0002363256270000193
Figure BDA0002363256270000194
Figure BDA0002363256270000195
Figure BDA0002363256270000196
the coefficients for each piece of sequence data were calculated as:
a1＝[-4.1145,5.7682,…,-0.3856]
a2＝[-4.0195,4.9995,…,-0.3998]
…
a12＝[-3.9209 5.0545…-0.3548]
the average of the coefficients for the model in 12 intact states was calculated as a [ -3.7455,4.3463, -0.1539, -2.0991, -0.8208,1.6196,0.8138, -0.6961, -1.0203,0.9845, -0.2255]
Calculation of mean values for 12 sets of sequence data is shown in Table 6
Table 6 shows the mean value of each sequence data in the hoisting condition of the tower crane in the intact state in example 3
Figure BDA0002363256270000197
Figure BDA0002363256270000198
Figure BDA0002363256270000201
The sum of the mean values of 12 sets of sequence data was calculated as:
Figure BDA0002363256270000202
and 5: calculating the deviation of each section of sequence data by using the mean value of the coefficients of the model in a good state, each section of sequence data and a deviation processing formula, calculating the characteristic value of each section of sequence data by using the deviation and an independent value formula, calculating the sum of all independent values,
the deviation processing formula is as follows:
Figure BDA0002363256270000203
the independent value formula is as follows:
Figure BDA0002363256270000204
τsum＝τ1+τ2+τ3…+τn
in the formulaWhere a is the mean value of the coefficients of the model in the intact state, εmIn order to be a deviation, the deviation,
Figure BDA0002363256270000211
the sequence data with the group number m obtained in the step 3 is m 123 … n, l is the length of each piece of sequence data, and taumIs an independent value, τsumIs the sum of the independent values;
independent values were obtained as shown in table 7,
table 7 shows independent values tau under the lifting condition of the tower crane in the intact state in embodiment 3m
Sequence data set number Independent values of sequence data (mm)2)
1 4.20735×10-8
2 3.25261×10-8
3 1.70585×10-8
4 1.90315×10-8
5 1.47195×10-8
6 1.41689×10-8
7 1.86541×10-8
8 2.07745×10-8
9 1.82519×10-8
10 1.65967×10-8
11 1.53136×10-8
12 1.37282×10-8
The sum of the independent values is obtained as:
Figure BDA0002363256270000212
step 6: under the conditions of different working conditions of normal and intact states of the same tower crane, respectively collecting, recording and calculating the mean value of each section of sequence data according to the steps 2-5
Figure BDA0002363256270000221
Independent value of taumMean, and
Figure BDA0002363256270000222
and independent value of andsumcounting to form a database;
and 7: after the same tower crane in the step 6 works and is used for a period of time, under the same working condition as that in the database in the step 6, according to the steps 2,Step 3, step 4 and step 5, substituting the mean value of the model coefficient in the intact state calculated in the step 4, and calculating the mean value of each section of sequence data of the tower crane to be evaluated at the moment
Figure BDA0002363256270000223
Independent value of taumMean, and
Figure BDA0002363256270000224
and independent value of andsumusing the average value of each section of sequence data of the tower crane in the good state of the database of the step 6
Figure BDA0002363256270000225
Independent value of taumMean, and
Figure BDA0002363256270000226
and independent value of andsumand the mean value of each section of sequence data of the tower crane to be evaluated
Figure BDA0002363256270000227
Independent value of taumMean, and
Figure BDA0002363256270000228
and independent value of andsumnormalizing by a normalization formula to obtain a mean characteristic value and an independent value characteristic value,
the normalization equation is:
Figure BDA0002363256270000229
Figure BDA00023632562700002210
the individual value characteristic and the mean value characteristic are shown in table 8,
table 8 shows the characteristic values of the independent values and the characteristic values of the mean values of the tower crane in the intact state and the tower crane to be evaluated in example 3
Figure BDA00023632562700002211
Figure BDA0002363256270000231
The group numbers 1-12 are tower cranes in the database under the same working condition as the tower crane to be evaluated, and the group numbers 13-24 are tower cranes to be evaluated;
and 8: the mean characteristic value of the tower crane to be evaluated calculated in the step 7
Figure BDA0002363256270000232
And the eigenvalue σ of the independent valuemThe characteristic value of the mean value of the tower crane in the same working condition and in the intact state in the database in the step 7
Figure BDA0002363256270000233
And the eigenvalue σ of the independent valuemCarrying out distance comparison analysis, and if the mean characteristic value of the tower crane to be evaluated is calculated in the step 7
Figure BDA0002363256270000241
And the eigenvalue σ of the independent valuemThe average characteristic value of the tower crane in the intact state under the same working condition as the database in the step 7
Figure BDA0002363256270000242
And the eigenvalue σ of the independent valuemIf the tower crane to be evaluated is judged to be of one type, the tower crane to be evaluated in the state of the step 7 has no damage, and if the mean characteristic value of the tower crane to be evaluated is calculated in the step 7
Figure BDA0002363256270000243
And the eigenvalue σ of the independent valuemThe average characteristic value of the tower crane in the intact state under the same working condition as the database in the step 7
Figure BDA0002363256270000244
And the eigenvalue σ of the independent valuemIf not, the step 7 stateAnd the tower crane to be evaluated below is damaged.
Examples 4,
The specific application method for determining whether the tower crane is in the damage state in the step 8 in the embodiment 3 is as follows:
step 1: calculating the Euclidean distance of the 24 groups of data in the table 8, wherein the characteristic value of the independent value is a horizontal axis and the characteristic value of the mean value is a vertical axis when the distance is calculated;
step 2: setting the minimum data number as 5, calculating the distance between 12 groups of characteristic values of the tower crane in a sound state, selecting the distance between each group of characteristic values and the 12 groups of characteristic value data groups, wherein the distance between each group of characteristic values and the 12 groups of characteristic value data groups comprises arranging 7 th data from small to large, the maximum value in the 12 data is the adjacent radius, and the adjacent radius is 0.008437;
step 3, if the distance between the two groups of data obtained in the step 2 is less than or equal to the adjacent radius 0.008437, the data are in the radius range, if the distance between the data is greater than the adjacent radius 0.008437, the data are not in the radius range, if the number of the data in the radius range is greater than the minimum number of data in the set radius range of 5, the data are judged as one type, otherwise, the data are not one type, the judgment result is shown in table 9,
table 9 shows the distances between 12 sets of characteristic values of the tower crane in a good condition in example 4
Figure BDA0002363256270000245
Figure BDA0002363256270000251
Knowing the mean characteristic value of 1-12 groups of tower cranes in good condition
Figure BDA0002363256270000252
And the eigenvalue σ of the independent valuemAll the 1-12 groups of data are judged to be 1, and the 13-24 groups of data are mean characteristic values of the tower cranes to be evaluated
Figure BDA0002363256270000253
And the eigenvalue σ of the independent valuemThe determination code of all data in the 13-24 groups of data is-1, the analysis comparison graph with the characteristic value of the independent value as the horizontal axis and the characteristic value of the mean value as the vertical axis is shown in fig. 3, wherein '+' in fig. 3 is a mark with the determination code being 1, and 'o' in fig. 2 is a mark with the determination code being-1, and the groups of data 1-12 and 13-24 are not determined as one type, that is, the tower crane to be evaluated is in a damage state.

Claims (4)

1. A method for monitoring damage of a tower crane body steel structure is characterized by comprising the following steps:
step 1: mounting a displacement sensor on a main limb of a standard joint at the highest position of the tower crane;
step 2: the displacement sensor respectively collects and records the dynamic horizontal displacement monitored by the displacement sensor within the time T of the installation position;
and step 3: dividing dynamic displacement recorded in T time into n sections of sequence data according to time, wherein the length of each section of sequence data is l, the group number of each section of sequence data is m, and m is 123 … n;
and 4, step 4: and (3) establishing a mathematical model by using the sequence data equally divided into n sections:
Figure FDA0002363256260000011
calculating the coefficient of each segment of sequence data by using least square method
Figure FDA0002363256260000012
Figure FDA0002363256260000013
Figure FDA0002363256260000014
Figure FDA0002363256260000015
Figure FDA0002363256260000016
Y＝XA+ε
Figure FDA0002363256260000017
Figure FDA0002363256260000018
In the above formulas (i), (i), (V) and (V i), p is the model order,
Figure FDA0002363256260000019
is the coefficient of the model, εmIn order to be a deviation, the deviation,
Figure FDA00023632562600000110
for the sequence data with the group number m obtained in step 3, m is 123 … n, t is a subscript of sorting the data of each piece of sequence data according to time, t is 1234 … l, f is 1234 … p, and g is 1234 … p;
calculating the mean value of the model coefficients in the intact state:
Figure FDA0002363256260000021
a＝(a1a2… ap) Is the mean value of the coefficients of the model in an intact state;
and averaging each piece of sequence data with the sequence data:
Figure FDA0002363256260000022
and solving the mean sum of all the mean values of each segment of sequence data by using the mean value of each segment of sequence data:
Figure FDA0002363256260000023
wherein the content of the first and second substances,
Figure FDA0002363256260000024
is the average value of each segment of sequence data, l is the length of each segment of sequence data,
Figure FDA0002363256260000025
is the sum of the mean values;
and 5: calculating the deviation of each section of sequence data by using the mean value of the coefficients of the model in a good state, each section of sequence data and a deviation processing formula, calculating the independent value of each section of sequence data by using the deviation and independent value formula, calculating the independent value sum of all the independent values,
the deviation processing formula is as follows:
Figure FDA0002363256260000026
the independent value formula is as follows:
Figure FDA0002363256260000027
τsum＝τ1+τ2+τ3…+τn(XΙΙ)
in formula (X), formula (X i) and formula (X i), a is the mean value of the coefficients of the model in good condition, epsilonmIn order to be a deviation, the deviation,
Figure FDA0002363256260000028
the sequence data with the group number m obtained in the step 3 is m 123 … n, l is the length of each piece of sequence data, and taumIs an independent value, τsumT is the sum of independent values, t is the subscript of each piece of data sorted by time, t is 1234 … l;
step 6: under the conditions of different working conditions of normal and intact states of the same tower crane, respectively collecting, recording and calculating the mean value of each section of sequence data according to the steps 2-5
Figure FDA0002363256260000031
Independent value of taumMean, and
Figure FDA0002363256260000032
and independent value of andsumcounting to form a database;
and 7: and 6, after the same tower crane works for a period of time, substituting the mean value of the model in the intact state calculated in the step 4 according to the step 2, the step 3, the step 4 and the step 5 under the same working condition as the database in the step 6, and calculating the mean value of each section of sequence data of the tower crane to be evaluated at the moment
Figure FDA0002363256260000033
Independent value of taumMean, and
Figure FDA0002363256260000034
and independent value of andsumusing the average value of each section of sequence data of the tower crane in the good state of the database of the step 6
Figure FDA0002363256260000035
Independent value of taumMean, and
Figure FDA0002363256260000036
and independent value of andsumand the mean value of each section of sequence data of the tower crane to be evaluated
Figure FDA0002363256260000037
Independent value of taumMean, and
Figure FDA0002363256260000038
and independent value of andsumnormalizing by a normalization formula to obtain a mean characteristic value and an independent value characteristic value,
the normalization equation is:
Figure FDA0002363256260000039
Figure FDA00023632562600000310
in formulas (X I) and (IXV),
Figure FDA00023632562600000311
the method comprises the following steps of (1) obtaining an average characteristic value of the tower crane to be evaluated and the tower crane in the same working condition and in a good condition in a database:
when m is 123 … n,
Figure FDA00023632562600000312
the mean characteristic value of the tower cranes in the same working condition and in good condition in the database is obtained;
when m is n +1n +2n +3 … n + n,
Figure FDA00023632562600000313
the mean characteristic value of the tower crane to be evaluated is obtained;
when m is 123 … n, σmThe characteristic values are independent values of tower cranes in the same working condition and in good condition in a database;
when m is n +1n +2n +3 … n + n, σmThe characteristic value is an independent value characteristic value of the tower crane to be evaluated;
and 8: the mean characteristic value of the tower crane to be evaluated calculated in the step 7
Figure FDA00023632562600000314
And the eigenvalue σ of the independent valuemThe characteristic value of the mean value of the tower crane in the same working condition and in the intact state in the database in the step 7
Figure FDA00023632562600000315
And the eigenvalue σ of the independent valuemPerforming distance comparison analysis:
if the mean characteristic value of the tower crane to be evaluated is calculated in the step 7
Figure FDA0002363256260000041
And the eigenvalue σ of the independent valuemThe average characteristic value of the tower crane in the intact state under the same working condition as the database in the step 7
Figure FDA0002363256260000042
And the eigenvalue σ of the independent valuemIf the tower crane to be evaluated is judged to be of one type, the tower crane to be evaluated is not damaged in the state of the step 7;
if the mean characteristic value of the tower crane to be evaluated is calculated in the step 7
Figure FDA0002363256260000043
And the eigenvalue σ of the independent valuemThe average characteristic value of the tower crane in the intact state under the same working condition as the database in the step 7
Figure FDA0002363256260000044
And the eigenvalue σ of the independent valuemIf the tower crane to be evaluated is not judged as one type, the tower crane to be evaluated in the state of the step 7 is damaged.
2. The tower crane tower body steel structure damage monitoring method according to claim 1, wherein the installation position of the displacement sensor in the step 1 is unchanged.
3. The tower crane tower body steel structure damage monitoring method according to claim 1, characterized in that the length l of each piece of data of the n pieces of sequence data in the step 3 is not less than 100, and the length l and the number n of the pieces of data of the sequence data are the same as those of the tower crane in a database in a good state when the tower crane to be evaluated performs comparative analysis.
4. The tower crane tower steel structure damage monitoring method according to claim 1, wherein the distance comparative analysis method of the step 8 comprises the following steps:
1) calculating Euclidean distance between data, wherein the characteristic value of the independent value is a horizontal axis and the characteristic value of the mean value is a vertical axis when the distance is calculated,
the formula for calculating the Euclidean distance between the data is as follows:
Figure FDA0002363256260000045
2) the method comprises the following steps that the adjacent radius of a set point and the minimum data number in a radius range are set, the minimum data number is (n/2) -1, the Euclidean distance formula is used for calculating the distance between n groups of sound characteristic value data sets, the distance between each group of sound characteristic value data sets and the distance is calculated when the distance comprises the distance between each group of sound characteristic value data sets and the distance, the (n/2) +1 data, which is arranged from small to large, of the distance between each group of sound characteristic value data sets and the n groups of characteristic value data sets is selected, and the maximum value in the n data is the adjacent radius;
3) if the distance between the data is less than or equal to the specified radius, the data is within the radius; if the distance between the data is greater than the adjacent radius, the data is not within the radius,
4) if the number of the data in the radius range is larger than the minimum number of the data in the set radius range, the data are judged to be of one type, otherwise, the data are not of one type.
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