CN109190287B - Rotating machinery operation stability online monitoring method facing time-varying working conditions - Google Patents

Rotating machinery operation stability online monitoring method facing time-varying working conditions Download PDF

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CN109190287B
CN109190287B CN201811108921.4A CN201811108921A CN109190287B CN 109190287 B CN109190287 B CN 109190287B CN 201811108921 A CN201811108921 A CN 201811108921A CN 109190287 B CN109190287 B CN 109190287B
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卢国梁
王晓峰
闫鹏
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Shandong University
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Abstract

The invention relates to an online monitoring method for the running stability of a rotating machine facing time-varying working conditions, which comprises the following steps: step 1, dynamic period division, namely, performing dynamic period division on a rotating machine operation real-time state signal to obtain a divided real-time period signal; step 2, model calculation: calculating to obtain a structure average model based on the optimal curved path; step 3, judging the stability state: and comparing the structural average model obtained by calculation with the signal actually observed at the current moment, quantizing the stability index, and judging the stability state of the machine at the current moment by adopting a Gaussian distribution statistical index-based hypothesis testing method. The method can solve the problem of difficulty in stability monitoring caused by time-varying working conditions, and has good potential in practical engineering application.

Description

Rotating machinery operation stability online monitoring method facing time-varying working conditions
Technical Field
The invention relates to the technical field of mechanical operation stability monitoring methods, in particular to a time-varying working condition-oriented online monitoring method for the operation stability of a rotary machine.
Background
At present, rotary machines are developed towards the direction of high speed, large-scale and automation, the structures of the rotary machines are more and more complex, the functions of the rotary machines are more and more perfect, different parts of the same equipment are mutually associated and tightly coupled, and the different equipment are also in tight connection. The stability monitoring of the operation of the rotary machine is particularly important because a part of the unstable part can generate chain reaction to influence the whole production chain. The online monitoring of the running stability of the rotary machine can send an alarm when the state of the mechanical stability changes, so that measures can be taken in time to avoid further degradation of the stability, the safety can be improved, and the reliability, safety, quality, productivity and the like of equipment and products can be improved.
The main problem of the stability monitoring of the rotating machinery is that the stability of the rotating machinery is in a non-stable state due to the existence of unavoidable factors such as load fluctuation of the equipment, the lubrication degree of the equipment, and field environment interference, and the time-varying working condition brings great difficulty to the monitoring of the running stability of the rotating machinery, such as selecting a mathematical model for describing the mechanical stability, determining model parameters, judging whether the stability state changes, and the like.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide the rotating machine operation stability on-line monitoring method facing the time-varying working condition, and a nonparametric model (a structure average model) is adopted to describe the operation stability of the rotating machine, so that the difficulty of the rotating machine on-line monitoring operation stability caused by the varying working condition is solved.
In order to achieve the purpose, the invention adopts the following technical scheme:
an online monitoring method for the operation stability of a rotating machine facing time-varying working conditions comprises the following steps:
step 1, dynamic period division, namely periodically dividing a rotating machine operation real-time state signal to obtain a divided real-time period signal;
step 2, model calculation: and calculating to obtain a structure average model based on the optimal curved path.
Step 3, judging the stability state: and comparing the structural average model obtained by calculation with the signal actually observed at the current moment, quantizing the stability index, and judging the stability state of the machine at the current moment by adopting a Gaussian distribution statistical index-based hypothesis testing method.
Further, the specific steps of step 1 are as follows:
determining a theoretical period of the rotating machine, determining a first period data segment f1Introducing the offset of the start time and the error of the cycle length, and combining the corresponding data in the range of the start point and the length of the second cycle to obtain different data segments
Figure BDA0001808586830000021
Calculating f using a dynamic time warping algorithm1And
Figure BDA0001808586830000022
to obtain the minimum matching loss
Figure BDA0001808586830000023
For the corresponding second period data segment f2，
Figure BDA0001808586830000024
Repeating the steps to divide the signal data { f (t) } collected at the monitoring time t into a series of m data segments with unequal periods { f1,f2,…,fmWhere m is a natural number.
Further, in step 1, the specific calculation method of the matching loss is that two periods of two-segment data a ═ a1,a2,…,anB ═ B1，b2，…bkComputing a distance matrix, the values of which are each pair of observations (a)h，bj) The path with the minimum euclidean distance in the distance matrix is the optimal curved path, and the cumulative distance at the end of the path is matching loss DTW (a, B) ═ d (a, B)n，bk)，
Wherein:
Figure BDA0001808586830000025
where DTW denotes the matched loss algorithm, A, B denotes the two-cycle data segment, ah、bjRepresenting data values, | a, within a data segmenth,bjL is ahAnd bjWhere h is {1 … n }, j is {1 … k }, and n and k are natural numbers.
Further, the specific steps of step 2 are:
step 1) selecting a reference period.
Step 2) after the reference period is selected, matching each of the rest periods with the reference period, and finding out the optimal curved path h matched with the reference period in each periodi(t) calculating an optimal curved path h (t) matching the reference period converted into the average time domain with the originally selected reference period:
Figure BDA0001808586830000026
step 3), path conversion: correcting the optimal curved path matched with the reference period in each period, and recording the corrected optimal curved path as ui
ui≡hi(h-1(t))，
Wherein h is-1(t) is the inverse of h (t).
Step 4) calculating an average structure model, converting each period into a common time domain according to the corrected optimal curved path of each period data segment, and solving the structure average model at the monitoring time t
Figure BDA0001808586830000031
Figure BDA0001808586830000032
In the steps 2) to 4), i is 1,2,3 … m; m is a natural number; f. ofi(ui) Is an optimal curved path u matched by correctioniThe corresponding relation of (a) is the data value in the i-th cycle under the average time domain searched in the original i-th cycle, fi(ui) F for each cycle for a series of valuesi(ui) Summing the points with the same order and averaging to obtain an average structure model
Figure BDA0001808586830000033
Further, the specific steps of step 1) are as follows:
step a: and selecting any period as a temporary reference period.
Step b: and matching each of the rest periods with the selected temporary reference period through a dynamic time warping algorithm, calculating matching loss, and summing the calculated matching loss to represent the total consumption of the selected temporary reference period.
Step c: and selecting the corresponding temporary reference period with the maximum total consumption as the finally selected reference period.
Further, in the step 2), an interpolation method is adopted to match each period with the optimal curved path h of the temporary reference periodi(t) are normalized to the same time range (0, 1).
Further, the specific steps of step 3 are:
step I, obtaining a stability index sequence { s) of normal m periodic data segments before the monitoring time t by a matching loss calculation method1,s2,…,smTherein of
Figure BDA0001808586830000034
Wherein e ∈ (3,4, … m), s1＝DTW(f1，f1)＝0，s2＝DTW(f1，f2)。
Step II, calculating the actually observed periodic data f at the current momentm+1Stability index s ofm+1
Figure BDA0001808586830000035
And step III, judging whether the stability state changes in the (m + 1) th period by adopting a common Gaussian distribution statistical index and hypothesis test:
Figure BDA0001808586830000041
Figure BDA0001808586830000042
wherein H0Indicating a change in stability state at the m +1 th cycle, H1Indicates that the stability state has not changed in the m +1 th cycle, wherein
Figure BDA0001808586830000043
Is a sequence s1,s2,…,smMean of samples of }, σmIs a sequence s1,s2,…,smThe sample variance of.
The invention has the beneficial effects that:
the method for monitoring the running stability of the rotating machinery on line facing the time-varying working condition utilizes the nonparametric model of the structure average model to describe the stability of the machinery, quantizes the stability and judges the stability state based on statistical analysis, and eliminates the problem of unequal period data lengths caused by the time-varying working condition by utilizing the transmissibility of a dynamic time warping algorithm on the basis of the optimal curved path, thereby effectively solving the difficulty of online monitoring the stability caused by the time-varying working condition and having good potential in practical engineering application.
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The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of the matching process of the DTW algorithm of the present invention;
FIG. 3 is a schematic diagram of OWP time warping process according to the present invention;
FIG. 4 is a schematic diagram of SAM computation according to the present invention;
FIG. 5 is a schematic diagram showing vibration signals of a transmission according to example 1 of application of the present invention;
FIG. 6 is a diagram illustrating an application example 1 of the present invention after cycle division;
FIG. 7 is a schematic diagram showing stability indexes of application example 1 of the present invention;
FIG. 8 is a schematic diagram of a vibration signal at a blade end of a motor according to example 2 of the present invention;
FIG. 9 is a diagram illustrating an application example 2 of the present invention after cycle division;
FIG. 10 is a schematic diagram showing stability indexes of example 2 of application of the present invention;
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, 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.
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.
As introduced in the background art, the main problem of the monitoring of the stability of the rotating machine is that the stability of the rotating machine is in a non-steady state due to the existence of unavoidable factors such as load fluctuation of equipment, lubrication degree of the equipment, and interference of a field environment, and the monitoring of the operating stability of the rotating machine under the time-varying working condition brings great difficulty.
In an exemplary embodiment of the present application, as shown in fig. 1, an online monitoring method for operation stability of a rotating machine facing a time-varying working condition, which uses a Structural Average Model (hereinafter referred to as SAM) to describe the operation stability of the machine, includes the following steps:
step 1, dynamic period division, namely periodically dividing the running real-time state signal of the rotating machine to obtain a divided real-time period signal.
Step 2, model calculation: and calculating to obtain a structure average model based on the optimal curved path.
Step 3, judging the stability state: and comparing the structural average model obtained by calculation with the signal actually observed at the current moment, quantizing the stability index, and judging the stability state of the machine at the current moment by adopting a Gaussian distribution statistical index-based hypothesis testing method.
The specific steps of the step 1 are as follows:
determining the theoretical period P of the rotating machine from the inherent kinematic relationship of the rotating machine or from a frequency domain analysis1To thereby determine a first periodic data segment f1＝{1，…，P1Introduce an initial time offset △ P and a length error δ to determine what is possibleSecond period data section of
Figure BDA0001808586830000051
I.e. determining the second period start existence range P1+1-△P,…,P1+1+ △ P and a second period length range { P }1-δ,…,P1+ delta, combining the corresponding data in the range of possible starting points and lengths to obtain different data
Figure BDA0001808586830000052
Calculating f1And
Figure BDA0001808586830000053
determining the condition of minimum matching loss
Figure BDA0001808586830000054
Corresponding case is f2Namely:
Figure BDA0001808586830000055
by repeating the steps, the signal { f (t) } collected at the monitoring time t can be segmented into a series of data segments { f (t) } with different lengths1,f2,…,fm}。
The specific calculation steps of the matching loss are as follows:
for two periods of data segment a ═ a1,a2,…,anB ═ B1，b2，…bkMatching with dynamic time warping requires the calculation of a distance matrix whose values are each pair of observations (a)h，bj) Where h is {1 … n }, j is {1 … k }, the path with the smallest euclidean distance in the distance matrix is the optimal curved path (OWP), the accumulated distance at the end of the path is the matching loss, and the formula of the matching loss is:
DTW(A，B)＝d(an，bk) (2)
wherein:
Figure BDA0001808586830000061
DTW denotes the match-loss algorithm of the dynamic warping algorithm, A, B denotes the data segment of two cycles, ah、bjRepresenting data values, | a, within a data segmenth,bjL is ahAnd bjWhere h is {1 … n }, j is {1 … k }, and n and k are natural numbers.
The specific steps of the step 2 are as follows:
step 1) selecting a reference period;
the reference period should have a typical pattern of divided periods, and the selection of the reference period comprises the steps of:
step a: selecting an arbitrary period fiAs a temporary reference period.
Step b: every other period f is divided by a dynamic time warping algorithmq(i ≠ q) is associated with a temporary reference period fiMatching is carried out, matching loss is calculated, the calculation method is the same as that in the step 1, and the calculated matching loss is summed to represent the temporary reference period fiThe total consumption of (D) is denoted asi
Figure BDA0001808586830000062
Step c: selecting the temporary reference period corresponding to the maximum total loss as the final reference period fe
Figure BDA0001808586830000063
Step 2) after the reference period is selected, every remaining period f is set as shown in FIG. 2iWith a reference period feMatching is carried out, OWP that each period is matched with the reference period is found, and the corresponding relation is recorded as hi(t) calculating an optimal curved path h (t) matching the reference period converted into the average time domain with the originally selected reference period:
Figure BDA0001808586830000071
as shown in FIG. 3, the length of each period data segment is different from hiThe time ranges of (t) are also different and are normalized to the same time range (0,1) by interpolation.
Step 3), path conversion: the matching path h obtained abovei(t) is relative to feIn the time domain, in order to obtain a matching path of each period of the average time domain and the reference period, hi(t) corrected and recorded as ui
ui≡hi(h-1(t)) (7)
Wherein h is-1(t) is the inverse correspondence of h (t);
step 4) calculating SAM, and according to the corrected optimal curved path matched with each period data segment, dividing each period f into a plurality of periods fiSwitching to a common time domain, and calculating SAM at monitoring time t and recording as
Figure BDA0001808586830000072
Figure BDA0001808586830000073
In the steps 2) to 4), i is 1,2,3 … m, q is 1,2,3 … m, fi(ui) Is an optimal curved path u matched by correctioniThe corresponding relation of (a) is the data value in the i-th cycle under the average time domain searched in the original i-th cycle, fi(ui) F for each cycle for a series of valuesi(ui) Summing the points with the same order and averaging to obtain an average structure model
Figure BDA0001808586830000074
In one embodiment of the present application, as shown in FIG. 4, the period is divided into 3 periods, each of which is f1、f2And f3Calculated, ginsengThe examination period is selected as f2，f1、f3Respectively and f2After matching, OWP matched with the reference period is obtained, and the corresponding relations are h1And h3Corrected to u1And u3Then calculating to obtain SAM
Figure BDA0001808586830000075
Wherein f'2Representing the reference period after conversion to the average time domain, A, B, C, E, F, G, H, I, K, M, N in fig. 4 represents data, and the numbers on one side thereof represent the numbers of the data.
In the step 3, the stability is quantified by comparing the obtained SAM with the currently observed data, the stability of the currently monitored data is judged by using a gaussian distribution statistical index and hypothesis test method in combination with the stability index sequence of the historical sample, whether the stable state of the currently monitored data changes or not is determined, if the stable state of the currently monitored data changes, the currently monitored data is marked and an alarm is given, and if the stable state of the currently monitored data does not change, the currently monitored data is supplemented into the historical sample, and the SAM is recalculated, so that the data in the next period can be judged. The method comprises the following specific steps:
step I, obtaining corresponding structure average models of m periods by adopting the methods of the step 1 to the step 2
Figure BDA0001808586830000081
Obtaining the stability index sequence { s) of the normal periodic data segment before the monitoring time t by using the algorithm of the matching loss1,s2,…,smTherein of
Figure BDA0001808586830000082
Wherein e is epsilon (,3,4, … m), s1＝DTW(f1，f1)＝0，s2＝DTW(f1，f2)。
Step II, calculating the actually observed periodic data f at the current momentm+1Stability index S ofm+1
Figure BDA0001808586830000083
Step III, adopting a common Gaussian distribution statistical index +/-3 sigma and hypothesis test to judge whether the stability state changes in the (m + 1) th period:
Figure BDA0001808586830000084
Figure BDA0001808586830000085
wherein H0Indicating a change in stability state at the m +1 th cycle, H1Indicates that the stability state has not changed in the m +1 th cycle, wherein
Figure BDA0001808586830000086
Is a sequence s1,s2,…,smMean of samples of }, σmIs a sequence s1,s2,…,smThe sample variance of.
In an embodiment 1 of the invention, the original mechanical signal is a vibration signal of the gearbox, initially the rotational speed v of the engine0Is 350rpm/s and then the rotational speed is increased to vtAt 400rpm/s, signals reflecting the change of the rotating speed collected by the gearbox in real time are shown in fig. 5, and as shown in fig. 6, the original signals are periodically divided according to the method described in step 1. Fig. 7 shows the calculated stability index, and the marked position is the position of the rotation speed variation period detected by the method of the present invention, and an alarm is issued.
In another application example 2 of the present invention, as shown in fig. 8, the vibration signal is measured at the end of the blade of the motor, the bearing state of the end of the blade is from the normal state to the early failure state, the result shown in fig. 9 is obtained by dividing the original signal periodically according to the method described in step 1, fig. 10 is the calculated stability index, and the mark in the figure is the position of the early failure occurrence period detected by the method of the present invention, and an alarm is given.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.

Claims (6)

1. An online monitoring method for the operation stability of a rotating machine facing time-varying working conditions is characterized by comprising the following steps:
step 1, dynamic period division, namely periodically dividing a rotating machine operation real-time state signal to obtain a divided real-time period signal;
step 2, model calculation: calculating to obtain a structure average model based on the optimal curved path;
the specific steps of the step 2 are as follows:
step 1) selecting a reference period;
step 2) after the reference period is selected, matching each of the rest periods with the reference period by using a dynamic time warping algorithm, and finding out the optimal curved path h matched with the reference period in each periodi(t) calculating an optimal curved path h (t) matching the reference period converted into the average time domain with the originally selected reference period:
Figure FDA0002409310440000011
step 3), path conversion: correcting the optimal curved path matched with the reference period in each period, and recording the corrected optimal curved path as ui
ui≡hi(h-1(t))，
Wherein h is-1(t) is the inverse correspondence of h (t);
step 4) calculating an average structure model, converting each period into a common time domain according to the corrected optimal curved path of each period data segment, and solving the structure average at the monitoring time tUniform model
Figure FDA0002409310440000012
Figure FDA0002409310440000013
In the steps 2) to 4), i is 1,2,3 … m; m is a natural number; f. ofi(ui) Is through the modified matching path uiThe corresponding relation of (a) is the data value in the i-th cycle under the average time domain searched in the original i-th cycle, fi(ui) F for each cycle for a series of valuesi(ui) Summing the points with the same order and averaging to obtain an average structure model
Figure FDA0002409310440000014
Step 3, judging the stability state: and comparing the structural average model obtained by calculation with the signal actually observed at the current moment, quantizing the stability index, and judging the stability state of the machine at the current moment by adopting a Gaussian distribution statistical index-based hypothesis testing method.
2. The online monitoring method for the operation stability of the rotating machine facing the time-varying working conditions as claimed in claim 1, wherein the specific steps of the step 1 are as follows:
determining a theoretical period of the rotating machine, determining a first period data segment f1Introducing the offset of the start time and the error of the cycle length, and combining the corresponding data in the range of the start point and the length of the second cycle to obtain different data segments
Figure FDA0002409310440000021
Calculating f using a dynamic time warping algorithm1And
Figure FDA0002409310440000022
is reduced byConsuming, obtaining minimum matching loss
Figure FDA0002409310440000023
For the corresponding second period data segment f2，
Repeating the steps to divide the signal data { f (t) } collected at the monitoring time t into a series of m data segments with unequal periods { f1,f2,…,fmWhere m is a natural number.
3. The method for on-line monitoring of the operation stability of the rotating machine facing the time-varying working conditions as claimed in claim 2, wherein the specific calculation method of the matching loss is that two-segment data a of two periods is { a ═ b }1,a2,…,anB ═ B1，b2，…bkComputing a distance matrix, the values of which are each pair of observations (a)h，bj) The path with the minimum euclidean distance in the distance matrix is the optimal curved path, and the cumulative distance at the end of the path is matching loss DTW (a, B) ═ d (a, B)n，bk)，
Wherein:
Figure FDA0002409310440000024
DTW denotes the matched loss algorithm, A, B denotes the data segment of two cycles, ah、bjRepresenting data values, | a, within a data segmenth,bjL is ahAnd bjWhere h is {1 … n }, j is {1 … k }, and n and k are natural numbers.
4. The online monitoring method for the operation stability of the rotating machine facing the time-varying working conditions as claimed in claim 1, wherein the specific steps of the step 1) are as follows:
step a: selecting any period as a temporary reference period;
step b: matching each of the rest periods with the selected temporary reference period through a dynamic time warping algorithm, calculating matching loss, and summing the calculated matching loss to represent the total consumption of the selected temporary reference period;
step c: and selecting the corresponding temporary reference period with the maximum total consumption as the finally selected reference period.
5. The on-line monitoring method for the operation stability of the rotating machine facing the time-varying working conditions as claimed in claim 1, wherein in the step 2), the optimal curved path h for matching each period with the reference period by interpolationi(t) are normalized to the same time range (0, 1).
6. The online monitoring method for the operation stability of the rotating machine facing the time-varying working conditions as claimed in claim 2, characterized by comprising the following steps:
step I, obtaining a stability index sequence { s) of normal m periodic data segments before the monitoring time t by a matching loss calculation method1,s2,…,smTherein of
Figure FDA0002409310440000031
Wherein e ∈ (3,4, … m), s1＝DTW(f1，f1)＝0，s2＝DTW(f1，f2)；
Step II, calculating the actually observed periodic data f at the current momentm+1Stability index s ofm+1
Figure FDA0002409310440000032
And step III, judging whether the stability state changes in the (m + 1) th period by adopting a common Gaussian distribution statistical index and hypothesis test:
Figure FDA0002409310440000033
Figure FDA0002409310440000034
wherein H0Indicating a change in stability state at the m +1 th cycle, H1Indicates that the stability state has not changed in the m +1 th cycle, wherein
Figure FDA0002409310440000035
Is a sequence s1,s2,…,smMean of samples of }, σmIs a sequence s1,s2,…,smThe sample variance of.
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