CN104155134B - A kind of determination methods of the Higher Order Cumulants feature extracting method suitability - Google Patents

A kind of determination methods of the Higher Order Cumulants feature extracting method suitability Download PDF

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CN104155134B
CN104155134B CN201410384189.9A CN201410384189A CN104155134B CN 104155134 B CN104155134 B CN 104155134B CN 201410384189 A CN201410384189 A CN 201410384189A CN 104155134 B CN104155134 B CN 104155134B
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fault
extracting method
tau
mechanical equipment
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CN201410384189.9A
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CN104155134A (en
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吴国新
徐小力
蒋章雷
左云波
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北京信息科技大学
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Abstract

The present invention relates to the determination methods of a kind of Higher Order Cumulants feature extracting method suitability, it comprises the following steps: (1) uses rotor testbed analog mechanical equipment normal operating condition, gathers rotor testbed vibration signal under normal operating conditions;(2) utilize rotor testbed analog mechanical equipment minor failure degree, moderate fault degree and three kinds of fault degrees of severe fault degree under a certain fault, and gather rotor testbed vibration signal under three kinds of fault degrees;(3) calculate in all vibration signals and often organize vibration signal 1.5 dimension spectrum;(4) judging whether 1.5 dimension spectrum signature extracting method have sensitivity and tendency for mechanical equipment fault deterioration, the feature extracting method simultaneously meeting sensitivity and tendency is applicable to the mechanical equipment fault trend prediction of this fault.The present invention can accurately judge which kind of fault type 1.5 dimension spectrum signature extracting method are applicable to, and can extensively apply in mechanical equipment fault trend prediction.

Description

A kind of determination methods of the Higher Order Cumulants feature extracting method suitability

Technical field

The present invention relates to a kind of mechanical breakdown extracting method determination methods of the suitability in failure trend prediction, particularly About a kind of Higher Order Cumulants feature extracting method be applicable to mechanical fault diagnosis field to 1.5 dimension spectrum signature extracting method The determination methods of the suitability.

Background technology

Mechanical equipment fault is carried out trend prediction research, is conducive to plant equipment is implemented actively maintenance, reduces economy Loss.Fault signature extracting method is by the key of failure trend prediction research, and therefore, in prior art, people are according to difference Theory propose different fault signature extracting method.Higher Order Cumulants theory is to be applied to fault signature in recent years extract A kind of theoretical method, wherein 1.5 dimension spectrum signature extracting method are that Application comparison is a kind of.But mechanical breakdown type is multiple many Sample, 1.5 dimension spectrum signature extracting method are applicable to any fault type, do not have theoretical foundation, can only the most tentatively judge, Judged result is the most subjective, there is bigger error.

Summary of the invention

For the problems referred to above, it is an object of the invention to provide the judgement of a kind of Higher Order Cumulants feature extracting method suitability Method, the method is that in equipment fault trend prediction research, the selection of feature extracting method provides theoretical basis, can be more Judge which kind of fault type 1.5 dimension spectrum signature extracting method are applicable to accurately.

For achieving the above object, the present invention takes techniques below scheme: a kind of Higher Order Cumulants feature extracting method is suitable for Property determination methods, it comprises the following steps: (1) uses rotor testbed analog mechanical equipment normal operating condition, utilizes existing Data acquisition equipment is had to gather rotor testbed vibration signal x under normal operating conditionsw(n)={ x1,...xN, wherein, N Representing and often organize data amount check, w represents data group, w=1;(2) utilize rotor testbed analog mechanical equipment in a certain fault Under minor failure degree, moderate fault degree and three kinds of fault degrees of severe fault degree, and utilize available data collection to set Standby collection rotor testbed vibration signal x under three kinds of fault degreesw(n)={ x1,...xN, wherein, N represents and often organizes data Number;W represents data group, w=2,3,4, represents minor failure level state, moderate fault degree state and severe respectively Fault degree state;(3) calculate in all vibration signals and often organize vibration signal 1.5 dimension spectrum;(4) assume that plant equipment is properly functioning Under state, the maximum of 1.5 dimension spectrums of vibration signal is S1, the event of plant equipment minor failure degree, moderate fault degree, severe The maximum of 1.5 dimension spectrums under barrier level state is respectively S2、S3And S4, when the maximums of 1.5 dimension spectrums meet following formula:

S 2 - S 1 S 1 ≥ 50 % ,

Then it is judged as that 1.5 dimension spectrum signature extracting method have sensitivity for mechanical equipment fault deterioration;If 1.5 dimension spectrums Maximum meets S1< S2< S3< S4, then it is judged as that 1.5 dimension spectrum signature extracting method have for mechanical equipment fault deterioration Gesture;The feature extracting method simultaneously meeting sensitivity and tendency is applicable to the mechanical equipment fault trend prediction of this fault.

In described step (3), often the calculation procedure of group vibration signal 1.5 dimension spectrum is as follows: I) every by all vibration signals In group data, N number of data are all divided into K section, every section of M data, and every segment data is as a record;II) each record is carried out Go average, then calculate three-order cumulant, obtain three-order cumulant meansigma methods;III) to Third-order cumulants Diagonal slices meansigma methods does one-dimensional Fourier transform, obtains 1.5 dimension spectrum S of vibration signalw,3xr) it is:

In formula, ωrRepresent frequency, r=1,2 ... N, N are positive integer; (τ, τ) is three-order cumulant meansigma methods;

τ is time delay;3x represents Third-order cumulants.

In described step II) in, it specifically comprises the following steps that (a) assumesIt is i-th record, wherein, i=1, ... K, h=0,1 ... M-1;Its three-order cumulant is sought in i-th recordFor:

x w , 3 x i ( τ , τ ) = 1 M Σ h = M 1 h = M 2 x w i ( h ) x w i ( h + τ ) x w i ( h + τ ) ,

In formula, M1=max (0 ,-τ);M2=min (M-1, M-1-τ), τ are time delay;B () is to all Third-order cumulants diagonal angles SectionAverage, obtain meansigma methodsFor:

c ^ w , 3 x ( τ , τ ) = 1 K Σ i = 1 K c w , 3 x i ( τ , τ ) .

Due to the fact that and take above technical scheme, it has the advantage that the maximum that due to the fact that according to 1.5 dimension spectrums Its failure trend prediction whether being applicable to certain fault type is judged by value, uses in its judged result and prior art Micro-judgment compare as accurately, can be that in equipment fault trend prediction research, the selection of feature extracting method provides theory Basis, solves the problem that in failure trend prediction research, the selection of feature extracting method lacks theoretical foundation.The present invention is permissible Extensively apply in mechanical equipment fault trend prediction.

Accompanying drawing explanation

Fig. 1 is the overall flow schematic diagram of the present invention.

Detailed description of the invention

With embodiment, the present invention is described in detail below in conjunction with the accompanying drawings.

As it is shown in figure 1, the present invention provides the determination methods of a kind of Higher Order Cumulants feature extracting method suitability, the method Being mainly used in 1.5 dimension spectrum signature extracting method determination methods of the suitability in failure trend prediction, it comprises the following steps:

(1) use rotor testbed analog mechanical equipment normal operating condition, utilize available data collecting device collection to turn Sub-laboratory table vibration signal x under normal operating conditionsw(n)={ x1,...xN, wherein, N represents and often organizes data amount check, w generation Table data group, w=1, represent normal operating condition.

(2) the rotor testbed analog mechanical equipment three kinds of fault degrees under a certain fault are utilized: minor failure journey Degree, moderate fault degree and severe fault degree, and utilize available data collecting device to gather rotor testbed three kinds of faults Vibration signal x under degreew(n)={ x1,...xN, wherein, N represents and often organizes data amount check;W represents data group, w=2,3, 4, represent minor failure level state, moderate fault degree state and severe fault degree state respectively.

(3) calculate in all vibration signals and often organize vibration signal 1.5 dimension spectrum, specifically comprise the following steps that

I) N number of data in the often group data of all vibration signals are all divided into K section, every section of M data, every segment data conduct One record.

II) each record is gone average, then calculate three-order cumulant, obtain Third-order cumulants diagonal angle Section meansigma methods, its step is as follows:

A () is assumedBe i-th (i=1 ... K) individual record, wherein, and h=0,1 ... M-1;It is asked in i-th record Three-order cumulantFor:

x w , 3 x i ( τ , τ ) = 1 M Σ h = M 1 h = M 2 x w i ( h ) x w i ( h + τ ) x w i ( h + τ ) ,

In formula, M1=max (0 ,-τ);M2=min (M-1, M-1-τ), τ are time delay;3x represents Third-order cumulants.

B () is to all three-order cumulantAverage, obtain meansigma methodsFor:

c ^ w , 3 x ( τ , τ ) = 1 K Σ i = 1 K c w , 3 x i ( τ , τ ) ;

III) to three-order cumulant meansigma methodsDo one-dimensional Fourier transform, obtain vibration signal 1.5 dimension spectrum Sw,3xr) it is:

S w , 3 x ( ω r ) = Σ τ = - ∞ ∞ c ^ w , 3 x ( τ , τ ) e - jωτ ,

In formula, ωrRepresent frequency, r=1,2 ... N, N are positive integer.

(4) 1.5 dimension spectrum S of vibration signal under plant equipment normal operating condition are assumedw,3xr) maximum be S1, machine 1.5 dimension spectrum S under tool equipment minor failure degree, moderate fault degree, severe fault degree statew,3xr) maximum divide Wei S2、S3And S4

If 1.5 dimension spectrum Sw,3xr) maximum meet following formula:

S 2 - S 1 S 1 ≥ 50 % , - - - ( 1 )

Then it is judged as that 1.5 dimension spectrum signature extracting method have sensitivity for mechanical equipment fault deterioration;

If 1.5 dimension spectrum Sw,3xr) maximum meet following formula:

S1< S2< S3< S4, (2)

Then it is judged as that 1.5 dimension spectrum signature extracting method have tendency for mechanical equipment fault deterioration.Meet quick simultaneously The feature extracting method of perception and tendency is applicable to the mechanical equipment fault trend prediction of this fault, and this judged result is relatively as the criterion Really.

The various embodiments described above are merely to illustrate the present invention, and the structure of the most each parts, connected mode etc. are all can be Change, every equivalents carried out on the basis of technical solution of the present invention and improvement, the most should not get rid of the present invention's Outside protection domain.

Claims (3)

1. a determination methods for the Higher Order Cumulants feature extracting method suitability, the method is mainly used in 1.5 dimension spectrum signatures Extracting method is the determination methods of the suitability in failure trend prediction, and it comprises the following steps:
(1) use rotor testbed analog mechanical equipment normal operating condition, utilize available data collecting device to gather rotor real Test platform vibration signal x under normal operating conditionsw(n)={ x1,...xN, wherein, N represents and often organizes data amount check, and w represents number According to group, w=1;
(2) utilize rotor testbed analog mechanical equipment minor failure degree under a certain fault, moderate fault degree and Three kinds of fault degrees of severe fault degree, and utilize available data collecting device to gather rotor testbed under three kinds of fault degrees Vibration signal xw(n)={ x1,...xN, wherein, N represents and often organizes data amount check;W represents data group, w=2,3,4, difference Represent minor failure level state, moderate fault degree state and severe fault degree state;
(3) calculate in all vibration signals and often organize vibration signal 1.5 dimension spectrum;
(4) assume that the maximum that 1.5 dimensions of vibration signal under plant equipment normal operating condition are composed is S1, plant equipment slightly event The maximum of 1.5 dimension spectrums under barrier degree, moderate fault degree, severe fault degree state is respectively S2、S3And S4, when 1.5 dimensions The maximum of spectrum meets following formula:
S 2 - S 1 S 1 ≥ 50 % ,
Then it is judged as that 1.5 dimension spectrum signature extracting method have sensitivity for mechanical equipment fault deterioration;If the maximum of 1.5 dimension spectrums Value meets S1< S2< S3< S4, then it is judged as that 1.5 dimension spectrum signature extracting method have trend for mechanical equipment fault deterioration Property;The feature extracting method simultaneously meeting sensitivity and tendency is applicable to the mechanical equipment fault trend prediction of this fault.
The determination methods of a kind of Higher Order Cumulants feature extracting method suitability the most as claimed in claim 1, it is characterised in that: In described step (3), often the calculation procedure of group vibration signal 1.5 dimension spectrum is as follows:
I) N number of data in the often group data of all vibration signals being all divided into K section, every section of M data, every segment data is as one Record;
II) each record is gone average, then calculate three-order cumulant, obtain three-order cumulant Meansigma methods;
III) three-order cumulant meansigma methods is done one-dimensional Fourier transform, obtain 1.5 dimension spectrum S of vibration signalw,3xr) it is:
S w , 3 x ( ω r ) = Σ τ = - ∞ ∞ c ^ w , 3 x ( τ , τ ) e - j ω τ ,
In formula, ωrRepresent frequency, r=1,2 ... N, N are positive integer;For three-order cumulant meansigma methods; τ is time delay;3x represents Third-order cumulants.
The determination methods of a kind of Higher Order Cumulants feature extracting method suitability the most as claimed in claim 2, it is characterised in that: In described step II) in, it specifically comprises the following steps that
A () is assumedIt is i-th record, wherein, i=1 ... K, h=0,1 ... M-1;I-th record is asked its three rank Cumulant diagonal slicesFor:
x w , 3 x i ( τ , τ ) = 1 M Σ h = M 1 h = M 2 x w i ( h ) x w i ( h + τ ) x w i ( h + τ ) ,
In formula, M1=max (0 ,-τ);M2=min (M-1, M-1-τ), τ are time delay, and 3x represents Third-order cumulants;
B () is to all three-order cumulantAverage, obtain meansigma methodsFor:
c ^ w , 3 x ( τ , τ ) = 1 K Σ i = 1 K c w , 3 x i ( τ , τ ) .
CN201410384189.9A 2014-08-06 2014-08-06 A kind of determination methods of the Higher Order Cumulants feature extracting method suitability CN104155134B (en)

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