CN103245518A - Rotary mechanical fault diagnosis method based on differential local mean decomposition - Google Patents

Abstract

The invention discloses a rotary mechanical fault diagnosis method based on differential local mean decomposition (DLMD), which comprises the steps as follows: adopting an acceleration sensor to test rotary mechanical equipment, and collecting and obtaining a vibration signal of the test rotary mechanical equipment; performing DLMD on the obtained acceleration vibration signal, and obtaining a plurality of PF components and residual components; and calculating an instantaneous frequency and an instantaneous amplitude of each PF component, and extracting fault characteristics. According to the method disclosed by the invention, firstly the collected signal is subjected to k-stage differentiation, then the signal subjected to differentiation is subjected to DLMD, and each PF component after the decomposition is circularly subjected to one-time integration and one-stage local mean decomposition until the circulation is performed for k times and m PF components and residual components are obtained; and the fault characteristics are extracted based on the result of DLMD, so that false interfering frequency in a traditional local mean decomposition process can be effectively restrained, and the rotary mechanical fault diagnosis is achieved.

Description

Rotary machinery fault diagnosis method based on the decomposition of differential local mean value

Technical field

The invention belongs to a kind of rotary machinery fault diagnosis method of mechanical engineering field.Specifically about a kind of rotary machinery fault diagnosis method that decomposes based on the differential local mean value.

Background technology

Progress along with science and technology, the development of process of industrialization, rotating machinery is in social production and in servicely account for to such an extent that proportion is increasing, in a single day fault has appearred in rotating machinery, the process of producing will directly be influenced, can bring enormous economic loss, so the diagnosis of rotating machinery fault has been subjected to attention in scientific research.

The diagnosis of rotating machinery fault is judged its running status by analyzing its vibration signal often, and its vibration signal is non-stationary, nonlinear properties, this non-stationary nonlinear properties can effectively be handled and analyze to time frequency analysis method, so obtained using widely.Using more Time-Frequency Analysis Method at present has: methods such as wavelet transformation, Wigner-Ville distribution, EMD decomposition.But this several method has weak point separately, and what wavelet transformation carried out time frequency plane is that a kind of lattice type of machinery decomposes, and this decomposition does not possess adaptivity.Wigner-Ville distributes owing to be the quadratic form time-frequency representation, exists cross term to disturb for many component signals.It is a kind of adaptive signal processing method that EMD decomposes, and especially is fit to handle non-stationary, nonlinear properties, and its research that is used for rotary machinery fault diagnosis at present is many, but shortcoming such as it also exists that mode is obscured, end effect, iterations are many.It is emerging Time-Frequency Analysis Method in recent years that local mean value is decomposed (LMD).The characteristics of this method be can picked up signal at any time time-frequency distributions and the instantaneous frequency of physical significance is arranged, for mechanical fault diagnosis provides a kind of new fault signature extracting method.But LMD decomposes the problem that often there is false frequency in the failure-frequency result of back gained, and this can influence the Fault Diagnosis result.

Summary of the invention

The present invention has overcome deficiency of the prior art, and a kind of rotary machinery fault diagnosis method that decomposes (DLMD) based on the differential local mean value is provided.

In order to solve the technical matters of above-mentioned existence, the present invention proposes a kind of improved signal processing algorithm on the LMD basis, overcome the traditional deficiency of LMD method in rotary machinery fault diagnosis.It is a kind of improved signal processing algorithm that proposes on the LMD basis---the differential local mean value is decomposed (DLMD), this method is on the basis of LMD, the differential that incorporates and the calculating process of integration finally can resolve into a plurality of PF components and remaining component sum to the non-stationary of a complexity, nonlinear properties.

To achieve these goals, technical scheme of the present invention is: a kind of rotary machinery fault diagnosis method based on differential local mean value decomposition (DLMD), its content comprises the steps:

(1) adopts acceleration transducer test rotating machinery, gather and obtain its vibration signal;

(2) the acceleration vibration signal to obtaining carries out DLMD and decomposes, and obtains some PF components and remaining component;

(3) obtain instantaneous frequency and the instantaneous amplitude of each PF component, extract fault signature.

The above-mentioned rotary machinery fault diagnosis method based on differential local mean value decomposition (DLMD), in step (two), the described process that the acceleration vibration signal is carried out the DLMD decomposition, its content may further comprise the steps:

1) to former starting acceleration vibration signal x ₀(t) carry out k rank differential, obtain x ^(k)(t);

2) ask the local mean value function m ₁₁(t), find out x ^(k)(t) all Local Extremum n _i, obtain the mean value m of all adjacent Local Extremum _i, with all adjacent mean point m of the bundle of lines _iCouple together, and then with moving average method it is carried out smoothing processing, can obtain the local mean value function m ₁₁(t);

3) ask envelope estimation function a ₁₁(t), calculate adjacent two envelope estimated value a _i, with all adjacent two envelope estimated value a of the bundle of lines _iCouple together, adopt the running mean method that it is carried out smoothing processing then, can obtain envelope estimation function a ₁₁(t);

4) from signal x ^(k)(t) isolate the local mean value function m in ₁₁(t), obtain h ₁₁(t);

5) use h ₁₁(t) divided by envelope estimation function a ₁₁(t), to h ₁₁(t) carry out demodulation, can get s ₁₁(t),

6) equally to s ₁₁(t) also repeat above-mentioned steps 2)～5), up to s _1n(t) become pure FM signal, i.e. a s _1n(t) envelope estimation function a _{1 (n+1)}(t)=1;

7) can obtain envelope signal (instantaneous amplitude function) a to all envelope estimation functions that produce in the iterative process mutually at convenience ₁(t);

8) pure FM signal s _1n(t) multiply by envelope signal a ₁(t), just can obtain signal x ^(k)(t) first PF component PF ₁(t);

9) with first PF component PF ₁(t) from signal x ^(k)(t) separate in, obtain a new signal u ₁(t), with u ₁(t) repeat above step 2 as raw data)-8), circulation r time is up to u _r(t) be till the monotonic quantity.Decomposition obtains m PF component, and each PF component is designated as:

10) each PF component is carried out integration one time, obtain

With

11) right

Carry out above-mentioned steps 2)～8) the single order decomposition, obtain

With

Be m PF component of the inferior back of original signal differential (k-1) signal, and obtain remaining component

12) repeating step 10), 11) obtain original signal x until integration k time ₀(t) each rank PF component and remaining component of DLMD decomposition.

The present invention has adopted the differential local mean value to decompose (DLMD) method on LMD algorithm basis, the vibration signal that is collected by acceleration transducer is decomposed, comprising differential, LMD decomposition, these three kinds of key operation processes of integration.Finally can resolve into a plurality of PF components and remaining component sum to the non-stationary of a complexity, nonlinear properties, wherein each PF component all multiply by an envelope signal by a pure FM signal and obtains, envelope signal is the instantaneous amplitude of this PF component, and can directly be obtained the instantaneous frequency of PF component by pure FM signal, after the instantaneous amplitude of obtaining all PF components and instantaneous frequency, further by combination, the time-frequency distributions that original signal is complete just can obtain again.

Pass through instantaneous frequency and the instantaneous frequency of each PF component of acquisition again, draw corresponding time-frequency spectrum, and envelope frequency spectrum figure, analyze time-frequency information, can suppress the false interfering frequency in the traditional local mean value decomposable process, effectively judge comprise in the signal that vibration transducer records the running state of rotating machine feature.

Owing to adopt technique scheme, a kind of rotary machinery fault diagnosis method that decomposes based on the differential local mean value provided by the invention compared with prior art has such beneficial effect:

The present invention at first carries out k rank differential to the signal that collects, then the signal behind the differential being carried out local mean value decomposes, each PF component circulation after decomposing is carried out integration and the single order local mean value is decomposed, obtain m PF component and remaining component for k time until circulation, the result who decomposes based on the differential local mean value extracts fault signature, can effectively suppress the false interfering frequency in the traditional local mean value decomposable process, realize rotary machinery fault diagnosis.This method overcomes the deficiency of said method in mechanical fault diagnosis, can effectively suppress the false interfering frequency that decomposition result produces, and better application is in the diagnosis of mechanical fault.

If can provide and adopt the inventive method to carry out diagnosis precision rate, then more convincing.

Description of drawings

Fig. 1 is DLMD algorithm flow chart of the present invention;

Fig. 2 is embodiment of the invention inner ring fault time domain waveform figure;

Fig. 3 is embodiment of the invention LMD exploded view;

Fig. 4 is embodiment of the invention LMD decomposed P F ₁Envelope frequency spectrum figure;

Fig. 5 is embodiment of the invention LMD decomposed P F ₂Envelope frequency spectrum figure;

Fig. 6 is embodiment of the invention DLMD exploded view;

Fig. 7 is embodiment of the invention DLMD decomposed P F ₁Envelope frequency spectrum figure;

Fig. 8 is embodiment of the invention DLMD decomposed P F ₂Envelope frequency spectrum figure.

Embodiment

The present invention is described in further detail below in conjunction with the drawings and the specific embodiments.

In the rotary machinery fault diagnosis process, at first adopt acceleration transducer that plant equipment is measured, obtain vibration acceleration signal x ₀(t), again vibration acceleration signal is carried out DLMD and decompose, extract eigenwert.The present invention utilizes the method for DLMD that vibration acceleration signal is decomposed.

Below the principle of decomposing the rotary machinery fault diagnosis method of (DLMD) based on the differential local mean value is elaborated.Concrete steps are as follows:

(1) adopts acceleration transducer test rotating machinery, gather and obtain its vibration signal;

(2) the acceleration vibration signal to obtaining carries out DLMD and decomposes, and finally obtains some PF components and remaining component.May further comprise the steps:

(1) to former starting acceleration vibration signal x ₀(t) carry out k rank differential, obtain x ^(k)(t).

(2) find out the signal x that obtains behind the differential ^(k)(t) all Local Extremum n _i, obtain the mean value of all adjacent Local Extremum:

$m_i=\frac{n_i+n_{i+1}}{2}---\left(1\right)$

With all adjacent mean point m of the bundle of lines _iCouple together, and then with moving average method it is carried out smoothing processing, can obtain the local mean value function m ₁₁(t).

(3) ask the envelope estimated value:

$a_i=\frac{|n_i-n_{i+1}|}{2}---\left(2\right)$

With all adjacent two envelope estimated value a of the bundle of lines _iCouple together, adopt the running mean method that it is carried out smoothing processing then, can obtain envelope estimation function a ₁₁(t).

(4) from signal x ^(k)(t) isolate the local mean value function m in ₁₁(t), can get:

h ₁₁(t)=x ^(k)(t)-m ₁₁(t) (3)

(5) use h ₁₁(t) divided by envelope estimation function a ₁₁(t), to h ₁₁(t) carry out demodulation, can get

$s_{11}\left(t\right)=\frac{h_{11}\left(t\right)}{a_{11}\left(t\right)}---\left(4\right)$

Equally to s ₁₁(t) also repeat above-mentioned steps (2)-(5), just can obtain s ₁₁(t) envelope estimation function a ₁₂(t), a ₁₂(t) be not equal to 1, s is described ₁₁(t) be not a pure FM signal, the iterative process of saying above so just repeating again n time is up to s _1n(t) become pure FM signal, i.e. a s _1n(t) envelope estimation function a _{1 (n+1)}(t)=1, so, have

$\left\{\begin{array}{c}{h}_{11}\left(t\right)=x^{\left(k\right)}\left(t\right)-m_{11}\left(t\right)\\ h_{12}\left(t\right)=s_{11}\left(t\right)-m_{12}\left(t\right)\\ \·\\ \·\\ \·\\ h_{1n}\left(t\right)=s_{1(n-1)}\left(t\right)-m_{1n}\left(t\right)\end{array}\right.---\left(5\right)$

In the formula $\left\{\begin{array}{c}{s}_{11}\left(t\right)=h_{11}\left(t\right)/a_{11}\left(t\right)\\ s_{12}\left(t\right)=h_{12}\left(t\right)/a_{12}\left(t\right)\\ \·\\ \·\\ \·\\ s_{1n}\left(t\right)=h_{1n}\left(t\right)/a_{1n}\left(t\right)\end{array}\right.---\left(6\right)$

Wherein the condition of iteration termination is:

$\underset{n\→\∞}{\lim }{a}_{1n}\left(t\right)=1---\left(7\right)$

Do not influence under the prerequisite of decomposing effect, in actual applications, in order to reduce number of iterations, reduce operation time, can use

a _1n(t)≈1 (8)

As stopping criterion for iteration.

(6) can obtain envelope signal (instantaneous amplitude function) to all envelope estimation functions that produce in the iterative process mutually at convenience

$a_1\left(t\right)=a_{11}\left(t\right)a_{12}\left(t\right)\·\·\·a_{1n}\left(t\right)=\underset{q=1}{\overset{n}{\mathrm{\Π}}}{a}_{1q}\left(t\right)---\left(9\right)$

(7) pure FM signal s _1n(t) multiply by envelope signal a ₁(t), just can obtain signal x ^(k)(t) first PF component

PF ₁(t)=a ₁(t)s _1n(t) (10)

The frequency content that signal is the highest has been included in this component, and this component is the amplitude modulation-FM signal of a simple component, and its instantaneous amplitude is exactly envelope signal a ₁(t), its instantaneous frequency f ₁(t) then can be by pure FM signal s _1n(t) obtain, that is:

$f_1\left(t\right)=\frac{1}{2\mathrm{\π}}\frac{d\left[\arccos \left(s_{1n}\left(t\right)\right)\right]}{\mathrm{dt}}---\left(11\right)$

(8) with first PF component PF ₁(t) from signal x ^(k)(t) separate in, obtain a new signal u ₁(t).

With u ₁(t) repeat above step (2)-(8) as raw data, circulation r time is up to u _r(t) be till the monotonic quantity.

$\left\{\begin{array}{c}{u}_1\left(t\right)=x^{\left(k\right)}\left(t\right)-{\mathrm{PF}}_1\left(t\right)\\ u_2\left(t\right)=u_1\left(t\right)-{\mathrm{PF}}_2\left(t\right)\\ \·\\ \·\\ \·\\ u_r\left(t\right)=u_{r-1}\left(t\right)-{\mathrm{PF}}_r\left(t\right)\end{array}\right.---\left(12\right)$

Decomposition obtains m PF component, and each PF component is designated as:

(9) each PF component is carried out integration one time:

$\∫{\mathrm{PF}}_j^{\left(k\right)}\left(t\right)\mathrm{dt}=e_j^{(k-1)}\left(t\right)+e_{j0}^{(k-1)}---\left(13\right)$

(10) to each

Carry out above-mentioned steps (2)～(8) single order and decompose (not carrying out the circulation of repeating step (2)-(8) and r time)

$e_j^{(k-1)}\left(t\right)={\mathrm{PF}}_j^{(k-1)}\left(t\right)+v_j^{(k-1)}\left(t\right)---\left(14\right)$

(11)

Be original signal x ₀(t) each PF component of the inferior back of differential (k-1) signal, and obtain remaining component

$v_0^{(k-1)}=\underset{j=1}{\overset{m}{\mathrm{\Σ}}}{v}_j^{(k-1)}\left(t\right)+\underset{j=1}{\overset{m}{\mathrm{\Σ}}}{e}_{j0}^{(k-1)}---\left(15\right)$

(12) repeating step (10), (11) obtain original signal x until integration k time ₀(t) each PF component PF of DLMD decomposition _j(t), j=1,2 ..., m and remaining component.

Signal x ₀(t) m PF component and the remaining component v that is decomposed and obtains ₀(t) reconstruct, namely

$x_0\left(t\right)=\underset{j=1}{\overset{k}{\mathrm{\Σ}}}{\mathrm{PF}}_j\left(t\right)+v_0\left(t\right)---\left(16\right)$

Wherein:

$v_0\left(t\right)=u_r+\underset{i=1}{\overset{k}{\mathrm{\Σ}}}{v}_0^i\left(t\right)---\left(17\right)$

Illustrate that the DLMD decomposition does not cause losing of original signal information.Instantaneous amplitude and the instantaneous frequency combination of all PF components just can be obtained original signal x ₀(t) complete time-frequency distributions.

About the specific algorithm process flow diagram of DLMD respectively as shown in Figure 1, with the acceleration vibration signal that collects as x ₀(t), carry out DLMD and decompose, obtain m PF component and residual volume v final the decomposition ₀(t).

(3) in conjunction with experiment, be experimental subjects with the vitals bearing in the rotating machinery, use DLMD to be rotated mechanical fault diagnosis.The rolling bearing inner ring fault-signal that collects with vibration transducer, as the original signal x that decomposes ₀(t), decompose instantaneous frequency and the instantaneous amplitude of obtaining each PF component by DLMD, extract fault signature, carry out fault diagnosis.

Experiment porch comprises that power is the motor of 1.49kW, a torque sensor, power meter and control electronics.Tested bearing is 6205-2RS JEM SKF deep groove ball bearing, and the supporting motor axle uses spark erosion technique to arrange the Single Point of Faliure of diameter as 0.007inch at bearing.Rotating speed of motor n is 1730r/min in the experiment, and then the rotating shaft fundamental frequency is f=n/60=28.83Hz, and sample frequency is f _s=12000Hz.Calculating the bearing inner race failure-frequency according to the structural parameters of bearing is 156.01Hz.

The time domain waveform figure of bearing inner race fault-signal as shown in Figure 2.After at first adopting local mean value to decompose to handle for fault-signal, obtained 6 PF components and a remaining component, its exploded view as shown in Figure 3 since along with LMD decompose by carrying out on rank, the amplitude of each PF component is more and more little, so the relatively large PF of desirable amplitude ₁And PF ₂Component is as research object, its envelope frequency spectrum figure is respectively as Fig. 4, shown in Figure 5, from its envelope spectrum as can be seen, peak value has appearred at 57.13Hz, 155.3Hz, 310.5Hz and 774.9Hz, this and 2 frequencys multiplication (57.66Hz) of the rotating shaft fundamental frequency that calculates, the frequency (156.01Hz) of inner ring fault and the value of 2 frequencys multiplication (312.02Hz) and 5 frequencys multiplication (780.05Hz) thereof approach, but simultaneously also as can be seen, exist considerable false interfering frequency among the figure, this will influence the accuracy of Analysis on Fault Diagnosis.And after decomposing by the local mean value of differential, having obtained 6 PF components and a remaining component, its exploded view is its PF as shown in Figure 6 ₁And PF ₂The envelope frequency spectrum figure of component such as Fig. 7, shown in Figure 8, peak value has appearred compare among this figure not only the frequency (156.01Hz) of 2 frequencys multiplication (57.66Hz) at the rotating shaft fundamental frequency, inner ring fault and 2 frequencys multiplication (312.02Hz) thereof with Fig. 4, Fig. 5 near, and the frequency peak that presents is very clearly demarcated, almost do not have false interfering frequency, judgement bearing inner race that can be clear and definite has produced fault.

Based on this, can better decompose the bearing fault signal by DLMD, judge its duty.

Claims (2)

1. one kind is decomposed the rotary machinery fault diagnosis method of (DLMD) based on the differential local mean value, and it is characterized in that: its content comprises the steps:

(1) adopts acceleration transducer test rotating machinery, gather and obtain its vibration signal;

(2) the acceleration vibration signal to obtaining carries out DLMD and decomposes, and obtains some PF components and remaining component;

(3) obtain instantaneous frequency and the instantaneous amplitude of each PF component, extract fault signature.

2. a kind of local mean value based on differential according to claim 1 Diagnosis of Rotating Machinery method of decomposing (DLMD), it is characterized in that: in step (two), the described local mean value that the acceleration vibration signal is carried out differential is decomposed (DLMD) process, and its content comprises the steps:

1) to former starting acceleration vibration signal x ₀(t) carry out k rank differential, obtain x ^(k)(t);

2) ask the local mean value function m ₁₁(t), find out x ^(k)(t) all Local Extremum n _i, obtain the mean value m of all adjacent Local Extremum _i, with all adjacent mean point m of the bundle of lines _iCouple together, and then with moving average method it is carried out smoothing processing, can obtain the local mean value function m ₁₁(t);

3) ask envelope estimation function a ₁₁(t), calculate adjacent two envelope estimated value a _i, with all adjacent two envelope estimated value a of the bundle of lines _iCouple together, adopt the running mean method that it is carried out smoothing processing then, can obtain envelope estimation function a ₁₁(t);

4) from original signal x ^(k)(t) isolate the local mean value function m in ₁₁(t), obtain h ₁₁(t);

5) use h ₁₁(t) divided by envelope estimation function a ₁₁(t), to h ₁₁(t) carry out demodulation, can get s ₁₁(t);

6) equally to s ₁₁(t) also repeat above-mentioned steps 2)-5), up to s _1n(t) become pure FM signal, i.e. a s _1n(t) envelope estimation function a _{1 (n+1)}(t)=1;

7) can obtain envelope signal (instantaneous amplitude function) a to all envelope estimation functions that produce in the iterative process mutually at convenience ₁(t);

8) pure FM signal s _1n(t) multiply by envelope signal a ₁(t), just can obtain signal x ^(k)(t) first PF component PF ₁(t);

9) with first PF component PF ₁(t) from signal x ^(k)(t) separate in, obtain a new signal u ₁(t), with u ₁(t) repeat above step 2 as raw data)-8), circulation r time is up to u _r(t) be till the monotonic quantity; Decomposition obtains m PF component, and each PF component is designated as:

10) each PF component is carried out integration one time, obtain With

11) right Carry out above-mentioned steps 2)～8) the single order decomposition, obtain

With

Be m PF component of the inferior back of original signal differential (k-1) signal, and obtain remaining component

12) repeating step 10), 11) obtain original signal x until integration k time ₀(t) each PF component and the remaining component of DLMD decomposition.

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