CN105760672A - Diagnosis method for mechanical equipment faults - Google Patents

Abstract

The invention discloses a diagnosis method for mechanical equipment faults, and belongs to the technical field of mechanical equipment fault diagnosis.The diagnosis method is characterized by comprising the following steps that the probability vector B1 of the faults of a part is determined according to fault symptoms; the inherent fault rate vector B2 of the part is determined according to statistic part fault historical data; weighting operation is carried out according to the probability vector B1 of the faults of the part and the inherent fault rate vector B2 of the part to determine the comprehensive probability vector B of the faults of the part; the reason for the faults is determined according to the comprehensive probability of the faults of the part.According to the diagnosis method, multiple factors such as the fault rate of the part, the fault mechanism, the fault symptom significance level and the difficulty level of acquiring the fault symptoms are comprehensively considered, and the accuracy of the diagnosis result of the diagnosis method is greatly improved.

Description

A kind of Trouble Diagnostic Method of Machinery Equipment

Technical field

The present invention relates to a kind of method for diagnosing faults based on fault rate Yu failure symptom, belong to mechanical fault diagnosis technical field.

Background technology

Fault diagnosis technology be one according to plant equipment running status, judge whether it normally and in time finds the technology of fault, it it is the powerful guarantee of plant equipment safety in production and Effec-tive Function, but the enforcement of such technology is also faced with lot of challenges: owing to the parts of plant equipment are various, the summation of the mechanical movement that its motor process is own partial parts or all parts carry out, this fault allowing for plant equipment is generally of polyphyly, complexity and concealed feature；The origin cause of formation that fault occurs is complex, and usual same fault can show various features, and same fault signature is likely to be caused by different faults；Additionally, existing method for diagnosing faults is based on simple failure symptom more makes judge, not having the failure mechanism of consideration equipment and the complexity of failure symptom acquisition, diagnostic result lacks objectivity.

Summary of the invention

It is an object of the invention to overcome the deficiency of method for diagnosing faults when single factor test, it is proposed to a kind of equipment fault diagnosis method based on Parts Breakdown rate Yu failure symptom, make diagnostic result more science, rationally.

For reaching above-mentioned purpose, the equipment fault diagnosis method based on Parts Breakdown rate Yu failure symptom that the present invention proposes, namely a kind of Trouble Diagnostic Method of Machinery Equipment, comprises the steps:

1) determine, according to failure symptom, the probability vector B1 that parts break down

(1) failure symptom collection U and failure cause collection V is set up；

Set up failure symptom collection, U={u₁, u₂..., u_m, wherein u₁, u₂..., u_mThe failure symptom of expression equipment, m is the number of failure symptom；Failure cause collection V={v₁,v₂,…,v_n, v₁,v₂,…,v_nRepresenting failure cause, n is failure cause number.

(2) failure symptom obvious degree vector A is determined

Obvious degree according to failure symptom, gives a mark to each failure symptom, forms failure symptom obvious degree vector A=(a₁, a₂..., a_m), a₁, a₂..., a_mRepresenting the obvious degree of each failure symptom, score value is according to following table.

Table 1 failure symptom obvious degree grade form

(3) the complexity vector F that failure symptom scene obtains is determined

The complexity grade form that table 2 failure symptom scene obtains

According to the complexity that failure symptom obtains, determine, by upper table, the complexity vector F that failure symptom scene obtains

F=(f₁,f₂,…,f_m)

Wherein f₁, f₂..., f_mThe failure symptom u of expression equipment₁, u₂..., u_mThe complexity obtained, m is the number of failure symptom

(4) Judgement Matrix R is determined

Set up Judgement Matrix R, it is determined that failure symptom u_iTo fault v_jDegree of membership r_ij, then the degree of membership of n failure cause is just constituted m × n rank Judgement Matrix R by m failure symptom.

$R=\left[\begin{array}{cccc}{r}_{11}& r_{12}& ...& r_{1n}\\ r_{21}& r_{22}& ...& r_{2n}\\ ...& ...& ...& ...\\ r_{m1}& r_{m2}& ...& r_{mn}\end{array}\right]$

R in formula_ijIt is failure symptom u_iTo failure cause v_jDegree of membership, meet 0≤r_ij≤1。

In the present invention, for each failure symptom u_i, the failure cause being likely to cause this failure symptom is compared between two, represents this failure symptom magnitude relationship to failure cause degree of membership with the number of demarcating shown in following table.For each failure symptom u_i, construct a comparator matrix C between two_i。

$C_i=\left[\begin{array}{cccc}{c}_{11}^{\left(i\right)}& c_{12}^{\left(i\right)}& ...& c_{1n}^{\left(i\right)}\\ c_{21}^{\left(i\right)}& c_{22}^{\left(i\right)}& ...& c_{2n}^{\left(i\right)}\\ ...& ...& ...& ...\\ c_{n1}^{\left(i\right)}& c_{n2}^{\left(i\right)}& ...& c_{nn}^{\left(i\right)}\end{array}\right]$

Table 3 is comparator matrix element Scale Method between two

In the present invention, comparator matrix between two is constructed respectively by multidigit expert, constructing between two after comparator matrix, recycling and method, Gen Fa or power method solve the failure symptom degree of membership to failure cause, and the preferred root method of the present invention solves the failure symptom degree of membership to failure cause.

$r_{ij}=\sqrt[n]{\underset{k=1}{\overset{n}{\Π}}{c}_{jk}^{\left(i\right)}}/\underset{i=1}{\overset{n}{\Σ}}\left(\sqrt[n]{\underset{k=1}{\overset{n}{\Π}}{c}_{jk}^{\left(i\right)}}\right)$

(5) the probability vector B that related components breaks down is determined₁

The probability vector B that parts break down₁=(b₁₁,b₁₂,…,b_1n), b₁₁,b₁₂,…,b_1nRepresent fault v₁,v₂,…,v_nThere is the size of probability, n is fault number.

Considering on the basis of the failure symptom order of severity and failure symptom scene acquisition complexity, following formula is utilized to try to achieve the probability vector B that parts break down₁

B₁=(k₁A+k₂F)○R

Above formula k₁And k₂For vector A and vector F weight coefficient, in the present invention, k₁Take 0.8, k₂Taking 0.2, zero is operational rule, and the present invention adopts weighted mean method.

2) related components fault rate vector B inherently is determined₂

(1) historical data according to statistics, by time between failures by sorting from small to large, sets up time between failures table.

(2) according to time between failures table, Median rank formula its reliability is estimated

R(t_i)=1-(i-0.3)/(n+0.4)

Wherein t₁,t₂,…,t_nFor time between failures, and t₁≤t₂≤…≤t_n, n is the sum gathering fault

(3) according to the reliability numerical value estimated, Weibull probability figure is drawn

Make x=lnt_i, y=ln [-lnR (t_i)], so (t₁,R(t₁)),(t₂,R(t₂)),…,(t_n,R(t_n)) data set is just transformed to ((x₁,y₁),(x₂,y₂),…,(x_n,y_n)) data set, these data points are drawn on Weibull probability figure, just obtain Weibull probability figure.

(4) on Weibull frequency paper, by the left-hand component matching straight line L of figure₁, obtain straight line L₁Slope and intercept on the y axis be k₁And b₁, at the right-hand member of figure, one asymptote L of matching₂, obtain straight line L₂Slope and intercept on the y axis be k₂And b₂。

(5) according to dual segments Weibull function, it is determined that its Reliability Function.

$R\left(t\right)=\left\{\begin{array}{cc}\exp \[-{(t/{\mathrm{\α}}_1)}^{{\mathrm{\β}}_1}\]& t\≤t_0\\ k\exp \[-{(t/{\mathrm{\α}}_2)}^{{\mathrm{\β}}_2}\]& t>t_0\end{array}\right.$

Wherein

α₁=exp (-b₁/k₁)

β₁=k₁

α₂=exp (-b₂/k₂)

β₂=k₂

$t_0=[\left({\mathrm{\β}}_1{\mathrm{\α}}_2^{{\mathrm{\β}}_2}\right)/\left({\mathrm{\β}}_2{\mathrm{\α}}_1^{{\mathrm{\β}}_1}\right){]}^{1/({\mathrm{\β}}_2-{\mathrm{\β}}_1)}$

$k=\exp \left[(1-{\mathrm{\β}}_2/{\mathrm{\β}}_1){(t_0/{\mathrm{\α}}_2)}^{{\mathrm{\β}}_2}\right]$

(6) parts fault rate inherently is determined

For fault set V={v₁,v₂,…,v_n, it is determined that the fault rate b of the unit in each fault set_2i, i=1,2,3 ..., n.

b_2i=1-R (t)

(7) above method is used, it is determined that related components fault rate vector B inherently₂

B₂=(b₂₁,b₂₂,..b_2n)

3) according to step 1) determine the probability that parts break down and step 2 according to failure symptom) parts fault rate inherently, compute weighted, it is determined that the combined chance that parts break down.

(1) the probability vector B that normalization is broken down according to the related components that failure symptom is determined₁

(2) the fault rate vector B of normalization related components₂

(3) the probability vector B that the related components after normalization is broken down₁Fault rate vector B with the related components after normalization₂It is weighted average computation, obtains the combined chance vector B that parts break down, namely

B=k₃B₁+k₄B₂

K in above formula₃And k₄Respectively vector B₁With vector B₁Weight coefficient, in the present invention, k₃Take 0.5, k₄Take 0.5.

4) size of the probability broken down by parts, it is determined that failure cause.

The present invention, compared with existing machinery equipment fault diagnosis method, has the advantage that

1. the present invention is in mechanical fault diagnosis process, both the failure symptom of equipment had been considered, it is further contemplated that the fault rate that equipment component is inherently, consider from phenomenon and two, source aspect, it is to avoid conventional method for diagnosing faults only considers that equipment fault symptom single factors causes the defect that mechanical fault diagnosis accuracy is not high at present；

2. the present invention is in the process according to failure symptom failure judgement, both the obvious degree of failure symptom had been considered, it is contemplated that the complexity that failure symptom obtains, it is to avoid current method for diagnosing faults only considers that the obvious degree single factors of equipment fault symptom causes the defect that mechanical fault diagnosis accuracy is not high.

Accompanying drawing explanation

Fig. 1 is the Fuzzy fault diagnosis flow chart of steps of the present invention,

Fig. 2 is the Weibull probability figure of the present invention.

Detailed description of the invention

The equipment fault diagnosis method based on Parts Breakdown rate Yu failure symptom that the present invention proposes, its flow chart is as it is shown in figure 1, below for certain Digit Control Machine Tool Failure Diagnosis of Hydraulic System, illustrate to be embodied as step as follows:

Step 1, determine the probability vector B that parts break down according to failure symptom₁

(1) failure symptom collection U and failure cause collection V is set up；

Set up failure symptom collection, U={u₁, u₂..., u_m, wherein u₁, u₂..., u_mThe failure symptom of expression equipment, m is the number of failure symptom；Failure cause collection V={v₁,v₂,…,v_n, v₁,v₂,…,v_nRepresenting failure cause, n is failure cause number.

For this Digit Control Machine Tool hydraulic system, when breaking down, failure symptom has Pressure gauge show value very low；Hydraulic cylinder works is unable；Shriek and noise is had in system；Pump body temperature is higher.I.e. failure symptom collection U={u₁,u₂,u₃,u₄}={ Pressure gauge show value is very low, and hydraulic cylinder works is unable, has shriek and noise in system, the temperature drift of oil pump }.

The parts that this Digit Control Machine Tool hydraulic system breaks down have the inner body of overflow valve to be stuck, damage or wear and tear；The inner body heavy wear of oil pump；Oil filter is blocked by dirt；Hydraulic cylinder sealing element damages or poorly sealed；Oil pump driving device skids.So failure cause collection V={v₁,v₂,v₃,v₄,v₅The inner body of }={ overflow valve is stuck, damages or weares and teares, the inner body heavy wear of oil pump, and oil filter is blocked by dirt, and hydraulic cylinder sealing element damages or poorly sealed, and oil pump driving device skids }.

(2) failure symptom obvious degree vector A is determined

Obvious degree according to failure symptom, gives a mark to each failure symptom, forms failure symptom obvious degree vector A=(a₁, a₂..., a_m), a₁, a₂..., a_mRepresenting the obvious degree of each failure symptom, score value is according to following table.

Table 1 failure symptom obvious degree grade form

Observe this Digit Control Machine Tool hydraulic system, it has been found that Pressure gauge show value is very low；The power of hydraulic cylinder output is less than normal；Shriek and the noise of system are bigger；Pump body temperature is somewhat higher.According to table 1, it is determined that failure symptom obvious degree vector A={0.7,0.3,0.7,0.3}.

(3) the complexity vector F that failure symptom scene obtains is determined

The complexity grade form that table 2 failure symptom scene obtains

According to the complexity that failure symptom obtains, determine, by upper table, the complexity vector F that failure symptom scene obtains

F=(f₁,f₂,…,f_m)

Wherein f₁, f₂..., f_mThe failure symptom u of expression equipment₁, u₂..., u_mThe complexity obtained, m is the number of failure symptom.

For the example of the present invention, the very low u of its failure symptom Pressure gauge show value₁It is very easy to observe, the unable u of hydraulic cylinder works₂Difficult observation, has shriek and noise u in system₃Easily observe, the temperature drift u of oil pump₄Easily observe, so complexity vector F=(0.3,0.7,0.5,0.5) that failure symptom scene obtains.

(4) Judgement Matrix R is determined

Set up Judgement Matrix R, it is determined that failure symptom u_iTo failure cause v_jDegree of membership r_ij, then the degree of membership of n failure cause is just constituted m × n rank Judgement Matrix R by m failure symptom.

$R=\left[\begin{array}{cccc}{r}_{11}& r_{12}& ...& r_{1n}\\ r_{21}& r_{22}& ...& r_{2n}\\ ...& ...& ...& ...\\ r_{m1}& r_{m2}& ...& r_{mn}\end{array}\right]$

R in formula_ijIt is failure symptom u_iTo failure cause v_jDegree of membership, meet 0≤r_ij≤1。

In the present invention, for each failure symptom u_i, the failure cause being likely to cause this failure symptom is compared between two, represents this failure symptom magnitude relationship to failure cause degree of membership with the number of demarcating shown in following table.For each failure symptom u_i, construct a comparator matrix C between two_i。

$C_i=\left[\begin{array}{cccc}{c}_{11}^{\left(i\right)}& c_{12}^{\left(i\right)}& ...& c_{1n}^{\left(i\right)}\\ c_{21}^{\left(i\right)}& c_{22}^{\left(i\right)}& ...& c_{2n}^{\left(i\right)}\\ ...& ...& ...& ...\\ c_{n1}^{\left(i\right)}& c_{n2}^{\left(i\right)}& ...& c_{nn}^{\left(i\right)}\end{array}\right]$

Table 3 is comparator matrix element Scale Method between two

In the present invention, comparator matrix between two is constructed respectively by multidigit expert, constructing between two after comparator matrix, recycling and method, Gen Fa or power method solve the failure symptom degree of membership to failure cause, and the preferred root method of the present invention solves the failure symptom degree of membership to failure cause.

$r_{ij}=\sqrt[n]{\underset{k=1}{\overset{n}{\Π}}{c}_{jk}^{\left(i\right)}}/\underset{i=1}{\overset{n}{\Σ}}\left(\sqrt[n]{\underset{k=1}{\overset{n}{\Π}}{c}_{jk}^{\left(i\right)}}\right)$

In this example, for the failure symptom u that Pressure gauge show value is very low₁Construct its comparator matrix between two,

$C_1=\left[\begin{array}{ccccc}1& 1/9& 1/7& 1/9& 1/7\\ 9& 1& 5& 1& 7\\ 7& 1/5& 1& 1/3& 1\\ 9& 1& 3& 1& 3\\ 7& 1/7& 1& 1/3& 1\end{array}\right]$

Calculate, the failure symptom u that Pressure gauge show value is very low₁Membership vector r to various failure causes₁

r₁=(0.03,0.43,0.11,0.32,0.11)

Calculate equally, the power failure symptom u less than normal of hydraulic cylinder output₂Membership vector r to various failure causes₂

r₂=(0.02,0.41,0.26,0.28,0.03)

The shriek of system and noise relatively major break down symptom u₃Membership vector r to various failure causes₃

r₃=(0.39,0.21,0.11,0.09,0.20)

The somewhat higher failure symptom u of pump body temperature₄Membership vector r to various failure causes₄

r₄=(0.31,0.38,0.14,0.04,0.13)

$R=\left[\begin{array}{c}{r}_1\\ r_2\\ r_3\\ r_4\end{array}\right]=\left[\begin{array}{ccccc}0.03& 0.43& 0.11& 0.32& 0.11\\ 0.02& 0.41& 0.26& 0.28& 0.03\\ 0.39& 0.21& 0.11& 0.09& 0.20\\ 0.31& 0.38& 0.14& 0.04& 0.13\end{array}\right]$

(5) the probability vector B that related components breaks down is determined₁

The probability vector B that parts break down₁=(b₁₁,b₁₂,…,b_1n), b₁₁,b₁₂,…,b_1nRepresent fault v₁,v₂,…,v_nThere is the size of probability, n is fault number.

Considering on the basis of the failure symptom order of severity and failure symptom scene acquisition complexity, following formula is utilized to try to achieve the probability vector B that related components breaks down₁。

B₁=(k₁A+k₂F)○R

Above formula k₁And k₂For vector A and vector F weight coefficient, in the present invention, k₁Take 0.8, k₂Taking 0.2, zero is operational rule, and the present invention adopts weighted mean method.

In this example, A={0.7,0.3,0.7,0.3}, F=(0.3,0.7,0.5,0.5)

Step 2, determine related components fault rate vector B inherently₂

(1) according to historical data, by time between failures by sorting from small to large, time between failures table is set up

(2) according to time between failures table, Median rank formula its reliability is estimated

R(t_i)=1-(i-0.3)/(n+0.4)

Wherein t₁,t₂,…,t_nFor time between failures, and t₁≤t₂≤…≤t_n, n is the sum gathering fault

For overflow valve, illustrate how to estimate its fault rate.Adding up certain factory's model lathe service data from June 30th, 1 day 1 January in 2012, table 4 below is the failure logging of overflow valve, and wherein x and y is the coordinate figure (owing to sample is relatively big, only provide part result here) of discrete data point

Table 4 fault data and Weibull map table thereof

(3) according to the reliability numerical value estimated, Weibull probability figure is drawn

Make x=lnt_i, y=ln [-lnR (t_i)], so (t₁,R(t₁)),(t₂,R(t₂)),…,(t_n,R(t_n)) data set is just transformed to ((x₁,y₁),(x₂,y₂),…,(x_n,y_n)) data set, these data points are drawn on Weibull probability figure, just obtain Weibull probability figure.

The coordinate figure of data scatterplot all in table 4 is retouched on Weibull probability paper (WeibullPlottingPaper is called for short WPP), as shown in Figure 2.

(4) on Weibull frequency paper, by the left-hand component matching straight line L of figure₁, obtain straight line L₁Slope and intercept on the y axis be k₁And b₁, at the right-hand member of figure, one asymptote L of matching₂, obtain straight line L₂Slope and intercept on the y axis be k₂And b₂。

As in figure 2 it is shown, at the left-hand component matching straight line L of figure₁, its slope and intercept on the y axis are k₁=0.763 and b₁=-5.564

At the right-hand member of figure, one asymptote L of matching₂, its slope and intercept on the y axis are k₂=1.303 and b₂=-9.029

(5) according to dual segments Weibull function, it is determined that its Reliability Function.

$R\left(t\right)=\left\{\begin{array}{cc}\exp \[-{(t/{\mathrm{\α}}_1)}^{{\mathrm{\β}}_1}\]& t\≤t_0\\ k\exp \[-{(t/{\mathrm{\α}}_2)}^{{\mathrm{\β}}_2}\]& t>t_0\end{array}\right.$

Wherein

α₁=exp (-b₁/k₁)

β₁=k₁

α₂=exp (-b₂/k₂)

β₂=k₂

$t_0={\[\left({\mathrm{\β}}_1{\mathrm{\α}}_2^{{\mathrm{\β}}_2}\right)/\left({\mathrm{\β}}_2{\mathrm{\α}}_1^{{\mathrm{\β}}_1}\right)\]}^{1/({\mathrm{\β}}_2-{\mathrm{\β}}_1)}$

$k=\exp \[(1-{\mathrm{\β}}_2/{\mathrm{\β}}_1){(t_0/{\mathrm{\α}}_2)}^{{\mathrm{\β}}_2}\]$

α₁=exp (-b₁/k₁)=exp ((5.564/0.763)=1468.9

β₁=k₁=0.763

α₂=exp (-b₂/k₂)=exp (9.029/1.303)=1021.9

β₂=k₂=1.303

$t_0={\[\left({\mathrm{\β}}_1{\mathrm{\α}}_2^{{\mathrm{\β}}_2}\right)/\left({\mathrm{\β}}_2{\mathrm{\α}}_1^{{\mathrm{\β}}_1}\right)\]}^{1/({\mathrm{\β}}_2-{\mathrm{\β}}_1)}=277$

$k=\exp \[(1-{\mathrm{\β}}_2/{\mathrm{\β}}_1){(t_0/{\mathrm{\α}}_2)}^{{\mathrm{\β}}_2}\]=0.8788$

Finally, the Reliability Function trying to achieve the double Weibull segmented model of this overflow valve is

$R\left(t\right)=\left\{\begin{array}{cc}\exp \[-{(t/1468.9)}^{0.763}\]& t\≤277\\ 0.8788\exp \[-{(t/1021.9)}^{1.303}\]& t>277\end{array}\right.$

In this example, overflow valve runs 305 hours, and according to above-mentioned formula, its reliability is 0.7145

Use the same method can in the hope of the Reliability Function of oil pump, oil filter, sealing member and oil pump driving device.

(6) parts fault rate inherently is determined

For failure cause collection V={v₁,v₂,…,v_n, it is determined that the fault rate b of the unit that each failure cause is concentrated_2i, i=1,2,3 ..., n.

b₂₁=1-R (t)=1-0.7145=0.2855

(7) above method is used, it is determined that related components fault rate vector B inherently₂

B₂=(b₂₁,b₂₂,..b_2n)

B₂=(0.2855,0.6791,0.2674,0.31,0.302)

Step 3, determine, based on failure symptom, the probability that parts break down according to step 1, and the fault rate that the parts determined of step 2 are inherently, compute weighted, it is determined that the combined chance that parts break down.

(1) the probability vector B that normalization is broken down according to the related components that failure symptom is determined₁

B₁=(0.3890,0.6902,0.2872,0.3778,0.2558) after normalized,

B₁=(0.1945,0.3451,0.1436,0.1889,0.1279)

(2) the fault rate vector B of normalization related components₂

B₂=(0.2855,0.6791,0.2674,0.31,0.302) after normalized,

B₂=(0.1548,0.3683,0.1450,0.1681,0.1638)

(3) the probability vector B that the related components determined according to failure symptom is broken down₁Fault rate vector B with related components₂It is weighted average computation, obtains the resultant fault probability vector B of parts, namely

B=k₃B₁+k₄B₂

K in above formula₃And k₄Respectively vector B₁With vector B₂Weight coefficient, in the present invention, k₃Take 0.5, k₄Take 0.5.

B=0.5 × (0.1548,0.3683,0.1450,0.1681,0.1638)+0.5 × (0.1945,0.3451,0.1436,0.1889,0.1279)

=(0.1747,0.3567,0.1442,0.1785,0.1459).

Step 4, the size of resultant fault probability occurs by parts, it is determined that failure cause.

Element in resultant fault probability vector B is ranked up according to order from big to small: b2 b4 > b1 > b5 > b3, it is seen that causing the not enough most probable reason of system pressure is the inner body heavy wear of oil pump.

Claims (5)

1. a Trouble Diagnostic Method of Machinery Equipment, it is characterised in that comprise the following steps:

1) determine, according to failure symptom, the probability vector B that parts break down₁；

2) the Parts Breakdown historical data according to statistics, it is determined that parts fault rate vector B inherently₂；

3) according to step 1) the probability vector B that breaks down of the parts determined₁, and step 2) parts determined fault rate vector B inherently₂, compute weighted, it is determined that the combined chance vector B that parts break down；

4) size of the combined chance broken down by parts, it is determined that failure cause.

2. Trouble Diagnostic Method of Machinery Equipment according to claim 1, it is characterised in that step 1) described in determine, according to failure symptom, the probability vector B that parts break down₁Method, comprise the following steps:

(1) failure symptom collection U and failure cause collection V is set up respectively by following formula；

Failure symptom collection U={u₁, u₂..., u_m, wherein u₁, u₂..., u_mThe failure symptom of expression equipment, m is the number of failure symptom；

Failure cause collection V={v₁,v₂,…,v_n, wherein v₁,v₂,…,v_nRepresenting failure cause, n is failure cause number；

(2) failure symptom obvious degree vector A is determined

According to failure symptom obvious degree, give a mark to each failure symptom, form failure symptom obvious degree vector A=(a₁, a₂..., a_m), wherein a₁, a₂..., a_mRepresent each failure symptom obvious degree, its score value according to the form below 1 value；

Table 1 failure symptom obvious degree grade form

(3) the complexity vector F that failure symptom scene obtains is determined

The complexity grade form that table 2 failure symptom scene obtains

According to the complexity that failure symptom obtains, determine, by upper table, the complexity vector F that failure symptom scene obtains

F=(f₁,f₂,…,f_m)

Wherein f₁, f₂..., f_mThe failure symptom u of expression equipment₁, u₂..., u_mThe complexity obtained, m is the number of failure symptom；

(4) Judgement Matrix R is determined

Set up Judgement Matrix R, it is determined that failure symptom u_iTo failure cause v_jDegree of membership r_ij, then the degree of membership of n failure cause is just constituted m × n rank Judgement Matrix R by m failure symptom；

$R=\left[\begin{array}{cccc}{r}_{11}& r_{12}& ...& r_{1n}\\ r_{21}& r_{22}& ...& r_{2n}\\ ...& ...& ...& ...\\ r_{m1}& r_{m2}& ...& r_{mn}\end{array}\right]$

R in above formula_ijIt is failure symptom u_iTo failure cause v_jDegree of membership, meet 0≤r_ij≤1；

For each failure symptom u_i, the failure cause being likely to cause this failure symptom is compared between two, adopts the number of demarcating shown in table 3 below to represent this failure symptom magnitude relationship to failure cause degree of membership；For each failure symptom u_iConstruct a comparator matrix C between two_i；

$C_i=\left[\begin{array}{cccc}{c}_{11}^{\left(i\right)}& c_{12}^{\left(i\right)}& ...& c_{1n}^{\left(i\right)}\\ c_{21}^{\left(i\right)}& c_{22}^{\left(i\right)}& ...& c_{2n}^{\left(i\right)}\\ ...& ...& ...& ...\\ c_{n1}^{\left(i\right)}& c_{n2}^{\left(i\right)}& ...& c_{nn}^{\left(i\right)}\end{array}\right]$

Table 3 is comparator matrix element Scale Method between two

Constructing comparator matrix between two respectively by multidigit expert, constructing between two after comparator matrix, recycling root method solves the failure symptom degree of membership to failure cause；

$r_{ij}=\sqrt[n]{\underset{k=1}{\overset{n}{\Π}}{c}_{jk}^{\left(i\right)}}/\underset{i=1}{\overset{n}{\Σ}}\left(\sqrt[n]{\underset{k=1}{\overset{n}{\Π}}{c}_{jk}^{\left(i\right)}}\right)$

(5) the probability vector B that related components breaks down is determined₁

The probability vector B that parts break down₁=(b₁₁,b₁₂,…,b_1n), wherein b₁₁,b₁₂,…,b_1nRepresent failure cause v₁,v₂,…,v_nThere is the size of probability, n is failure cause number；

Considering on the basis of the failure symptom order of severity and failure symptom scene acquisition complexity, following formula is utilized to try to achieve the probability vector B that related components breaks down₁

B₁=(k₁A+k₂F)○R

In above formula, k₁And k₂For vector A and vector F weight coefficient, wherein, k₁Take 0.8, k₂Taking 0.2, zero represents operational rule, i.e. weighted mean method.

3. Trouble Diagnostic Method of Machinery Equipment according to claim 1, it is characterised in that step 2) described according to statistics Parts Breakdown historical data, it is determined that parts fault rate vector B inherently₂Method, comprise the following steps:

(1) according to historical data, by time between failures by sorting from small to large, time between failures table is set up,

(2) according to time between failures table, Median rank formula is utilized to calculate its reliability R (t_i):

R(t_i)=1-(i-0.3)/(n+0.4)

Wherein t₁,t₂,…,t_nFor time between failures, and t₁≤t₂≤…≤t_n, n is the sum gathering fault,

(3) according to the reliability numerical value calculated, Weibull probability figure is drawn

Make x=lnt_i, y=ln [-lnR (t_i)], by (t₁,R(t₁)),(t₂,R(t₂)),…,(t_n,R(t_n)) data set is transformed to ((x₁,y₁),(x₂,y₂),…,(x_n,y_n)) data set, and the data point of described data set is drawn on Weibull probability figure, just obtain Weibull probability figure；

(4) on Weibull frequency paper, the left-hand component matching straight line L of the figure obtaining Weibull probability figure that step (3) is obtained₁, obtain straight line L₁Slope and intercept on the y axis be k₁And b₁, at the right-hand member of figure, one asymptote L of matching₂, obtain straight line L₂Slope and intercept on the y axis be k₂And b₂；

(5) according to dual segments Weibull function, it is determined that its Reliability Function R (t)；

$R\left(t\right)=\left\{\begin{array}{cc}\exp \[-{(t/{\mathrm{\α}}_1)}^{{\mathrm{\β}}_1}\]& t\≤t_0\\ k\exp \[-{(t/{\mathrm{\α}}_2)}^{{\mathrm{\β}}_2}\]& t>t_0\end{array}\right.$

Wherein

α₁=exp (-b₁/k₁)

β₁=k₁

α₂=exp (-b₂/k₂)

β₂=k₂

$t_0={\[\left({\mathrm{\β}}_1{\mathrm{\α}}_2^{{\mathrm{\β}}_2}\right)/\left({\mathrm{\β}}_2{\mathrm{\α}}_1^{{\mathrm{\β}}_1}\right)\]}^{1/({\mathrm{\β}}_2-{\mathrm{\β}}_1)}$

$k=\exp \[(1-{\mathrm{\β}}_2/{\mathrm{\β}}_1){(t_0/{\mathrm{\α}}_2)}^{{\mathrm{\β}}_2}\]$

(6) parts fault rate inherently is determined

For fault set V={v₁,v₂,…,v_n, it is determined that the fault rate b of the unit in each fault set_2i, i=1,2,3 ..., n；

b_2i=1-R (t)

(7) above method is used, it is determined that related components fault rate vector B inherently₂

B₂=(b₂₁,b₂₂,..b_2n)。

4. Trouble Diagnostic Method of Machinery Equipment according to claim 1, it is characterised in that step 3) described in the method for combined chance vector B that breaks down of determination parts, comprise the following steps:

(1) the probability vector B that normalization is broken down according to the related components that failure symptom is determined₁,

(2) the fault rate vector B of normalization related components₂,

(3) the probability vector B that the related components determined according to failure symptom after normalization is broken down₁Fault rate vector B with the related components after normalization₂It is weighted average computation, obtains the combined chance vector B that parts break down, namely

B=k₃B₁+k₄B₂

K in above formula₃And k₄Respectively vector B₁With vector B₂Weight coefficient, wherein, k₃Take 0.5, k₄Take 0.5.

5. Trouble Diagnostic Method of Machinery Equipment according to claim 2, it is characterised in that the described failure symptom method to the degree of membership of failure cause that solves also includes and method or power method.