CN102298978A - MFM (multilevel flow model)-based indeterminate fault diagnosis method for nuclear power plant for ship - Google Patents

Abstract

The invention provides an uncertain fault diagnosis method for a nuclear power plant based on a multilayer flow model. When the measured value of the variable representing each functional component detected by the sensor exceeds the normal range, the alarm is displayed as a fault, and the location of the abnormal component is marked; according to the flow direction in the MFM model, the causal relationship that may lead to the fault is intercepted; the logical loop is cut off , form a micro-fault tree; convert the micro-fault tree into a micro-GO-FLOW method; process and merge the micro-GO-FLOW models, and combine them into a GO-FLOW model with the intersection of each abnormal function detected by the sensor as the final signal; Input the GO-FLOW model into the software for calculation, and finally get the probability of the intersection of each abnormal function caused by each basic fault. The invention is suitable for solving complex system problems, is easy to verify, has accurate results, is fast and meets the needs of real-time diagnosis.

Description

Uncertain fault diagnosis method for marine nuclear power plant based on multilayer flow model

technical field

The present invention relates to a complex system fault diagnosis method, specifically a fault diagnosis method based on a multilayer flow model proposed for the complex and uncertain logical relationship between marine nuclear power plant faults and their symptoms . the

Background technique

Fault diagnosis refers to the application of test analysis methods and diagnostic theories to identify the mechanism, cause, location and degree of faults that occur during system operation, and provide support for further formulating maintenance plans for faulty equipment and taking appropriate emergency operations. System fault diagnosis is based on equipment state identification, starting from the abnormal state of the equipment (that is, symptoms), and analyzing the causal relationship between symptoms according to the principle of system conservation and control rules, so as to realize fault location, characterization and cause determination. the

The object involved in the present invention is a marine nuclear power plant. During the actual operation of a marine nuclear power plant, the structure and performance of the equipment are affected by factors such as noise, swing and seawater corrosion, showing different deterioration trends: on the one hand, different process types The equipment is affected by external factors to different degrees, and exhibits different types of failures; Aspects such as performance characteristics may also differ. In particular, the marine nuclear power plant is a small sample problem, lacks complete fault diagnosis knowledge, and the fault mechanism of some equipment is not clear; or the fault mechanism of the equipment is clear, but due to the complexity of the system itself, other equipment and The impact of the system is difficult to judge. In addition, due to the large size of the marine nuclear power plant system, limited by the space of the hull, it cannot achieve independent functions, and there is a phenomenon of equipment sharing. Once a failure occurs, a large number of system parameters will be abnormal, and the causal relationship between symptoms is difficult to determine. For example: there is uncertainty between appearance (fault symptom) and cause (failure mode), and one fault symptom may correspond to multiple fault modes; conversely, a certain fault mode may also show multiple fault symptoms. Finally, the operating environment of the equipment, control process, operator intervention, sensor detection, and expert knowledge all have uncertainties. Therefore, a key problem to be solved in the fault diagnosis of marine nuclear power plant is to recognize and deal with the uncertainty in fault diagnosis. the

Uncertainty can be understood as making judgments in the absence of sufficient information, which is the essential feature of intelligence problems; reasoning is the human thinking process, which is the process of starting from known facts and using relevant knowledge to gradually draw conclusions. The so-called uncertainty reasoning is a thinking process that starts from incomplete initial evidence and uses uncertainty knowledge to finally draw a certain degree of uncertainty but a reasonable or near-reasonable conclusion. the

Uncertainty reasoning can be qualitative, quantitative, or a combination of qualitative and quantitative. Uncertainty reasoning can be divided into two categories: symbolic reasoning and numerical reasoning. Among them, the symbolic reasoning method has less information loss in the reasoning process, but has a large amount of calculation. Typical methods include Endorsement Theory and so on. Although the numerical reasoning method has a certain amount of information loss in the reasoning process, it is easy to implement, and typical methods such as probabilistic reasoning and so on. At present, in the field of uncertainty reasoning, numerical reasoning methods are mainly used. the

The earliest method of probabilistic reasoning is the uncertainty factor method, which was proposed by Shortliffe and Buchanan in 1975 to deal with uncertain information in the MUCIN system. In 1976, Duda et al. proposed the subjective Bayesian method and applied it to the design of the prospecting expert system PROSPECTOR. The subjective Bayesian method mainly uses the Bayesian formula and the deformed Bayesian formula to calculate the probability of a hypothesis (a given fault) occurring under a given evidence. In 1985, Per1 gave the Bayesian network method. The Bayesian network is a directed acyclic graph, in which the nodes are variables, and the arcs represent the dependencies between related variables. The values of variables correspond to evidence and assumptions. The degree of trust between variables is expressed by conditional probability. In recent years, Chinese scholar Zhang Qin proposed the causal directed graph method on the basis of Bayesian networks, which solved the problem that Bayesian networks cannot handle loops. the

However, when using existing system modeling methods such as Bayesian networks and causal directed graphs to organize uncertain knowledge, the uncertainty reasoning model built on the basis of expert knowledge is inherently arbitrary, and different experts use the same method to target The models established for the same system problem may also be different. In addition, due to the complexity of the system itself, the built model is usually huge, resulting in a complicated calculation process and affecting the real-time performance of diagnosis. the

The main purpose of the invention is to propose a standardized, modularized and hierarchical artificial system uncertainty knowledge expression and reasoning method for fault diagnosis of marine nuclear power plants. This method uses multilevel flow models (Multilevel Flow Models, MFM) as the system knowledge representation method. MFM was proposed by Morten Lind of the Technical University of Denmark in the 1980s. From the perspective of cognitive science, semiotics is used to describe the design purpose and means of realization of complex systems from the perspectives of system goals, functions and physical components. Different from other modeling methods, MFM not only describes the process behavior of complex systems, but also emphasizes the purpose of these behaviors. Objectives, functions, and physical components are connected together through different relationships (including achievement relationships, realization relationships, and conditional relationships), so that MFM can perform multi-level analysis of the target system in the two directions of "means-purpose" and "part-whole". abstract. MFM introduces the concept of "Flow", and the propagation of "Flow" follows the principle of conservation. Therefore, using MFM to describe the system has the advantages of model specification, simplicity, clarity, understanding, modification and verification. the

In terms of reasoning methods, combined with the success of BN, probability is used to measure uncertainty knowledge, and the response relationship between failure modes and symptoms, and between symptoms and symptoms is described. In the multi-layer flow model, each function has corresponding constraints, which reflect the causal relationship between each function. Therefore, it can be based on the connection relationship between various functions in the multi-layer flow model. Each network is gradually expanded according to the relationship between functions and functions, and the causal relationship between each failure mode and each symptom is found and compared to complete diagnostic reasoning. the

Effect of the present invention is mainly reflected in:

1) The proposed method has standardized modeling, is simple and easy to modify, and is suitable for solving complex system problems. the

2) The logical relationship of the model is clear, and the relationship between symptoms and faults conforms to the principle of conservation, which is easy to verify. the

3) Using the exact solution method, the inference results obtained are accurate. the

4) Modular calculation is adopted, which is fast and meets the needs of real-time diagnosis. the

Contents of the invention

The purpose of the invention is to provide an uncertain fault diagnosis method capable of meeting real-time requirements for a marine nuclear power plant. the

Before setting forth the specific steps of the present invention, it is necessary to explain the meanings of some nouns and letters involved in the text as follows:

Goal G _i : Indicates the expected purpose of the system, usually realized by one or more functions through the flow net.

Flow network NETWORK: a set of material, energy or information flow composed of the same attributes and interrelated functions, used to achieve the goal. the

Function F _i : the functional unit in the system model, which is the abstraction of the actual physical components performing their functions. The classification and constraints of its functions are shown in Figure 2.

State variable S _i : indicates the operating state of the functional unit. In practice, S _i can be multi-state. In this application, it is assumed that all functions (except the Barrier function) are three-state, including:

-OK status, indicating that the function is normal;

-H state, abnormally high state of water level, flow, pressure, temperature, etc.;

-L state, abnormally low state of water level, flow, pressure, temperature, etc. the

Barrier function is two states

-OK status, indicating that the function is working normally;

-N state, Barrier cannot complete the specified function;

Fault: The state that the system cannot or partially fails to complete its designated functions, which can be divided into two types in this manual.

●Component failure B _i : It is the failure mode of the physical component of the analyzed function, such as partial or complete damage to the equipment, wrong opening or closing of the switching element caused by the operator's misoperation, etc.

●System failure X _i : refers to the failure caused by the failure of physical components other than the analyzed function, corresponding to the abnormal state of function, and can be divided into the following three types:

1. Upstream failure: A failure due to an abnormal state of an upstream function. the

2. Downstream failure: A failure due to an abnormal state of a downstream function. the

3. Conditional failure: a failure caused by the condition required for the function not being fulfilled. the

Events: Divided into basic events and result events. The basic event is located at the bottom of the causal tree, corresponding to the physical failure mode; the result event is divided into intermediate events and top events, corresponding to the functional state, the intermediate event is not only the cause event of a certain event, but also the result event of another event; the top event is The target event to be analyzed is usually the top event for causal analysis. the

Influence intensity p _ij : Indicates the possibility that the occurrence of a certain event will cause the occurrence of the result event (0≤p _ij ≤1, 0 indicates that there is no relationship between the two, and 1 indicates that there is a complete correlation between the two).

Causal subtree: The causal analysis is carried out with a certain state of the target function as the top event, and the bottom event is only composed of component failures of the function itself and system failures directly related to the function. The tree structure is called the causal subtree. the

Causal tree: The causal analysis is carried out with a certain state of the target function as the top event, and the tree structure formed by the component failure of each function as the bottom event is called the causal tree. the

Concrete implementation steps of the present invention are as follows:

In the first step, when the system fails, firstly, according to the system observation (that is, the system operating parameters), it is determined whether each goal is achieved, and the analysis network is determined. the

In the multilayer flow model, the goal is achieved by one or more functions in the form of a flownet. Therefore, when a certain goal is achieved, it can be considered that the main function of the flownet that realizes the goal at the current moment is normal, so the flownet as a whole can be regarded as an unexpanded event and not analyzed. the

The second step is to determine the running state S _i of each function F _i according to the system observation. The event with S _i as the top event is expanded into a causal subtree. If the operating state S _i of a certain function F _i is known, it will be expanded according to the known state; if the state variable S _i of a certain function F _i is unknown, then all states (H, L and OK) corresponding to S _i need to be Events were analyzed separately.

The third step is to obtain the causal tree of the state variable S _i of the function F _i . On the basis of the causal subtree targeted at each functional state variable obtained in the first step, connect and expand the intermediate events until the basic event, and obtain the causal tree with S _i as the top event.

Due to the non-reflexive nature of the system, that is, the event cannot be its own cause, so when the causal tree is formed, it needs to be truncated at the point where the causal chain forms a causal loop with the upper event. the

The fourth step is to translate the causal tree above into a micro GO-FLOW (a probability analysis method) model, and the meanings represented by each component in the GO-FLOW model are shown in Figure 3. the

The translation rules are as follows:

1) The basic event is represented by the No. 25 operator of the start signal generator. The No. 25 operator is used to simulate a single signal generator in GO-FLOW and has only one output signal. In this method the influence propagation of elementary events can be simulated. The probability of occurrence of its basic events can be expressed by the strength of the input signal. the

2) The influence strength is represented by the No. 21 operator of the two-state element, and the No. 21 operator is used in GO-FLOW to simulate components with only two states. In this method, it can be used to simulate the response intensity p _ij representing the influence relationship between the cause and the effect.

3) The logical relationship "or" is represented by the No. 22 operator. The No. 22 operator is used to simulate the OR gate logic relationship of multiple signals in GO-FLOW. There are multiple input signals and one output signal. In this method, use To simulate the logical "OR" relationship of multiple events. the

4) The logical relationship "AND" is represented by operator No. 30. Operator No. 30 is used to simulate the AND gate logic relationship of multiple signals in GO-FLOW. There are multiple input signals and one output signal. In this method, use To simulate the logical "AND" relationship of multiple events. the

5) Intermediate events can be represented by the combination of basic events and impact intensity through logical "and" and logical "or".

The fifth step is to obtain the intersection of the state parameters S _i of all abnormal function states to obtain the evidence E.

In this specification, the intersection of all monitored abnormal function states S _i is defined as evidence, that is, E=S ₁ ·S ₂ ···S _n (n is the number of functions in abnormal states)

The sixth step is to calculate the impact intensity P _i of each component failure B _i on the evidence.

By the Bayesian formula are: $P(B_i/\mathrm{E.})=\frac{P_r(B_i\·\mathrm{E.})}{P_r\left(\mathrm{E.}\right)}$

Comparing the size of the failure P _i of its various components, the greater the probability value, the greater the possibility of occurrence.

Description of drawings

Fig. 1 is the brief flowchart of this method;

Figure 2 is the functional diagram and constraints in MFM;

Figure 3 is the function definition table of each operator of GO-FLOW;

Figure 4 is a schematic diagram of the nuclear power system;

Fig. 5 is the MFM diagram of the steam generator system shown in Fig. 2;

Figure 6-1 to Figure 6-9 are the causal subtrees of each functional state, where:

Figure 6-1: The causal subtree expanded with F1(H) as the top event;

Figure 6-2: The causal subtree expanded with F2(L) as the top event;

Figure 6-3: The causal subtree expanded with F3(H) as the top event;

Figure 6-4: The causal subtree expanded with F4(OK) as the top event;

Figure 6-5: The causal subtree expanded with F5 (OK) as the top event;

Figure 6-6: The causal subtree expanded with F9(H) as the top event;

Figure 6-7: The causal subtree expanded with F10(OK) as the top event;

Figure 6-8: The causal subtree expanded with F11(OK) as the top event;

Figure 6-9: The causal subtree expanded with F12(OK) as the top event;

Figures 7-1 through 7-4 are causal trees for abnormal functioning states, where:

Figure 7-1: The causal tree expanded with F1(H) as the top event;

Figure 7-2: The causal tree expanded with F2(L) as the top event;

Figure 7-3: The causal tree expanded with F3(H) as the top event;

Figure 7-4: The causal tree expanded with F9(H) as the top event;

Figure 8-1 to Figure 8-4 are the GO-FLOW models formed by the translation of the causal tree in Figure 5, where:

Figure 8-1: The GO-FLOW model translated from the causal tree with F1(H) as the top event;

Figure 8-2: The GO-FLOW model translated from the causal tree with F2(L) as the top event;

Figure 8-3: The GO-FLOW model translated from the causal tree with F3(H) as the top event;

Figure 8-4: The GO-FLOW model translated from the causal tree with F9(H) as the top event;

Figure 9 is the evidence-based GO-FLOW model;

Figure 10 is a table of functional semantics and evaluation parameters of each function in the MFM model; the

Figure 11 is the obtained inference results. the

Detailed ways

Fig. 1 is a brief flow chart of this method. This method is based on the premise of a large amount of nuclear power operation experience, and the experts in this field evaluate the function of nuclear power plant operation, establish a multilayer flow model, and set and express each functional component variable (temperature , pressure, water level, etc.) normal change domain, which defines "H", "L" and "OK" of the functional state. And according to the actual operation of the equipment, the expert will give the probability value of the mutual response between each function. the

Referring to Fig. 4, the nuclear power system includes a reactor S1, a pressurizer S2, a steam generator S3, a steam turbine S4, a feed water pump S5 and a main pump S6. The multilayer flow model established by the steam generator system is shown in Figure 5, where B1 is the steam generator heat transfer tube rupture accident (SGTR); B2 is the main steam pipe rupture accident (MSLB); B3 is the main feed water pump stoppage . G0 is the power generation goal of the nuclear power plant; G1 is the goal of providing coolant to the steam generator; G2 is the goal of providing cooling water to the steam generator; G3 is the radioactive detection of the secondary circuit; G4 is the external power supply for the water pump. The physical meaning represented by each functional component is shown in Figure 8. the

The response relationship of each functional component given by experts is as follows:

B1=10 ^-5 , B2=10 ^-5 , B3=10 ^-8

B1→F8(N):1.0 B2→F4(L):0.9 B2→F10(L):0.9 B3→F12(L):1.0

F1(H)→F2(H):0.9 F1(H)→F2(OK):0.1

F1(OK)→F2(OK):0.8 F1(OK)→F2(H):0.1 F1(OK)→F2(L):0.1

F1(L)→F2(L):0.9 F1(L)→F2(OK):0.1

F2(H)→F3(H):0.9 F2(H)→F3(OK):0.1 F2(H)→F1(L):0.8 F2(H)→F1(OK):0.2

F2(OK)→F3(OK):0.8 F2(OK)→F3(H):0.1 F2(OK)→F3(L):0.1 F2(OK)→F1(OK):0.8F2(OK)→F1 (H):0.1 F2(OK)→F1(L):0.1

F2(L)→F3(L):0.8 F2(L)→F3(OK):0.2 F2(L)→F1(H):0.9 F2(L)→F1(OK):0.1

F3(H)→F4(H):0.9 F3(H)→F4(OK):0.1 F3(H)→F2(L):0.8 F3(H)→F2(OK):0.2

F3(OK)→F4(OK):0.8 F3(OK)→F4(H):0.1 F3(OK)→F4(L):0.1 F3(OK)→F2(OK):0.8F3(OK)→F2 (H):0.1 F3(OK)→F2(L):0.1

F3(L)→F4(L):0.9 F3(L)→F4(OK):0.1 F3(L)→F2(H):0.8 F3(OK)→F2(OK):0.2

F4(H)→F5(H):0.8 F4(H)→F5(OK):0.2 F4(H)→F3(L):0.9 F4(H)→F3(OK):0.1

F4(OK)→F5(OK):0.8 F4(OK)→F5(H):0.1 F4(OK)→F5(L):0.1 F4(OK)→F5(OK):0.8F4(OK)→F5 (H):0.1 F4(OK)→F5(L):0.1

F4(L)→F5(L):0.9 F4(L)→F5(OK):0.1 F4(L)→F3(H):0.9 F4(L)→F3(OK):0.1

F5(H)→F4(L):0.9 F5(H)→F4(OK):0.1

F5(OK)→F4(OK):0.8 F5(OK)→F4(L):0.1 F5(OK)→F4(H):0.1

F5(L)→F4(H):0.8 F5(L)→F4(OK):0.2

F6(H)→F7(H):0.9 F6(H)→F7(OK):0.1

F6(OK)→F7(OK):0.8 F6(OK)→F7(L):0.1 F6(OK)→F7(H):0.1

F6(L)→F7(L):0.9 F6(L)→F7(OK):0.1

F7(H)→F6(L):0.8 F7(H)→F6(OK):0.2

F7(OK)→F6(OK):0.8 F7(OK)→F6(L):0.1 F7(OK)→F6(H):0.1

F7(L)→F6(H):0.9 F7(L)→F6(OK):0.1

F8(N)→F6(L):0.9 F8(N)→F6(OK):0.1 F8(N)→F9(H):0.9 F8(N)→F9(OK):0.9F8(N)→F3 (H):0.9 F8(N)→F3(OK):0.1

F8(OK)→F6(OK):1.0 F8(OK)→F9(OK):1.0 F8(OK2)→F3(OK):1.0

F9(H)→F10(H):0.8 F9(H)→F10(OK):0.2 F9(H)→F12(L):0.9 F9(H)→F12(OK):0.1

F9(OK)→F10(OK):0.8 F9(OK)→F10(H):0.1 F9(OK)→F10(L):0.1 F9(OK)→F12(OK):0.8 F9(OK)→F12 (H):0.1 F9(OK)→F12(L):0.1

F9(L)→F10(L):0.9 F9(L)→F10(OK):0.1 F9(L)→F12(H):0.9 F9(L)→F12(OK):0.1

F10(H)→F11(H):0.8 F10(H)→F11(OK):0.2 F10(H)→F9(L):0.9 F10(H)→F9(OK):0.1

F10(OK)→F11(OK):0.8 F10(OK)→F11(H):0.1 F10(OK)→F11(L):0.1 F10(OK)→F9(OK):0.8 F10(OK)→F9 (H):0.1 F10(OK)→F9(L):0.1

F10(L)→F11(L):0.9 F10(L)→F11(OK):0.1 F10(L)→F9(H):0.9 F10(L)→F9(OK):0.1

F11(H)→F12(H):0.8 F11(H)→F12(OK):0.2 F11(H)→F10(L):0.9 F11(H)→F10(OK):0.1

F11(OK)→F12(OK):0.8 F11(OK)→F12(H):0.1 F11(OK)→F12(L):0.1 F11(OK)→F10(OK):0.8 F11(OK)→F10 (H):0.1 F11(OK)→F10(L):0.1

F11(L)→F12(L):0.9 F11(L)→F12(OK):0.1 F11(L)→F10(H):0.9 F11(L)→F10(OK):0.1

F12(H)→F9(H):0.8 F12(H)→F9(OK):0.2 F12(H)→F11(L):0.9 F12(H)→F11(OK):0.1

F12(OK)→F9(OK):0.8 F12(OK)→F9(H):0.1 F12(OK)→F9(L):0.1 F12(OK)→F11(OK):0.8 F12(OK)→F11 (H):0.1 F12(OK)→F11(L):0.1

F12(L)→F9(L):0.9 F12(L)→F9(OK):0.1 F12(L)→F11(H):0.9 F12(L)→F11(OK):0.1

Now assume that the entire system has a SGTR failure, and the status of each functional component detected by the detector is:

F1(H), F2(L), F3(H), F4(OK), F5(OK), F6(OK), F7(OK), F8(N), F9(H), F10(OK), F11(OK), F12(OK). the

The first step is to check whether each goal is achieved and determine the network to be analyzed. the

According to the detected status of each function, it can be known that G0 is not achieved, G1 is achieved, G2 is not achieved, G3 is achieved, and radioactivity is detected in G4. Therefore, the flow nets to be analyzed are N0, N2 and function F8. the

In the second step, according to the state variable S _i of the function F _i , the causal subtree of the state variables of all functional components is obtained, as shown in Fig. 6 .

The third step is to further simplify on the basis of the first step, expand the unexpanded events in the causal subtree, and obtain the causal tree of the state variable S _i of the function F _i , as shown in Figure 7.

The fourth step is to translate the causal graph formed in the second step into the micro GO-FLOW model shown in Figure 8. the

The fifth step is to obtain the intersection of the state variables S _i of all abnormal function states, and obtain the GO-FLOW model with the evidence E as the target, as shown in Figure 9.

Evidence E＝F ₁ (H) · F ₂ (L) · F ₃ (H) · F ₉ (H)

The sixth step is to calculate the impact intensity P _i of each component failure B _i on the evidence.

By the Bayesian formula are: $P({\mathrm{<figure-callout\; id="1"\; label="B"\; filenames="HDA0000061687350000022.png,HDA0000061687350000042.png"\; state="\{\{state\}\}">B</figure-callout>}}_1/\mathrm{E.})=\frac{P_r({\mathrm{<figure-callout\; id="1"\; label="B"\; filenames="HDA0000061687350000022.png,HDA0000061687350000042.png"\; state="\{\{state\}\}">B</figure-callout>}}_1\&CenterDot;\mathrm{E.})}{P_r\left(\mathrm{E.}\right)}=0.05824$ $P({\mathrm{<figure-callout\; id="2"\; label="B"\; filenames="HDA0000061687350000091.png,HDA0000061687350000121.png"\; state="\{\{state\}\}">B</figure-callout>}}_2/\mathrm{E.})=\frac{P_r({\mathrm{<figure-callout\; id="2"\; label="B"\; filenames="HDA0000061687350000091.png,HDA0000061687350000121.png"\; state="\{\{state\}\}">B</figure-callout>}}_2\&Center\; Dot;\mathrm{E.})}{P_r\left(\mathrm{E.}\right)}=0.94176$

The results obtained are shown in Figure 11. the

Claims (2)

1. a nuclear power unit is characterized in that based on the indeterminate fauit diagnostic method of multilayer flow model:

The first step, when system broke down, at first measurement was that system operational parameters determines that whether each target is reached, and determines phase-split network according to systematic perspective;

Second step, measure according to systematic perspective, determine each function F _iRunning status S _i, with S _iFor top event is launched into the cause and effect subtree respectively, if certain function F _iRunning status S _iFor known, then launch according to known state; If certain function F _iState variable S _iBe the unknown, then need S _iAll corresponding state events are deployment analysis respectively;

In the 3rd step, ask for function F _iState variable S _iCause and effect tree, on the basis of the resulting cause and effect subtree that is target with each functional status variable of the first step, connect and also launch intermediate event till elementary event, obtain with S _iCause and effect tree for top event;

In the 4th step, above-mentioned cause and effect tree is translated into little GO-FLOW model;

The 5th goes on foot, and asks for the state parameter S of all abnormal function states _iCommon factor, obtain evidence E;

In the 6th step, ask for each unit failure B _iEvidence influenced intensity P _i,

By the Bayesian formula: $P(B_i/E)=\frac{P_r(B_i\&CenterDot;E)}{P_r\left(E\right)}$

Compare its each unit failure P _iSize, probable value is big more, the possibility of generation is big more.

2. nuclear power unit according to claim 1 is characterized in that based on the indeterminate fauit diagnostic method of multilayer flow model: described that cause and effect tree is translated into the translation rule of little GO-FLOW model is as follows:

1) elementary event is represented by No. 25 operational characters of start signal generator, and No. 25 operational character is used for simulating the mono signal generator in GO-FLOW, and an output signal is only arranged;

2) influence intensity and represent that by No. 21 operational characters of two-state element No. 21 operational character is used for simulating the component that has only two states in GO-FLOW;

3) logical relation " or " represent that with No. 22 operational characters No. 22 operational characters are used for simulating a plurality of signals in GO-FLOW or gate logic relation has a plurality of input signals and an output signal;

4) logical relation " with " represent with No. 30 operational characters, No. 30 operational characters in GO-FLOW, be used for simulating a plurality of signals with the gate logic relation, a plurality of input signals and an output signal are arranged;

5) intermediate event by elementary event with influence intensity and represent by the combination of logical and logical "or".

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