US 11,231,038 B2 · Load identification method for reciprocating machinery based on information entropy and envelope features of axis trajectory of piston rod

Abstract

A load identification method for reciprocating machinery based on information entropy and envelope features of an axis trajectory of a piston rod. According to the present disclosure, firstly, the position of an axial center is calculated according to a triangle similarity theorem to obtain an axial center distribution; secondly, features are extracted from the axial center distribution of the piston rod by means of an improved envelope method for discrete points as well as an information entropy evaluation method; thirdly, a dimensionality reduction is carried out on the features by means of manifold learning to form a set of sensitive features of the load; and finally, a neural network is trained to obtain a load identification classifier to fulfill automatic identification on the operating load of the reciprocating machinery. The advantages of the present disclosure are verified by means of actual data of a piston rod of a reciprocating compressor.

A load identification method for reciprocating machinery based on information entropy and envelope features of an axis trajectory of a piston rod, comprising the following steps:

step 1. setting different load conditions Load={0, d, 2d, 3d, . . . , wd}, w=0, 1, 2, . . . , wherein d represents a load gradient, and the number of the load conditions is (w+1) in total;
respectively acquiring, by an on-line monitoring system of reciprocating machinery, an original deflection displacement Xm={x1, x2, x3, . . . , xm} and original settlement displacement Ym {Y1, Y2, Y3, . . . , ym} of a piston rod in a corresponding load condition through an eddy current displacement sensor in a horizontal direction and an eddy current displacement sensor in a vertical direction to obtain an original data set
XYn={(Xm,Ym)1T (Xm,Ym)2T, . . . , (Xm,Ym)nT}T, wherein m represents the number of sampling points, and n represents the number of data groups;
step 2. removing average values of an original signal Xm in and an original signal Ym by means of formula (1) to obtain X′m={x′1, x′2, x′3, . . . , x′m} and Y′m={y′1, y′2, y′3, . . . , y′m}, wherein the original data set is turned to XY′n={(X′m,Y′m)1T, (X′m,Y′m)2T, . . . , (X′m,Y′m)nT}T
F
m
′
⁡
(
j
)
=
F
m
⁡
(
i
)
-
1
m
⁢
∑
i
=
1
m
⁢
F
m
⁡
(
i
)
⁢
⁢
i
,
j
=
1
,
2
,
…
⁢
,
m
(
1
)
wherein, in formula (1), Fm represents the original deflection or settlement displacement of the piston rod, and F′m represents the deflection or settlement displacement, obtained after the average values are removed, of the piston rod;
and
setting a horizontal direction measured by a deflection sensor as an X-axis and a vertical direction measured by a settlement sensor as a Y-axis to build a plane-rectangular coordinate system, wherein if a position of an axial center of the piston rod at an initial time is denoted by O0(a0,b0), the position of the axial center of the piston rod at another time is denoted by Om(am,bm), and a radius of the piston rod is denoted by R, a point of intersection between a circumference of the piston rod and the X-axis at this time is Jx (R+x′m,0), and a point of intersection between the circumference of the piston rod and the Y-axis at the time is JY (0,R+y′m);
setting an included angle between the X-axis and a line connecting the point Om to the point Jx as θ and an included angle between a line connecting the point Om to the point Jy and a straight line, parallel to the X-axis, on which the point Om is located as φ to derive formula (2) and formula (3) according to a triangle similarity theorem;
and solving, by means of formula (2) in combination with formula (3), the position Om (am, bm) of the axial center of the piston rod at different times to form an axial center distribution set
O
=
{
O
1
⁡
(
a
1
,
b
1
)
,
O
2
⁡
(
a
2
,
b
2
)
,
O
3
⁡
(
a
3
,
⁢
b
3
)
,
…
⁢
,
O
m
⁡
(
a
m
,
b
m
)
}
⁢
⁢
{
R
+
X
m
′
⁡
(
j
)
+
a
m
X
m
′
⁡
(
j
)
=
R
X
m
′
⁡
(
j
)
/
cos
⁢
θ
θ
=
arctan
⁢
b
m
R
+
X
m
′
⁡
(
j
)
+
a
m
(
2
)
{
R
+
Y
m
′
⁡
(
j
)
-
b
m
Y
m
′
⁡
(
j
)
=
R
Y
m
′
⁡
(
j
)
/
sin
⁢
⁢
φ
φ
=
arctan
⁢
R
+
Y
m
′
⁡
(
j
)
-
b
m
a
m
(
3
)
wherein, in formula (2) and formula (3), j=1, 2, 3, . . . , m
step 3. calculating an envelope feature Bao of the axial center distribution O={O1 (a1, b1), O2 (a2, b2), . . . , O3 (a3, b3), . . . , Om (am, bm)} by means of an improved envelope method for a discrete point distribution contour, wherein the improved envelope method for the discrete point distribution contour particularly comprises the following steps:
step 3.1. determining, according to the axial center distribution O, four limit points by seeking a minimum point al and a maximum point ar in the horizontal direction X as well as a minimum point bd and a maximum point bu in the vertical direction Y, wherein the four limit points are respectively denoted by Ol(al,bl),Or (ar,br),Od (ad,bd),Ou (au, bu), an inside of a quadrangle formed by the four limit points is counted as an internal side, and an outside of the quadrangle is counted as an external side;
step 3.2. extracting a convex envelope of an axial center distribution contour at a minimum slope with the foregoing limit points as starting points by anticlockwise traversing all over the positions of the axial center at all times;
and
calculating a convex envelope between a limit point Od and a limit point Or through a method comprising the following steps;
(1) setting a line connecting the point Od to the point Or as L1, wherein a slope α1 of the line is expressed as:
α
1
=
b
d
-
b
r
a
d
-
a
r
(
4
)
(2) if a set of all common axial center points on an external side of the line L1 is assumed as P={p1 (a1,b1), p2 (a2 b2), p3 (a3b3), . . . }, calculating a slope K={β1,β2,β3, . . . } of a line connecting the point Od to any point in P;
if there are multiple points with a same slope, calculating a distance D={dis1,dis2,dis3, . . . } between the corresponding point and the point Od;
and seeking a point p′(ap,bp) in the set P, and enabling p′ to meet the following formula:
β′=minK,β′≤α1, and dis′=maxD (5)
wherein, in formula (5), β′ represents a slope of a line connecting the point p′ to the point Od, and dis′ represents a distance between the point p′ and the point Od;
and the point p′ is a convex envelope point;
(3) replacing the point Od with the convex envelope point p′, setting a line connecting the point p′ to the point Or as L′1 and a slope of the line L′1 as α′1, and then carrying out a next iteration to seek a new convex envelope point;
(4) repeating step (1), step (2), and step (3);
and when a distance between the new convex envelope point and the point Or as 0, stopping the iteration to obtain a convex envelope B′dr={p′1,p′2,p′3, . . . } of an axial center distribution contour between the limit point Od and the limit point Or;
and
calculating a convex envelope B′ru between the limit point Or and a limit point Ou, a convex envelope B′ul between the limit point Ou and a limit point Ol, and a convex envelope B′ld between the limit point Ol and the limit point Od to obtain a set Btu={Od, B′dr,Or,B′ru, Ou, B′ul,Ol,B′ld} of all convex envelope points in the axial center distribution;
(5) calculating an area S1 of the convex envelope of the axial center distribution contour by means of formula (6) below:
S
⁢
1
=
1
2
⁢
∑
e
=
1
c
⁢
1
⁢
(
a
p
⁢
e
*
b
p
⁡
(
e
+
1
)
-
a
p
⁡
(
e
+
1
)
*
b
p
⁢
e
)
(
6
)
wherein, in formula (6), c1 represents the number of the convex envelope points;
step 3.3. calculating a concave envelope of the axial center distribution according to the convex envelope obtained in step 3.2;
and
calculating a concave envelope between the limit point Od and the limit point Or by successively anticlockwise carrying out the following calculation on two continuous convex envelope points p′1(ap1,bp1) and p′2(ap2,bp2) in the convex envelopes B′dr={p′1, p′2, p′3, . . . }:
(1) setting a line connecting the point p′1(ap1,bp1) to the point p′2(ap2,bp2) as L2, wherein a slope α2 of the line L′ is expressed as:
α
2
=
b
p
⁢
⁢
2
-
b
p
⁢
⁢
1
a
p
⁢
⁢
2
-
a
p
⁢
⁢
1
(
7
)
(2) if a set of all common axial center points on an internal side of the line L2 is assumed as Q={q1(a1, b1),q2 (a2, b2),q3(a3,b3), . . . }, calculating a slope K′={β′1,β′2, β′3, . . . } of a line connecting the point p′1 to any point in Q;
if there are multiple points with a same slope, calculating a distance D′={dis′1,dis′2,dis′3, . . . } between the corresponding point and the point p′1;
and seeking a point q′(aq,bq) in the set Q, and enabling q′ to meet the following formula:
β″=min K′,β″≥α2, and dis″=max D′ (8)
wherein, in formula (8), β″ represents a slope of a line connecting the point q′ to the point p′1, and dis″ represents a distance between the point q′ and the point p′1;
and
the point q′ is a concave envelope point;
(3) replacing the point p′2 with the concave envelope point q′, setting a line connecting the point p′1 to the point q′ as L′2 and a slope of the line L′2 as α2, and then carrying out a next iteration to seek a new concave envelope point;
(4) repeating step (1), step (2), and step (3);
and when a distance between the new concave envelope point and the point p′1 is not greater than M, stopping the iteration to obtain a concave envelope B″dr={q′1, q′2, q′3, . . . } of the axial center distribution contour between the limit point Od and the limit point Or;
wherein,
an initial value of M indicates an average distance between two adjacent axial center points, and M is calculated by means of formula (9) in combination with formula (10):
⁢
M
=
2
⁢
M
′
m
⁡
(
m
-
1
)
(
9
)
M
′
=
∑
i
=
2
m
⁢

(
a
1
-
a
i
)
2
+
(
b
1
-
b
i
)
2

+
∑
i
=
3
m
⁢

(
a
2
-
a
i
)
2
+
(
b
1
-
b
i
)
2

+
…
+
∑
i
=
m
m
⁢

(
a
m
-
1
-
a
i
)
2
+
(
b
m
-
1
-
b
i
)
2

(
10
)
calculating a concave envelope B″ru between the limit point Or and a limit point Ou, a concave envelope B″ul between the limit point Ou and a limit point Ol, and a concave envelope B″ld between the limit point Ol and the limit point Od to obtain a set Bao={Od,B″dr,Or,B″ruOu,B″ul,Ol,B″ld} of all concave envelope points in the axial center distribution;
(5) calculating an area S2 of the concave envelope of the axial center distribution contour by means of formula (11) below:
S
⁢
2
=
1
2
⁢
∑
g
=
1
c
⁢
2
⁢
(
a
qg
*
b
q
⁡
(
g
+
1
)
-
a
q
⁡
(
g
+
1
)
*
b
qg
)
(
11
)
wherein, in formula (11), c2 represents the number of the concave envelope points;
step 3.4. determining whether or not the set Bao, obtained in step 3.3, of the concave envelope points is the envelope feature of the axial center distribution of the piston rod;
(1) calculating a relative error E of S2 and S1 by means of formula (12) till E is less than or equal to 5%, wherein the set Bao, obtained in step 3.3, of the concave envelope points is the envelope feature of the axial center distribution of the piston rod after the calculation is stopped;
E
=

S
⁢
⁢
2
-
S
⁢
⁢
1

S
⁢
1
×
100
⁢
%
;
(
12
)
and
(2) in a case where the distance M is reduced by 50% when E is greater than 5%, replacing S1 with S2, repeating step 3.3 to obtain a new set B′ao of the concave envelope points as well as an area S2′ of the concave envelope;
and repeatedly calculating a relative error E′ of S2′ and S2 by means of formula (13) till E′ is less than or equal to 5%, wherein the iteration is stopped at this moment, and the set B′ao obtained in Step 3.3 is the envelope feature of the axial center distribution of the piston rod during the last iteration;
E
′
=

S
⁢
⁢
2
′
-
S
⁢
⁢
2

S
⁢
⁢
2
×
100
⁢
%
(
13
)
step 4. calculating an information entropy feature of the axial center distribution O:
calculating, by means of formula (14), arithmetic square roots of coordinates of all the points in the axial center distribution O to obtain a set Sm={s1 s2, s3, sm} of the arithmetic square roots, and then calculating the information entropy feature Sh of the axial center distribution O by means of formula (15), wherein an initial feature set T={Bao,Sh} is formed by the information entropy feature Sh and the envelope feature;
S
m
⁡
(
i
′
)
=

a
k
2
+
b
k
2

,
k
,
i
′
=
1
,
2
,
…
⁢
,
m
(
14
)
Sh
=
-
∑
i
′
=
1
m
⁢
(

S
m
⁡
(
i
′
)

∑
i
′
=
1
m
⁢

S
m
⁡
(
i
′
)

*
log
2
(

S
m
⁡
(
i
′
)

∑
i
′
=
1
m
⁢

S
m
⁡
(
i
′
)

)
)
(
15
)
step 5. carrying out, by means of t-distributed stochastic neighbor embedding (T-SNE), an unsupervised dimensionality reduction on the initial feature set to extract sensitive features of a load;
and assuming that the initial feature set T includes 1*Col-dimensional features, given perplexity is 30, and a given learning rate is 1e-5, setting a label as Labels={0,1,2, . . . ,w},and then inputting the initial feature set T to a T-SNE algorithm for the unsupervised dimensionality reduction in the (w+1) load conditions to obtain a set T′={t1, t2} of the sensitive features of a 1*2-dimensional load;
and
step 6. firstly, sorting data acquired by the on-line monitoring system in the (w+1) load conditions to form a training set and a test set;
secondly, processing the data in the training set and the test set through the above steps to obtain a final training set Train_T′ and a final test set Test_T′;
and thirdly, setting, according to different reciprocating machinery, the number of neurons of a back-propagation (BP) neural network as 20-30, a learning rate as 0.0005-0.001, training accuracy as 0.0001-0.0005, and maximum iterations as 70-100, then inputting the data set Train_T′ to the BP neural network for training to obtain a classifier capable of distinguishing the (w+1) load conditions of the reciprocating machinery, and testing the classifier of the BP neural network by means of the test set Test_T′.

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Load identification method for reciprocating machinery based on information entropy and envelope features of axis trajectory of piston rod

Life of this patent

Load identification method for reciprocating machinery based on information entropy and envelope features of axis trajectory of piston rod

Life of this patent

Abstract

Claims1