CN102436252B - Process industry fault diagnosis method and system based on immune hazard theory - Google Patents

Abstract

The invention aims at using the characteristics of strong adaptability and self-learning capacity of an artificial immune system, overcoming the defects of long calculation time, high incorrect and missed report rate and the like of the existing artificial immune method, and applying an immune hazard theory to the fault diagnosis of a process industry. Compared with the existing artificial immune system, the invention provides a method for determining hazard signals and hazard rules and dividing hazardous areas, and additionally provides a fault diagnosis method based on the immune hazard theory. The process industry fault diagnosis method based on the immune hazard theory comprises a hazardous area dividing step, which is used for dividing the hazardous areas; an antibody generation step, which is used for generating antibodies according to history data in each hazardous area in the original fault samples; and a fault detection step, which is used for monitoring whether a process is faulted or not. The invention additionally provides a process industry fault diagnosis system based on the immune hazard theory.

Description

A kind of method for diagnosing faults of the process industry based on immune danger theory and system

Invention field

The present invention relates to a kind of method for diagnosing faults and system based on immune danger theory, it is mainly used in chemical process, oil refining process, the fault diagnosis field of the process industries such as bio-pharmaceuticals process.

Background technology

A gordian technique of the stable operation of realization flow industry be exactly under different mode of operations to the unusual service condition occurred can carry out accurately, reliable early diagnosis or early warning.In recent years, along with scientific and technological development, in the modern production process, the system of application is increasingly sophisticated, and the equipment related to is increasingly specialized, for continuous large batch of modern production process, more is necessary to set up supervisory system, finds in time and diagnose the reason that is out of order.From nineteen seventies, the research of process failure diagnosis is just an emphasis of process industry research field always.Fault diagnosis not only can guarantee the stability of production run, the generation of prevention serious accident effectively, timely, can also after occurring, fault provide instruction, help works engineer to be processed, repair fault, effectively reduce the loss that fault causes.

Through years development, now main method for diagnosing faults roughly can be divided three classes from the diagnosis principle: the method based on quantitative model, the method based on qualutative model, the method based on historical data, typical method has neural network, least square method, wavelet analysis method, principal component analysis (PCA) etc.To the fault diagnosis of transient process, more common method is Multivariable Statistical Methods in recent years, its advantage is the generation that fast detecting is abnormal, but not high with the diagnosis accuracy of contribution plot analytical approach, and lack adaptive ability and the self-learning capability of diagnosing new fault.For this present situation, the artificial immune system that has the scholar to propose to have adaptivity and self-learning capability is incorporated into fault diagnosis field.

Artificial immune system is a kind of integrated intelligent system that relates to multidisciplinary field, and it organically combines immunology and engineering science, utilizes the technology such as mathematics, computing machine to set up the immunologic mechanism model, and is applied to the aspects such as design, enforcement of engineering.In recent years, artificial immunity is introduced in fault diagnosis field gradually.But due to traditional artificial immune system algorithm, because calculated amount is very large, the element related to is more, has long problem computing time, the wrong report likely occurred in process fault detection is failed to report.

For these problems, other method can be combined with artificial immune system on the one hand, set up the Hybrid fault diagnosis system of multi-method.On the other hand, artificial immune system itself is improved, the artificial immune system based on danger theory is exactly a kind of effective method wherein.

The immunity danger theory is for the immune many deficiencies of Traditional Man, a new immune model of proposition.This model thinks, immune system is not to the nonego reaction, but to hazardous reaction.After the cell that is upset produces alarm signal, this class antigen presenting cell of macrophage is by near antigen capture and offer to lymphocyte.This signal itself can be set a hazardous location around oneself, and only the B cell in this scope just likely produces the antibody of coupling antigen, experience immune response process.Do not mate or not the cell in hazardous location can not be upset.Danger theory, as a kind of brand-new theoretical model, no longer needs to distinguish the concept of oneself and nonego, thereby and only need judge whether to bring loss to produce danger signal to body cell.It provides the thought of more effective deal with data for artificial immune system, concentrate on dangerous, on significant data.

Summary of the invention

The object of the invention is to utilize the strong characteristics of artificial immune system adaptability and self-learning capability simultaneously, overcome the defects such as existing artificial immunity method long wrong report rate of failing to report computing time is higher, immune danger theory is applied to the fault diagnosis of process industry, and proposes the division methods of the definite and hazardous location of danger signal and dangerous rule.More existing artificial immune system, definite and division methods hazardous location of the danger signal that the present invention proposes and dangerous rule has represented a kind of brand-new method for diagnosing faults based on immune danger theory.

According to an aspect of the present invention, provide a kind of method for diagnosing faults based on immune danger theory, it is characterized in that comprising:

The accompanying drawing explanation

Fig. 1 has shown the process flow diagram of method according to an embodiment of the invention.

Fig. 2 has shown the process flow diagram of the risk zontation that method according to an embodiment of the invention adopts.

Fig. 3 has shown the secondary continuous stir reactor schematic flow sheet of method application according to an embodiment of the invention.

Fig. 4 has shown the SDG figure of the secondary continuous stir reactor flow process of method application according to an embodiment of the invention.

Embodiment

The object of the invention is to utilize the strong characteristics of artificial immune system adaptability and self-learning capability simultaneously, overcome the defects such as existing artificial immunity method long wrong report rate of failing to report computing time is higher, immune danger theory is applied to the fault diagnosis of process industry.More existing artificial immune system, the present invention proposes the division methods that reaches hazardous location of determining of danger signal and dangerous rule, and proposed a kind of method for diagnosing faults based on immune danger theory.

For the fault diagnosis of process industry, as shown in Figure 1, method according to an embodiment of the invention comprises:

-SDG and HAZOP analytical procedure

Immune danger theory diagnostic system according to an embodiment of the invention can directly carry out SDG (signed digraph) and HAZOP (hazard and operability analysis) analysis at step S101 to the original process flow process, at first by SDG, analyze, obtain the mutual relationship between the flow process variable, the hazard level of preliminary judgment variable, and obtain SDG figure.Analyze by HAZOP again, find the dangerous hidden danger existed in flow process, and analyze the judgement danger classes by calculating Dow Chemical index and LOPA (layer of protection analysis).In this step, HAZOP analyzes and can use other safe evaluation methods such as safety checklist method, fault tree analysis to replace.

Definite step of-danger signal and dangerous rule

The Dow Chemical index obtained according to step S101 and LOPA analysis result, can determine the dangerous hidden danger of technique and obtain the danger classes of variable at step S102, the variable that danger classes is higher is decided to be danger signal, and according to characteristics and the HAZOP analysis result of variable, determine dangerous rule, reach the variable requirement that danger signal must be satisfied, otherwise just think to have occurred fault.

-risk zontation step

Once S102 has determined danger signal through step, enter step S103, divide hazardous location according to the SDG analysis result centered by described danger signal.

According to a specific embodiment of the present invention as shown in Figure 2, this risk zontation step further comprises:

1 makes the neighbouring relations matrix by SDG figure.The numerical value of each row of each row is done and is added and obtain:

C _i＝d _1i+d _2i+...d _ni，R _j＝d _j1+d _j2+...+d _jn (1)

D wherein _ijfor the element of the capable j row of adjacency matrix i, C _ifor row element and, R _jfor column element and.

2.1 determine the hazardous location under each variable i.At first, make i=1.

2.2 judge whether danger signal of i, if i is the dangerous center using it as a hazardous location of danger signal, to 2.3.Otherwise, to 3.1, calculate the affiliated hazardous location of i, according to SDG figure, with D _irecord variable i calculates immediate hazardous location, SD downstream _irecord variable i is to the distance of calculating downstream center, immediate hazardous location.

2.3 make i=i+1, to the next point of 2.2 inspections.

3.1 whether judgment variable i has link variable downwards, if do not have, i.e. and R _i=0, explanatory variable i does not have the hazardous location connected downwards, makes D _i=0, SD _i=0, to 4.1 steps.Otherwise explanatory variable i is at least 1 to the distance of calculating downstream center, immediate hazardous location, makes SD _i=1, to 3.2 steps, calculate the hazardous location that variable i connects downwards.

3.2.1 judgment variable i has several link variables downwards, if only have link variable, i.e. a R _i=1, make i*=j, wherein j meets d _ij=1, walk to 3.2.2.Otherwise link variable is greater than 1, to 3.3.1.

3.2.2 judge whether i* is danger signal, if danger signal, i* is the center, hazardous location met the demands, and makes D _i=i*, to 4.1 steps.Otherwise, enter the 3.2.3 step.

3.2.3 judge whether that i forms loop to i*, or i* is downwards without link variable, if so, i.e. i*=i or R _i*=0, make D _i=0, to 4.1 steps.Otherwise, enter the 3.2.4 step.

3.2.4 judgment variable i* has several link variables downwards, if only have link variable, i.e. a R _i*=1, make i*=j, wherein j meets d _i*j=1, walk to 3.2.6.Otherwise the link variable number is greater than 1, walk to 3.2.5.

3.2.5 variable i * has a more than link variable downwards, makes j={j (1), j (2) ... .} meets d _{i* (jk)}=1.Make i*=j (k*), wherein k* meets C _{j (k*)}=minC _j.Be about to the link variable of linking number minimum as next variable, walk to 3.2.6.

3.2.6 obtain new link variable i*, the initial variable i distance of distance adds 1.Make SD _i=SD _i+ 1.To 3.2.2.

3.3.1R _i>1, establish j*={j (1), j (2) ..., meet d _{i (jk)}=1.Make t=1, consider the feasible hazardous location of each j (t), walk to 3.3.2.

3.3.2 make i*=j (t), the possibility of investigating hazardous location under this branch road walks to 3.3.3.

3.3.3 judge whether i* is danger signal, if danger signal, i* is the center, hazardous location met the demands, and makes D _{i (t)}=i*, walk to 3.3.8.Otherwise, enter the 3.3.4 step.

3.3.4 judge whether that i forms loop to i*, or i* is downwards without link variable, if so, i.e. i*=i or R _i*=0, make D _{i (t)}=0, walk to 3.3.8.Otherwise, enter the 3.3.5 step.

3.3.5 judgment variable i* has several link variables downwards, if only have link variable, i.e. a R _i*=1, make i*=j, wherein j meets d _i*j=1, walk to 3.3.7.Otherwise the link variable number is greater than 1, walk to 3.3.6.

3.3.6 variable i * has a more than link variable downwards, makes j={j (1), j (2) ..., meet d _{i* (jk)}=1.Make i*=j (k*), wherein k* meets C _{j (k*)}=minC _j.Be about to the link variable of linking number minimum as next variable, walk to 3.3.7.

3.3.7 obtain new link variable i*, the initial variable i distance of distance adds 1.Make SD _{i (t)}=SD _{i (t)}+ 1.To 3.3.3.

3.3.8 judge whether that all j (t) have calculated, if calculated, i.e. t=R _ito 4.1, otherwise consider next j (t), make t=t+1, walk to 3.3.2.

4.1 whether judgment variable i upwards has link variable, if do not have, i.e. and C _i=0, explanatory variable i does not upwards have the hazardous location connected, and makes U _i=0, SU _i=0, to 5 steps.Otherwise explanatory variable i is at least 1 to the distance of calculating downstream center, immediate hazardous location, makes SU _i=1, to 3.2 steps, calculate the hazardous location that variable i connects downwards.

4.2.1 judgment variable i has several link variables downwards, if only have link variable, i.e. a C _i=1, make i*=j, wherein j meets U _ij=1, walk to 4.2.2.Otherwise link variable is greater than 1, to 4.3.1.

4.2.2 judge whether i* is danger signal, if danger signal, i* is the center, hazardous location met the demands, and makes U _i=i*, to 5 steps.Otherwise, enter the 4.2.3 step.

4.2.3 judge whether that i forms loop to i*, or i* is downwards without link variable, if so, i.e. i*=i or C _i*=0, make U _i=0, to 5 steps.Otherwise, enter the 4.2.4 step.

4.2.4 judgment variable i* has several link variables downwards, if only have link variable, i.e. a C _i*=1, make i*=j, wherein j meets U _i*j=1, walk to 4.2.6.Otherwise the link variable number is greater than 1, walk to 4.2.5.

4.2.5 variable i * has a more than link variable downwards, makes j={j (1), j (2) ..., meet U _{i* (jk)}=1.Make i*=j (k*), wherein k* meets C _{j (k*)}=minC _j.Be about to the link variable of linking number minimum as next variable, walk to 4.2.6.

4.2.6 obtain new link variable i*, the initial variable i distance of distance adds 1.Make SU _i=SU _i+ 1.To 4.2.2.

4.3.1C _i>1, establish j*={j (1), j (2) ..., meet U _{i (jk)}=1.Make t=1, consider the feasible hazardous location of each j (t), walk to 4.3.2.

4.3.2 make i*=j (t), the possibility of investigating hazardous location under this branch road walks to 4.3.3.

4.3.3 judge whether i* is danger signal, if danger signal, i* is the center, hazardous location met the demands, and makes U _{i (t)}=i*, walk to 4.3.8.Otherwise, enter the 4.3.4 step.

4.3.4 judge whether that i forms loop to i*, or i* is downwards without link variable, if so, i.e. i*=i or C _i*=0, make U _{i (t)}=0, walk to 4.3.8.Otherwise, enter the 4.3.5 step.

4.3.5 judgment variable i* has several link variables downwards, if only have link variable, i.e. a C _i*=1, make i*=j, wherein j meets U _i*j=1, walk to 4.3.7.Otherwise the link variable number is greater than 1, walk to 4.3.6.

4.3.6 variable i * has a more than link variable downwards, makes j={j (1), j (2) ..., meet U _{i* (jk)}=1.Make i*=j (k*), wherein k* meets C _{j (k*)}=minC _j.Be about to the link variable of linking number minimum as next variable, walk to 4.3.7.

4.3.7 obtain new link variable i*, the initial variable i distance of distance adds 1.Make SU _{i (t)}=SU _{i (t)}+ 1.To 4.3.3.

4.3.8 judge whether that all j (t) have calculated, if calculated, i.e. t=C _ito 5, otherwise consider next j (t), make t=t+1, walk to 4.3.2.

5 compare i apart from center, hazardous location, upstream and hazardous location, downstream centre distance.If SD _i<SU _i, Z _i=D _i.If SD _i>SU _i, Z _i=U _i.If SD _i=SU _i, Z _i={ U _i, D _i.So far can determine the hazardous location that variable i is affiliated, to 6 steps.

Whether as calculated 6 check i complete all variablees, if check out to 2.3, under inspection a bit.If all variablees have all calculated, regional partiting step all finishes.

-data acquisition and processing

Immune danger theory diagnostic system according to an embodiment of the invention can directly be connected with production equipment, at step S201 (Fig. 1), by such as PLC (Programmable Logic Controller), DCS (Distributed Control System (DCS)) and LIMS (Laboratory Information Management System) etc. gather required flow process variable data.Wherein known historical data, for system initialization, is set up and is determined dangerous rule and produce original antibody, and real-time online data is for fault detection and diagnosis.All historical datas are introduced into step S202 and carry out normalized by formula (1):

$X=0.5+\frac{x-\stackrel{\&OverBar;}{X}}{X_{\max }-X_{\min }}---\left(1\right)$

Wherein x is real data,

x _maxwith X _minthe mean value of this variable data, maximal value and minimum value while being respectively nominal situation.

According to one embodiment of present invention, data acquisition and treatment step can be used as implements another preposition step of carrying out before method of the present invention.

-generation antibody library

For the primary fault sample obtained, at step S203, determine its failure mode and time after the normalization of S202 step, for the variable in each hazardous location, get fault and introduce the data of rear set time length, generate first generation antibody, be denoted as Ab_fault=[Ab ₁, Ab ₂..., Ab _n].The matrix that these antibody all are comprised of the seasonal effect in time series data sample, Ab _itime series for each variable in same hazardous location.Each fault sample will, according to different hazardous locations, generate several different first generation antibody like this.First generation antibody of the same type, through step S204, generates a large amount of second generation antibodies by formula (2) variation and clone, has formed antibody library.

$X*=\left\{\begin{array}{cc}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{a}_i{X}_i+b(X_c-X_d),& \underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{a}_i=1...n>1\\ (1+\frac{b}{2}){\mathrm{<figure-callout\; id="1"\; label="X"\; filenames="HSA00000292471400021.png"\; state="\{\{state\}\}">X</figure-callout>}}_1& ...n=1\end{array}\right.---\left(2\right)$

The number that wherein n is known this kind first generation antibody, X _ifor known first generation antibody, X* is the second generation antibody generated through the variation clone.A _ibe the random decimal in 0 to 1, b is the random decimal between-1 to 1.C and d are the random integers between 1 to n.

Calculate again the threshold value of every kind of fault type antibody.

For two antibody (or antigen) of determining, can adopt the Dynamic Time Warping algorithm to calculate the diversity factor of antigen and antibody.At first get DTW for time series (Dynamic Time Warping) the algorithm calculated difference degree of identical variable, also can directly calculate by the straightforward procedure of formula (3):

${\mathrm{\&eta;}}_k=\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}|\mathrm{Ab}\left(i\right)-\mathrm{Ag}\left(i\right)|}{n}---\left(3\right)$

The diversity factor of calculating all variablees just can obtain the total variances degree vector of antigen and antibody:

η＝[η ₁，η ₂，…，η _n] (4)

Diversity factor in the calculating antibody storehouse between all antibody of the same type, note is η (i, j).The threshold value of calculating respectively each variable of the type antibody is:

${\mathrm{threshold}}_k=\underset{1\&le;i\&le;\mathrm{<figure-callout\; id="1"\; label="n\; min"\; filenames="HSA00000292471400021.png"\; state="\{\{state\}\}">n</figure-callout>}}{\mathrm{<figure-callout\; id="1"\; label="max"\; filenames="HSA00000292471400021.png"\; state="\{\{state\}\}">max</figure-callout>}}\underset{}{\min }\underset{1\&le;j\&le;n,i\&NotEqual;j}{}\left|{\mathrm{\&eta;}}_k(i,j)\right|---\left(5\right)$

-utilize danger signal to carry out fault detect

The real time data obtained by the S201 step, before carrying out fault diagnosis, is introduced into step S205, after carrying out normalization by formula (1), obtains real-time detection data x _new.Enter step S206, utilize danger signal to carry out fault detect, whether broken down by danger signal and dangerous rule monitoring flow process.

-utilize antibody library to carry out fault diagnosis

Once detect confirm to break down at step S206, process and enter step S207, the data of set time length before detecting are constantly generated respectively to different antigen according to the variable of different hazardous locations, be denoted as Ag=[Ag ₁, Ag ₂..., Ag _n].These antigens are identical with the form of the antibody of identical hazardous location in antibody library, the matrix all be comprised of the time series data sample of hazardous location internal variable, Ag _itime series for each variable.

After antigen generates, process the failure mode diagnosis that enters step S208, utilize DTW algorithm or formula (6) to calculate the diversity factor of antigen and all antibody of the interior identical hazardous location of antibody library.

${\mathrm{\&eta;}}_k=\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}|\mathrm{Ab}\left(i\right)-\mathrm{Ag}\left(i\right)|}{n}---\left(6\right)$

When each hazardous location has antibody to calculate diversity factor to be less than corresponding threshold value, think that fault is consistent with the type antibody.Otherwise be judged as a kind of new fault.

The fault diagnosis result of refer step S208, the operator can carry out Artificial Diagnosis (S301), and is input to system, and system enters step S209, accepts the result of input as final diagnostic result.

-antibody library upgrades

At step S203, using antigen as first generation antibody.Enter again step S204, by formula (2), the type antibody is carried out to clonal vaviation, upgrade the antibody in antibody library, and upgraded the threshold value of this antibody by formula (5).

Embodiment:

The method according to this invention has been applied to the fault diagnosis of the reaction realistic model of secondary continuous stir reactor.

The flow process of secondary continuous stir reactor as shown in Figure 3, this flow process be by the hot water in uniform temperature (95 ℃) and cold water (40 ℃) in continuous stir reactor R-1, mix and be cooled to definite temperature (50 ℃) after enter continuous stir reactor R-2, the flow of cold water is fixing (60kg/h) relatively, and the flow of hot water is determined by the fluid level controller of R-1.Obtain determining the chilled water of flow (360kg/h) and temperature (30 ℃) after cooling, and by adding appropriate hot water (95 ℃) to regulate the liquid level in R-2.Two continuous stir reactors have the chilled water of band control to meet the liquid material temperature requirement in still.

Monitored parameters comprises temperature T r1 and the liquid level Lr1 in continuous stir reactor R-1, entrance cold water flow and temperature F101, T101, cold water flow and temperature F102, T102, rate of discharge and temperature F102, T102, the flow of chilled water and temperature F104, T104, the temperature T r2 of continuous stir reactor R-2 and liquid level Lr2, entrance hot water flow and temperature F201, T201, rate of discharge and temperature F102, T102, the flow of chilled water and temperature F104, T103.

The fundamental purpose of this flow process is exactly in order to obtain determining the water of flow and temperature.The control index of whole process main operating parameters and controller is as shown in table 1.

Table 1 nominal situation parameter arranges

Operating parameter	Numerical value	Control target	Numerical value
				The R-1 cold water flow	60±5kg/h	The R-1 temperature	50℃
Cold water temperature	40±2℃	The R-2 temperature	30℃
				The hot water temperature	95±2℃	The R-1 liquid level	50％
Cooling water temperature	30℃	The R-2 liquid level	50％
						The R-2 outlet temperature	360kg/h

At first according to step S101, flow process is carried out to HAZOP analysis and SDG analysis.Analyze the dangerous hidden danger of this technological process of discovery and main dangerous matter sources through HAZOP.Analyze the relation obtained between SDG figure confirmation variable through SDG, as shown in Figure 4.

Enter again step S102, confirm danger signal and dangerous rule.According to Dow Chemical index result of calculation and LOPA analysis result, find Tr1, Tr2, the harmful grade of Lr2 is higher, and these three variablees are decided to be to danger signal.Because these three variablees are control variable, according to the HAZOP analysis result, the concrete numerical value of its variable and stability are all very crucial for flow process again.Therefore, these three danger signals are determined to dangerous rule is as follows:

$0.9*V_{\min }<{\tilde{V}}_i<1.1*V_{\max }$ $\left(7\right)$

$|{\tilde{V}}_i-{\tilde{V}}_{i-1}|<1.1*{|V_k-V_{k-1}|}_{\max }$

Wherein

for the real time data value of danger signal, V _khistorical data value for danger signal.Subscript i and k mean the time sequence number of variable.V _minand V _maxbe respectively minimum value and the maximal value of danger signal historical data.

According to the SDG of secondary continuous stir reactor figure (seeing Fig. 4), determine three danger signal: Tr1, Tr2, Lr2, then by all variablees according to danger signal step as shown in Figure 2, be divided into three hazardous locations.Take T101 as example, at first make adjacency matrix as shown in table 2.

Table 2 secondary continuous stir reactor fault diagnosis test result

As calculated, the hazardous location 1 centered by Tr1, comprise T101, T102, T103, T104, F101, F102, F103, F104, Tr1; Hazardous location 2 centered by Tr2, comprise T103, T201, T202, T203, F103, F201, F203, Tr2; Hazardous location 3 centered by Lr2, comprise F101, F102, Lr1, F103, F201, F202, Lr1, Lr2.

When carrying out preliminary analysis and planning, obtain the detection data of these all variablees by step S201, sampling time interval is 5 seconds.

Bring the normal sample in the historical data after S202 walks normalization into formula (7), help the details about dangerous rule in step S102 to determine.Enter again step S202

In addition, too high cold water flow F102, the V-3 valve turns down, each 2 groups of data of the malfunctioning three kinds of faults of LC-2 controller, carry out normalization at step S202, then near step S203 gets the fault introducing time 10 sampling number certificates, according to the division of three hazardous locations, generate respectively three independently antibody, and carry out variation and clone's generation antibody library of antibody at step S204.

Completed like this initialization of fault diagnosis system, then real time data has been carried out to fault detection and diagnosis after step S205 normalization.This example has adopted altogether 13 groups of real time data samples to verify method.

At first take sample 1 as example, sample 1, for slightly improving the sample produced after cold water flow, after the normalization of S205 step, enters the S206 step and utilizes danger signal to carry out fault detect.The danger signal data that calculate all sampled points all meet dangerous rule, that is to say that this disturbance does not produce actual fault effects for the production of this technological process, so testing result think that this sample is nominal situation.And that the method for diagnosing faults that utilizes other is easy to that this sample is diagnosed as to cold water flow is too high and report to the police.

Take sample 2 as example again, the fault sample that sample 2 produces for significantly improving cold water flow, after the normalization of S205 step, enter the S206 step and utilize danger signal to carry out fault detect, after fault is introduced 15s, the danger signal data have exceeded the restriction range of dangerous rule, think fault has occurred, enter the S207 step by ten sampling number certificates before failure detection time, according to the division of hazardous location, generate 3 independently antigens.Walk by S208 again, utilize the diversity factor of antibody to calculate the tracing trouble kind, find that in 3 antigens and the too high antibody library of cold water flow, 3 kinds of Antibody difference ratios are less than the type fault threshold, and all be greater than its corresponding threshold value with the other types Antibody difference ratio.Therefore diagnostic result is the too high fault of cold water flow, confirms that through step S209 the Artificial Diagnosis result is cold water flow too high, enters the S203 step, by antigen, generates new antibodies.Walk by S204 again, carry out clone and the variation of antibody, upgrade antibody library.

The all fault diagnosis results of this example are as shown in table 3.

Table 3 secondary continuous stir reactor fault diagnosis test result

Sequence number	Fault category	Detect time delay	Diagnostic result	Computing time
						1	Normal *	-	Normally
2	1. cold water flow is too high	15s	Fault	1	0.2458s
						3	1. cold water flow is too high	15s	Fault	1	0.1640s
4	1. cold water flow is too high	15s	Fault	1	0.1575s
						5	2.V-3 valve turns down	15s	Fault	2	0.1667s
6	2.V-3 valve turns down	15s	Fault	2	0.1626s
						7	2.V-3 valve turns down	15s	Fault	2	0.1595s
8	3.LC-2 controller is malfunctioning	300s	Fault	3	1.1324s
						9	3.LC-2 controller is malfunctioning	145s	Fault	3	0.2985s
10	3.LC-2 controller is malfunctioning	130s	Fault	3	0.3440s
						11	4.TC-2 controller is malfunctioning	255s	New fault	0.7584s
12	4.TC-2 controller is malfunctioning	90s	Fault	4	0.3105s
						13	4.TC-2 controller is malfunctioning	135s	Fault	4	0.4054s

* this normal sample, for slightly improving the sample produced after cold water flow, does not destroy dangerous rule.

Should be understood that, the description of in above narration and explanation, the present invention being carried out just illustrates but not is determinate, and do not breaking away under the prerequisite of the present invention limited as appended claims, can carry out various changes, distortion and/or revise above-described embodiment.

Claims (4)

1. the method for diagnosing faults of the process industry based on immune danger theory is characterized in that comprising:

The risk zontation step, for dividing hazardous location;

Antibody generates step, and the historical data in the primary fault sample in each described hazardous location generates antibody;

Whether failure detection steps, break down for monitoring flow process;

SDG and Safety Evaluation Analysis step, for carrying out corresponding analysis to the original process flow process;

Danger signal and dangerous regular determining step, for being determined danger signal and dangerous rule by described SDG and HAZOP analysis result;

Antigen generates step, in the situation that described failure detection steps is determined with fault, exists, and by real time data in each hazardous location of schedule time length before the detection constantly of described failure detection steps, generates respectively a plurality of antigen;

The failure mode diagnosis algorithm, utilize described antigen and described antibody, judges the kind of fault;

Data acquisition and treatment step, gather required flow process variable data, and described flow process variable data comprises historical data and described real time data, and described historical data comprises original normal sample and described primary fault sample,

Described danger signal and dangerous regular determining step comprise:

Thereby the hazard level of the judgment variable as a result of analyzing with Dow Chemical index and LOPA is determined danger signal,

Determine the danger rule of described danger signal with described HAZOP analysis result, and

Divide danger signal with described SDG and HAZOP analysis result,

Wherein said risk zontation step further comprises: centered by described danger signal, according to the SDG analysis result, divide described hazardous location.

2. according to the method for diagnosing faults of the process industry based on immune danger theory of claim 1, it is characterized in that

Described SDG and Safety Evaluation Analysis step comprise:

At least one analysis of carrying out that SDG analyzes and analyzing, select analysis based on the safety checklist method and the analysis based on fault tree analysis from HAZOP,

Calculate the Dow Chemical index, and

Carry out the LOPA analysis.

3. the fault diagnosis system of the process industry based on immune danger theory is characterized in that comprising:

The risk zontation device, for dividing hazardous location;

The antibody generating apparatus, the historical data in the primary fault sample in each described hazardous location generates antibody;

Whether failure detector, break down for monitoring flow process;

The Safety Evaluation Analysis device, for carrying out SDG and reaching Safety Evaluation Analysis to the original process flow process;

Danger signal and dangerous rule are determined device, for determined danger signal and dangerous rule by described SDG and HAZOP analysis result;

The antigen generating apparatus, exist in the situation that described failure detector is determined with fault, by real time data in each hazardous location of schedule time length before the detection constantly of described failure detector, generates respectively a plurality of antigen;

The failure mode diagnostic device, utilize described antigen and described antibody, judges the kind of fault;

Data acquisition and treating apparatus, gather required flow process variable data, and described flow process variable data comprises historical data and described real time data, and described historical data comprises original normal sample and described primary fault sample,

Described danger signal and dangerous rule determine that device comprises:

Thereby the hazard level of the judgment variable as a result of analyzing with Dow Chemical index and LOPA is determined the part of danger signal,

Determine the part of the danger rule of described danger signal with described HAZOP analysis result, and

Divide the part of danger signal with described SDG and HAZOP analysis result,

Wherein said risk zontation device further comprises: centered by described danger signal, according to the SDG analysis result, divide described hazardous location.

4. according to the fault diagnosis system of the process industry based on immune danger theory of claim 3, it is characterized in that

Described Safety Evaluation Analysis device comprises:

For carrying out that SDG analyzes and from the part of at least one analysis that HAZOP analyzes, analysis based on the safety checklist method and the analysis based on fault tree analysis are selected,

For calculating the part of Dow Chemical index, and

Carry out the part of LOPA analysis.

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