US 11,480,490 B2 · Large-scale high-speed rotary equipment measuring and intelligent learning assembly method and device based on vector minimization of geometry center, mass center, center of gravity and center of inertia

Abstract

The present invention provides a large-scale high-speed rotary equipment measuring and intelligent learning assembly method and device based on vector minimization geometry center, mass center, the center of gravity and the center of inertia, belonging to the technical field of mechanical assembly. The method includes the steps of establishing a four-parameter circular profile measuring model for a single stage of rotor, simplifying the established four-parameter circular profile measuring model for the single stage of rotor, and establishing a four-target optimization model of the geometry center, mass center, the center of gravity and the center of inertia of multiple stages of rotors based on the angular orientation mounting position of each stage of rotor. The device include a base, an air flotation shaft system, an aligning and tilt regulating workbench, precise force sensors, a static balance measuring platform, an upright column, a lower transverse measuring rod, a lower telescopic inductive sensor, an upper transverse measuring rod and an upper lever type inductive sensor.

A large-scale high-speed rotary equipment measuring and intelligent learning assembly method, comprising:

step 1:
establishing a four-parameter circular profile measuring model for a single stage of rotor, and obtaining an actual sampling angle distribution function of large-scale high-speed rotary equipment by utilizing the four-parameter circular profile measuring model, wherein the actual sampling angle distribution function of the large-scale high-speed rotary equipment is:
φ
i
=
θ
i
+
arcsin
(
d
+
e
⁢
sin
⁡
(
θ
i
-
α
)
r
+
r
0
⁢
cos
2
⁡
(
φ
i
-
β
)
cos
2
⁢
γ
+
sin
2
⁡
(
φ
i
-
β
)
)
,
⁢
i
=
1
,
2
,
…
⁢
,
n
wherein φi is a sampling angle relative to the geometric center, θi is a sampling angle relative to the center of gyration, d is the offset of a sensor measuring head, e is the eccentricity of the rotor, α is the eccentric angle of the rotor, r0 is a short axis of a fitted ellipse, r is the radius of the sensor measuring head, γ is an included angle between an axis of gyration and a geometric axis, β is an included angle between the projection of the geometric axis on a measuring plane and an initial direction of measuring, and n is the number of sampling points;
step 2:
obtaining a morphological filter based on non-equal interval sampling angles by utilizing the envelope filter principle according to the four-parameter circular profile measuring model for the single stage of rotor, taking any point P1 in a two-dimensional point set S, and enabling the point P1 as a start point and points which are less than 2α from the point P1 to constitute a subset S1, wherein S is a two-dimensional space coordinate point set of circular profile sampling points, and α is an alpha disc radius;
step 3:
taking any point P2 in the subset S1 to obtain two alpha discs which have a radius α and pass through the point P1 and the point P2, wherein the locus equation of the centers of inner and outer circles of the alpha discs is:
{
ρ
0
=
x
0
2
+
y
0
2
φ
0
=
arctan
⁢
y
0
x
0
⁢
⁢
or
⁢
⁢
{
ρ
0
′
=
x
0
′
2
+
y
0
′
2
φ
0
′
=
arctan
⁢
y
0
′
x
0
′
wherein P0 and P0′ are the centers of circles of the two alpha discs respectively;
and
{
x
0
=
ρ
1
⁢
cos
⁢
⁢
φ
1
+
0.5
⁢
(
ρ
2
⁢
cos
⁢
⁢
φ
2
-
ρ
1
⁢
cos
⁢
⁢
φ
1
)
+
H
⁡
(
ρ
2
⁢
sin
⁢
⁢
φ
2
-
ρ
1
⁢
sin
⁢
⁢
φ
1
)
y
0
=
ρ
1
⁢
sin
⁢
⁢
φ
1
+
0.5
⁢
(
ρ
2
⁢
sin
⁢
⁢
φ
2
-
ρ
1
⁢
sin
⁢
⁢
φ
1
)
+
H
⁡
(
ρ
1
⁢
cos
⁢
⁢
φ
1
-
ρ
2
⁢
cos
⁢
⁢
φ
2
)
{
x
0
′
=
ρ
1
⁢
cos
⁢
⁢
φ
1
+
0.5
⁢
(
ρ
2
⁢
cos
⁢
⁢
φ
2
-
ρ
1
⁢
cos
⁢
⁢
φ
1
)
-
H
⁡
(
ρ
2
⁢
sin
⁢
⁢
φ
2
-
ρ
1
⁢
sin
⁢
⁢
φ
1
)
y
0
′
=
ρ
1
⁢
sin
⁢
⁢
φ
1
+
0.5
⁢
(
ρ
2
⁢
sin
⁢
⁢
φ
2
-
ρ
1
⁢
sin
⁢
⁢
φ
1
)
-
H
⁡
(
ρ
1
⁢
cos
⁢
⁢
φ
1
-
ρ
2
⁢
cos
⁢
⁢
φ
2
)
H
=
α
2
(
ρ
1
⁢
cos
⁢
⁢
φ
1
-
ρ
2
⁢
cos
⁢
⁢
φ
2
)
2
+
(
ρ
1
⁢
sin
⁢
⁢
φ
1
-
ρ
2
⁢
sin
⁢
⁢
φ
2
)
2
-
1
4
ρ0, φ0, ρ0′, φ0′, ρ1, φ1, ρ2 and φ2 are polar diameters and polar angles of points P0, P0′, P1 and P2 in polar coordinates, respectively;
x0, y0, x0′ and y0′ are coordinates of points P0 and P0′ in an X-axis direction and a Y-axis direction respectively;
step 4:
obtaining the relationship between an alpha envelope boundary ∂Hα(S) and sampling point polar coordinates (ρi,φi) by utilizing the locus equation in step 3, and obtaining the circular profile information of the single stage of rotor according to the relationship between the alpha envelope boundary ∂Hα(S) and the sampling point polar coordinates (ρi,φi);
step 5:
when the eccentric error is e/r0<10−3 relative to the short axis of the fitted ellipse, performing power series expansion on the four-parameter circular profile measuring model for the single stage of rotor in step 1 so as to obtain a simplified four-parameter circular profile measuring model;
the simplified four-parameter circular profile measuring model is:
{
l
i
=
e
⁢
⁢
cos
⁡
(
θ
i
-
α
)
+
Δ
⁢
⁢
r
i
+
ξ
i
-
(
d
+
e
⁢
⁢
sin
⁡
(
θ
i
-
α
)
)
2
2
⁢
(
r
+
Δ
⁢
⁢
r
i
+
ξ
i
)
ξ
i
=
r
0
⁢
cos
2
⁡
(
η
i
)
cos
2
⁢
γ
+
sin
2
⁡
(
η
i
)
η
i
=
θ
i
+
arcsin
⁡
(
d
+
e
⁢
⁢
sin
⁡
(
θ
i
-
α
)
r
+
r
0
+
Δ
⁢
⁢
r
i
)
-
β
,
⁢
i
=
0
,
1
,
2
,
…
⁢
,
n
wherein li represents the simplified four-parameter circular profile measuring model;
step 6:
substituting the filtered circular profile data into the simplified four-parameter circular profile measuring model in step 5, accurately estimating the eccentric error of the single stage of rotor by utilizing the simplified four-parameter circular profile measuring model, and obtaining the eccentric error value of the single stage of rotor;
then, establishing a four-target optimization model of the geometry center, the mass center, the center of gravity and the center of inertia of multiple stages of rotors based on the angular orientation mounting position of each stage of rotor by utilizing the vector stack projection theory according to the eccentric error value of the single stage of rotor;
step 7:
inputting the rotor assembly result obtained by the four-target optimization model in step 6 into a BP neural network for prediction so as to compensate for the deviation between the model predicted value and the actual measured value.

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Large-scale high-speed rotary equipment measuring and intelligent learning assembly method and device based on vector minimization of geometry center, mass center, center of gravity and center of inertia

Life of this patent

Large-scale high-speed rotary equipment measuring and intelligent learning assembly method and device based on vector minimization of geometry center, mass center, center of gravity and center of inertia

Life of this patent

Abstract

Claims8