Improvement of fault diagnosis efficiency in nuclear power plants using hybrid intelligence approach

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Progress in Nuclear Energy 76 (2014) 122e136

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Progress in Nuclear Energy

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Improvement of fault diagnosis efficiency in nuclear power plantsusing hybrid intelligence approach

Yong-kuo Liu a,*, Chun-li Xie b, Min-jun Peng a, Shuang-han Ling c

a Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin 150001, Chinab Traffic College, Northeast Forestry University, Harbin 150040, Chinac Suzhou Nuclear Power Research Institute, Suzhou 215004, China

a r t i c l e i n f o

Article history:Received 18 December 2013Received in revised form27 February 2014Accepted 1 May 2014

Keywords:Fault diagnosisNuclear power plantArtificial neural networkData fusionSigned directed graph (SDG)

* Corresponding author. Tel.: þ86 (0) 451 8256982569622.

E-mail address: [email protected] (Y.-k. Liu).

http://dx.doi.org/10.1016/j.pnucene.2014.05.0010149-1970/� 2014 Elsevier Ltd. All rights reserved.

a b s t r a c t

Different types of faults could occur in a nuclear power plant, and there was no direct correspondencebetween a specific fault and its symptoms. So, a hybrid intelligence approach is proposed for the faultdiagnosis at a nuclear power plant. Depending up the symptoms observed and the progress of faultdiagnosis process, different fault diagnosis technologies, such as artificial neural network, data fusion andsigned directed graph, could be combined as appropriate to detect and identify different faults at local orglobal level in nuclear power plants. The effectiveness of hybrid intelligence approach in improving thefault diagnosis efficiency in nuclear power plants was verified through simulation experiments.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

A nuclear reactor is a complex engineering system involvingseveral interdependent processes. As the nuclear power plantsgrowing in size andcomplexity, it became increasingly important forthe operators of these nuclear power plants to have a very goodunderstanding of these interdependent processes and their trouble-shooting processes. In order to help the operators to quickly identifythe faults and take necessary actions to ensure the safety of theirplants, many operator support systems or/and diagnosis systems(Chug et al., 1994; Seock, 1994; Ozaki et al., 1997; Varde et al., 1998)were developed using different diagnosis methodologies.

Much attention was always paid to the fault detection anddiagnosis (FDD) processes in the NPPs (Li and Upadhyaya, 2011; Liet al., 2012). The application of an effective fault diagnosis tech-nology, such as artificial neural network (ANN), fuzzy logic (FL),data fusion or hybrid intelligence approach could help the NPPoperators to identify the faults and take necessary actions to sus-pend the fault and alleviate the consequence of an accident. TheNPP fault diagnosis technologies could be classified into two cate-gories, namely, model-based and data-driven technologies. Therewere reports on both model-based and data-driven technologies in

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literature for fault detection and diagnosis at NPPs. For example,model-based condition monitoring was reported by Zhao andUpadhyaya (2006) and Gross et al. (1997). ANN and PrincipalComponent Analysis (PCA)-based tools (Upadhyaya et al., 2003; Luand Upadhyaya, 2005) are most popular data-driven tools. Model-based technologies were easy to understand, but their reliabilityand computational efficiency were not very good as the systemsbecame more complex. However, if the goal was to monitor theinputeoutput information from a collection of (appropriately cali-brated) sensors and the whole systemwas taken as a black box, thedata-driven technologies could still be considered reliable andefficient. Unless the acquired information was handled properly,the data-driven technologies might become computationallyintensive and the fault detection efficiencymight deteriorate due tothe degradation of sensors. Furthermore, the data-driven technol-ogies would require a large volume of training data.

The diversified applications of Data-drivenmodeling based ANNin NPP range from the fault diagnosis of a single component (Zhaoand Upadhyaya, 2006) to the fault diagnosis of the whole nuclearsteams supply system (NSSS) (Xin et al., 2010). Most of the work(Upadhyaya et al., 2003; Lu and Upadhyaya, 2005; Xin et al., 2010;Santosh et al., 2009) tried Multilayer ANN with back propagationtraining, because this type of ANNs had an excellent capacity ofapproximation and generalization. ANN, FL and data fusion hadbeen tried in recent years for FDD and structural damage detection,because of their capabilities in extracting and attaining precise,

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Nuclear power plant fault diagnosis

fault diagnosis ofReactor

fault diagnosis ofPressurizer

fault diagnosis ofSteam Generator

fault diagnosis ofPipeline

……

forruptureofcorefuelelement

forsealingleakage

from

pressurevessel

forU-tubeleakagefrom

steamgenerator

f orimproperopeningof

saf etyv alve

forleakagefrom

safetyvalve

forimproperclosingof

sprayval ve

forruptu reofcoolanthe atpi pe

forruptureofmainsteampipe

r up ture

……

forruptureofcoolantcoldpipe

……

Fig. 1. Breakdown of nuclear power plant fault diagnosis.

Y.-k. Liu et al. / Progress in Nuclear Energy 76 (2014) 122e136 123

reliable, consistent and intelligible information from imprecise,unreliable, inconsistent and uncertain data (Gao, 2004; Pedrycz,1997).

ANN technology itself was capable of doing auto-association,self-organization, self-learning and non-linear modeling, and so, itwas already utilized for FDD. ANN could provide high training pre-cision with generalization for the continuous function mapping ofthe underlying process models and applications for specific outputrules. FL technology functioned to model human knowledge inaccordancewith the understandable “if-then” rules andwas knownfor outstanding ambiguous data processing, but its self-learningcapability was poor (Liu et al., 2009; Rodriguez et al., 2008;Sawyer and Rao, 2000). FNN technology was developed by usingthe strong points of both ANN and FL to offset their weakness and itwas in essence the integration of the above mentioned two tech-nologies (Adeli and Jiang, 2006; Kong and Chen, 2004). Test resultsindicated that FNN could achieve a much better accuracy for FDD.

Data fusion was known as a tool to extract information fromdifferent sources and integrate them into a consistent, accurate andreliable data set. Its potential application in structural damagedetection and its ever-increasing appeal for researchers enabled itsapplication to the structural damage detection and FDD to improvethe reliability of damage detection and fault diagnosis (Du et al.,2012; Basir and Yuan, 2005).

Table 1Major symptoms of SGTR and MFLB.

Fault no. Symptoms of MFLB Symptoms of SGTR

1 The average temperature ofprimary loop was higher thanthe normal

The water level in thepressurizer was lowerthan the normal

2* The water level in the steamgenerator was lower than thenormal

The water level in thesteam generator washigher than the normal

3* The main steam pressure waslower than the normal

The main steam pressurewas higher than the normal

4 The main steam flow was lowerthan the normal

The water temperature atthe steam generator secondaryside was higher than the normal

5 The containment temperaturewas beyond the normal

The radiation dose at the steamgenerator secondary side wasbeyond the normal

*Some of the symptoms trend of MFLB and SGTR are the opposite such as the waterlevel and the main steam pressure.

The complexity of systems and the great variety of data made, itdifficult for a NPP to diagnose double or some multiple faults ofeach subsystem for necessary information. For some faults, it waseven very difficult for some operators to identify a particular faultfrom the right location in a timely manner. The system runningparameters and the indications of an alarm system could not meanmuch about the types and/or locations of faults. Some types offaults did not have their obvious symptoms, sometimes, and therewere no parallel relations between faults and their symptoms. So,the operators needed rich experience, deep knowledge of the sys-tems, strong sense of responsibility and good psychological qualityto analyze a large amount of characteristic parameters, and todetermine the extent and location of faults. In this case, it would bevery difficult to properly diagnose a complex system by relying on asingle fault diagnosis technology only.

Therefore, different fault diagnosis technologies were combinedas appropriate to the types of faults and/or the progress of diagnosisprocesses made in a distributed framework. This hybrid intelli-gence approach had now become a general trend of fault diagnosisin NPP (Ma, J., 2011).

The fault diagnosis process in a distributed framework could bebroken down into the different fault monitoring and diagnosistasks as shown in Fig. 1 (Yan et al., 2009).

Fig. 2. Water level in the steam generator.

Fig. 3. Water level in the steam generator with SGTR superposed by MFLB.

Y.-k. Liu et al. / Progress in Nuclear Energy 76 (2014) 122e136124

Each diagnosis task could be accomplished using appropriatediagnosis methods based on the proper understanding of the fault.The kind of arrangement brought the capability of a single faultdiagnosis method into full play, but it also got the coupling rela-tionship cut off. The same characteristic parameters of faults mighthave different effect even opposite, as shown in Table 1.

As shown in Table 1, the effect of the 2nd symptom of SGTR wasopposite to that of the 2nd symptom of MFLB, and the effect of the3rd symptom of SGTR was opposite to that of the 3rd symptom ofMFLB. If there were time sequences for both double and multiplefaults, themonitoring parameters would be very complicated in thefollowing three cases. In the first case, the water level in the steamgenerator would be as shown in Fig. 2, when a single fault occurred.In the second case, the water level in the steam generator would beas shown in Fig. 3, when double faults occurred in the 32 s from themoment when the simulation system had run normally for 10 s. Inthe third case, the water level in the steam generator would be asshown in Fig. 4 in 22 s, from the moment when the simulationsystem had run normally for 15 s.

As shown in Figs. 2 and 3, the water level in the SG rose whenSGTR occurred, the water level in the SG changed from up to downwhen SGTR was superposed by MFLB, after MFLB was inserted atthe 32 nd s, when the MFLB superposed with SGTR occurs, thewater flow of feedwater was greater than that of water flow fromthe ruptured U-shaped pipe. Thus, the water level in the SG was

Fig. 4. Water level in the steam generator with MFLB superposed by SGTR.

decreased (in Fig. 4). It can clearly be seen that, the characteristicparameters monitored was not the same in three fault modes. Inorder to achieve the mutual coupling between double and multiplefaults, both the fault information of equipment at the local level,and the operating status of the system at the global level wereinevitably needed.

In this paper, the hybrid intelligence approach was based onfuzzy neural network (FNN), and data fusion. At the local level FNNwas used to diagnose single faults, and at the global level datafusion was used to deal with more fault characteristics. FNN andSDG were used in a double or multiple systems. The diagnosticresults given by FNN, and the SDG were used to validate the doubleor multiple faults and to establish the fault propagation path. Thehybrid intelligence fault diagnosis approach was developed on thebasis of different fault diagnosis methods, in which each diagnosismethod worked independently, and brought into full play thecapability of each independent diagnosis method. The flow chart ofhybrid intelligence fault diagnosis approach is shown in Fig. 5.

The paper is organized as follows. Section 2 provides adescription of the distributed single fault diagnosis subsystem,including the distributed single fault diagnosis strategy in Section2.1; the fault diagnosis methods including FNN diagnosis algorithmin local level and data fusion diagnosis algorithm in global level arerespectively described in Section 2.2 and Section 2.3. Section 3presents the global diagnosis strategy of the double and multiplefaults. In the Section 4 the fault path validated based on SDG (inSection 4.1), information share of SDG and fault diagnosis system(in Section 4.2), and SDG modeling and validation diagnosticreasoning rules (in Section 4.3) are discussed. Section 5 describesthe test result of simulation experiment. Finally, Section 6 presentsthe concluding remarks for this paper.

2. Distributed single fault diagnosis subsystems

2.1. Distributed single fault diagnosis strategy

The distributed fault diagnosis strategy had now been proposed(Salahshoor et al., 2011; Cilliers, 2013). Generally, a specific faultdiagnosis task was assigned to each principal equipment of coolantsystem, such as reactor, SG, primary coolant pump, pressurizer, andetc., and each of them was equipped with a local fault diagnosticsubsystem. While the system was running, such characteristic pa-rameters as temperature, pressure, water flow, and etc. weregathered through different sensors, and then transferred to thereal-time database for storage and to the local fault diagnosissubsystem at the same time. FNN was in the local diagnostic sub-system for preliminary diagnosis. If the result of fault diagnosis wasreliable, the preliminary diagnosis would be continued and theresult of fault diagnosis would be directly output. If the outputresult of a local fault diagnosis subsystemwas not guide satisfactorythe global data fusion diagnosis subsystem should be turned on todiagnose the fault once again. After these two diagnosis steps, thediagnosis results should then be sent to the real-time database forstorage, and to the humanemachine interface (HMI) for display atthe same time. Logic diagram of distributed fault diagnosis strategyis shown in Fig. 6.

2.2. FNN fault diagnostic method

(1) Structure of FNN

The FNN in the local fault diagnosis subsystem was the combi-nation of the fuzzy theory with the neural network, which keptgathering, learning, association, recognition, self-adaption andfuzzy information processing in one and FNN is the double

Fig. 5. Flow chart of hybrid intelligence fault diagnosis approach.


simulation the structure of human brain and its thinking function,i.e. the “hardware” topological structure of the neural network ofhuman brain and the “software” function of the fuzzy informationprocessing system at the same time (Shi and Han, 2009). The to-pology structure of FNN in this paper is shown in Fig. 7.

The 1st Layer is the input layer, and the number of nodes “n”wasequal to the number of the monitored characteristic parameterswhen the equipment was running. The 2nd Layer functioned tofuzzily the output characteristic parameters of the 1st layer. Somefuzzy subsets were firstly defined for some input characteristicparameters, and then converted the characteristic parameters intothe corresponding membership of fuzzy subsets. In the presentstudy, we firstly define the three (small, normal, and big) fuzzy

data acquisitionand display

primary coolantpump diagnosis

module

pressurizerdiagnosis module

HMI

database

global faultdiagnosis module

local faultdiagnosis module

.

.

.

.

.

.

data monitoringmodule

Steam generatordiagnosis module

Fig. 6. Logic diagram of distributed fault diagnosis strategy.

subsets were firstly defined the input characteristic parameters.Each of the nodes represents a fuzzy subset, and so, the inputmonitoring characteristic parameters was calculated by the fuzzymembership function from “n” variables into “3n”variables in the2nd Layer, i.e., the FNN nodes in the 2nd layer was three times thatin the 1st layer. The 4th layer is the output layer in which nodenumber “m” represented the number of fault types, and each of thenodes represented one type of fault. The value of the output resultrepresented the reliability of each type of faults. The 3rd layer is themiddle hidden layer, which implemented the fuzzification values ofthe input variable to the fuzzy values of output variable mapping.The active function of this layer is Sigmoid function, and the numberof nodes “b” in the training block was determined in accordancewith the actual conditions. The empirical equation of the nodenumber in the middle hidden layer was given as shown below.

b ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi3nþm

pþ a (1)

where “n” was the number of input layer nodes, “m” was thenumber of output layer nodes, and “a” was an integer between 1and 10 to be determined in accordance with the actual conditionsin the FNN training process.

X1

X2

Xn

Y1

Y2

Ym

Input layern

Fuzzy layer3n

Hidden layerb

Output layerm

Fig. 7. Structural diagram of fuzzy neural network.

0.9

0.1

0.3

0.4

0.5

0.6

0.2

0.7

0.8 Small Normal Large

1

00ijXminijX ijL ijH maxijX

Fig. 8. Membership function of “x”.

(3) Fuzzy neural network learning and training

Table 2Training sample for FNN-1 network.

Input Output

Sample number a1 a2 a3 a5 a7 Out1

F1 15.1926 5.43073 10.5142 5.87144 1375.84 1N2 15.2221 5.46016 10.4779 5.85759 153.011 0


(2) Fuzzy neural network algorithm

If the operating status of the system could be monitored using“n” characteristic parameters, the ith sample of the set of moni-toring characteristic parameters could be represented byXi¼[xi1,xi2,...,xin,], i¼1,2,...,m, where “i” is the number of samples. TheGaussian function had good local approximation properties, Sig-moid function had good global approximation properties, and bothof them were differentiable. So, Gaussian function was selected asthe “normal” membership function (near the universe of discoursecenter), and Sigmoid function was selected as the “small” and “big”(in the universe of discourse boundary) membership functions(Chang, 2007). The following were the membership functions ofthree fuzzy subsets.

The “small” membership function of fuzzy subset

mLðxÞ ¼ 1� 11þ e�aðx�LÞ ; (2)

the “normal” membership function of fuzzy subset

mNðxÞ ¼ e�bðx�NÞ2; (3)

and the “big” membership function of fuzzy subset

mHðxÞ ¼ 11þ e�cðx�MÞ ; (4)

Fig. 9. Simulator of NPP.

where, a, b, c and L,M, Nwere constant parameters of membershipfunctions. When the system was normally running, each of themonitoring characteristic parameters had a normal range, and sothe input value of each parameter Xij had Xijmin the “minimum”

normal input value, Xij0 the “best” normal input value, and Xijmaxthe “maximum” normal input value.

With a divided margin of 1/20 kept, the boundary of input pa-rameters could be divided once again as shown below.

The normal value of the input center

Nij ¼ Xij0; (5)

the lower limit value of the normal input

Lij ¼ Xijmin þ�Xijmax � Xijmin

�20

; (6)

and the upper limit value of the normal input

Hij ¼ Xijmax ��Xijmax � Xijmin

�20

: (7)

where, constant parameters a, b, c and L,M, N could be obtained bysubstituting the points (Xijmin, 0.5) and (Lij， 0.45) into formula (2),points (Lij， 0.5) and (Xij0， 1) into formula (3), and points (Hij，0.45) and (Xijmax， 0.5) into formula (4). As shown in Fig. 8, themembership functions conform to the actual distribution ofmonitored characteristic parameters. In addition, among themembership function values of the three fuzzy subsets only onevalue was greater than 0.5, which proved the selection of the abovefuzzy algorithm was reasonable.

As shown in Fig. 9, the simulator could simulate the real-timeoperating conditions of a nuclear power plant. When a faultoccurred, the operating status could be observed, and the nuclearpower operator could be trained through it.

Different fuzzy neural network were incorporated in the localdiagnosis module. For example, in the local FNN learning andtraining module, SGTR was taken as an example.

The local FNN-1 is responsible for the fault diagnosis of SGTR inthe left loop, such training parameters as the pressurizer pressurea1, water level of pressurizer a2, left loop water level of SG a3, leftloop pressure of SG a5 and left loop main steam radiation dose a7were the inputs to the FNN-1. As shown in Table 2, the parameterswith a probability of 1 and a probability of 0 would be used to trainnetwork 1.

The local FNN-2 was used for the fault diagnosis of SGTR in theright loop, such training parameters as pressurizer pressure a1,water level of pressurizer a2, right loop water level of SGa4, rightloop pressure of SG a6 and right loop main steam radiation dose a8were the inputs to the FNN-2. As shown in Table 3, the parameters

Table 3Training sample for FNN- 2 network.

Input Output

Sample number a1 a2 a4 a6 a8 Out2

F2 15.0457 5.31803 10.5356 5.88208 6257.21 1N2 15.2225 5.46006 10.4781 5.84549 151.527 0

Table 4Actual output of each training sample for FNN network.

Sample numbers Out1 Out2

F1 0.99321 /N1 6.8549E-03 /F2 / 0.98831N2 / 2.8682E-02

Diagnosticobject

Signalacquisition

Featureextraction

1X

Signalacquisition

Featureextraction

Signalacquisition

Featureextraction

2X

nX

Data levelLocalfuzzyneuralnetwork

diagnos issubsyst em

Feature level

Normalized

Decis ionlevel

Globalfu si ondiagnos is

results

Fig. 10. Structure of global fusion diagnosis subsystem.


with a probability of 1 and a probability of 0 would be used to trainnetwork 2.

The learning rate of each network is 0.6 during training, and theerror threshold is 0.0005 after training, node of each local FNNnetwork was identified as 5e15e11e1, 5e15e11e1, and the actualoutput of each local FNN network after training on the column inTable 4, and the output value meet the error threshold.

2.3. Data fusion diagnosis method

In the global level diagnosis subsystem, data fusion could bedefined to make full use of the information resources of multi-

Fig. 11. Working processes of double or

sensors in different time and space, use computer technology todo automatic analysis, synthesize, control and use the informationobserved by multi-sensors in time sequence, and obtain the con-sistency interpretation and description to complete the requireddecision and estimated task, then make the system perform betterthan each of its components (Cao, 2006).

The use of the data fusion theory to build the concrete structureframework of a fault diagnosis system has great subjectivity, andthe selection of fusion algorithm has a great influence on the sys-tem performance. D-S evidence theory is a kind of importantmethod to deal with uncertainty problems, and the well expressedcognitive concepts of unknown and uncertainty, the basic proba-bility assignment function, trust function and likelihood functionare the three basic points of D-S evidence theory (Chang and Yu,1990; Zhou et al., 2010).

As shown in Fig. 10, in the study, we take the all types of faultswere taken as the recognition framework of D-S evidence theory,and the output values of a local FNN diagnosis subsystem wastaken as the basic probability assignment of D-S evidence theory,then the evidence fusion result of the D-S evidence theory wascalculated using formula (8) (Worden et al., 2009; Oukhellou et al.,2010).

mðOÞ ¼

8>>>>>><>>>>>>:

Pi;j

OiXOj ¼O

m1ðOiÞm2�Oj�

1� KcO3U Os4

0 O ¼ 4

(8)

where, m1(Oi) and m2(Oj) are basic probability assignments of eachfault, and K is an inconsistent factor for m1(Oi) and m2(Oj), i.e., thedegree of conflicting evidence, the greater K the greater conflictbetween the evidence. If K ¼ 1, the failure of classic D-S evidentialtheory could be expressed as

multiple faults diagnosis strategy.

water levelin SG

feedwaterflow

Fig. 12. Cause-effect (CE) chart of feedwater flow and water level in SG.


K ¼Xi;j

OiXOj ¼f

m1ðOiÞm2�Oj�< 1: (9)

3. Double or multiple faults diagnosis strategy

Single fault diagnosis subsystem under distributed frameworkwas not applicable to double or multiple faults, so a specific strat-egy should be designed for double or multiple faults diagnosis. Thebasic idea of a double or multiple faults diagnosis strategy was thatfor different fault should be used different double or multiple faultsdiagnosis subsystems. As shown in Fig. 11, FNN was used to di-agnose different double or multiple faults, and it was trained usingdifferent samples.

At the beginning of the system running, only the single faultdiagnosis subsystem was working. Once fault “A” occurred, analarm was triggered, and the alarm did not work until it wasremoved, and fault “A” is thought to always occur. After the singlefault diagnosis subsystem triggered the alarm, if the other faultsstill occurred as well, the fault diagnosis mode is necessarily doubleor multiple faults. So, the single fault diagnosis subsystem underthe distributed framework is no longer available, the single faultdiagnostic subsystem would be disabled at this time, and thedouble or multiple faults diagnosis subsystem would be enabled.

The double or multiple faults diagnostic subsystem is called bythe module controller, which determines to activate the next faultsdiagnostic subsystem according to the alarm.

(1) If the alarm belongs to the single fault diagnosis subsystemunder distributed framework, the alarm is named “name1”,and then the beginning of “name1” double faults would beactivate the double fault diagnosis subsystem.

(2) If the alarm belongs to the double fault diagnosis subsystemunder distributed framework, the alarm is named“name1&name2”, and “name1&name2” is activated, themultiple faults would be activated in the multiple faultsdiagnosis subsystem.

For example, the alarm in the single fault diagnosis subsystemunder distributed framework sends Fault “A” to the module

A B C D E F G H

+ ++

++

++++++++

+

++

+++

+++ + + + +

+

+

++

+++

--

--

-------

- - -

--

- - -

- -- -

-

- -----

--

--

---

Fig. 13. Search path before information sharing.

controller. modules “A&B”, “A&C”, “A&D”, .,“A&n” in the doublefault diagnosis subsystem would be used to determine whethersuperposition faults occurred.

While the double fault diagnosis subsystem was running, theworking process is analogous to that of the single fault diagnosissubsystem under the distributed framework. Once Fault “B” occurs,an alarmwould be triggered, and the alarmwould be working untilit was removed. the double or multiple faults diagnostic rules are asfollows, enable the “n” fault diagnosis subsystemdtriggeralarmddisable the “n” multiple faults diagnosis sub-systemdenable the “nþ1” fault diagnosis subsystem, the reasoningprocess can continuously go on one by one until all the double andmultiple faults diagnosis subsystems are involved. During thestudy, when a new fault occurred, only the double faults and somemultiple faults were tested to validate the effectiveness of a faultdiagnosis system.

4. Validation subsystem based on SDG

Signed directed graph (SDG) was used to build the systemmodel, used to analyze the relationship between systems, equip-ment, monitoring parameters, and combination of double or mul-tiple faults diagnosis subsystem at global level, reasoning andverification evidence was provided for the validation of double ormultiple faults diagnosis.

4.1. SDG method

If Sv ¼ {þ, 0,�} (j（vi）˛{þ, 0,�}, where “þ” and “-” representsincrement function and decrease function respectively. In G3

SDG, j(vi, vj) ¼þ represents viþ-d>vjþ-, j (vi, vj) ¼ - represents viþ-d>vj�þ

(Kramer and Palowitch, 1987; Chang, and Yu, 1990; Vedam, andVenkatasubramanian, 1997; Claire, and Paul, 1999). Usually solidline represents increment function (positive role), and dotted linerepresents decrease function (negative role). Cause-effect (CE)chart of feedwater flow and water level in SG is shown in Fig. 12.

4.2. Information share of SDG and fault diagnosis system

In the present study, the SDG validation subsystem shared in-formation with the double or multiple faults diagnosis subsystemat the global level. Therefore, the SDG validation subsystem withthe double or multiple faults diagnosis subsystem work together,and the reasoning workload of the SDG validation subsystem isreduced. If there are 8 faults (“A”, “B”, “C”, “D”, “E”, “F”, “G”, “H”), thediagnostic result of the fault diagnosis systemwas that “A”, “C”, “D”,“G”, and “F” did not occur. Before and after the information sharing,the SDG search path is shown in Fig. 13 and Fig. 14.

A B C D E F G H

--

---

----

- ----+

+

++

++

+++

+

+++

+

--

-

Fig. 14. Search path after information sharing.

Fig. 15. Simplified SDG model for primary loop of PWR.


Fig. 16. Example of bidirectional reasoning rules.

Step 1: The alarm nodes are arranged in accordancewith their path sites and thepriority of the nodes increases as the distance to the end of the path site de-creases, and the nodes were then stored in the set of alarm nodes.

Table 6Schedule of changes in characteristic parameters.

Characteristicparameter

Water levelin SG

SG pressure Radiation doseon secondaryside

Time(s) 35 35 38Mode of change 0.25–>0.75 0.25–>0.75 0.5–>0.75


In the Figs. 13 and 14, the round icons represent state nodes, andthe different colors round icons represent the different state, that is,red round icon represents high threshold, symbol is “þ”, blue roundicon represents low threshold, symbol is “-”, green round iconrepresents normal, and the square icon represents the fault nodethat to be diagnosed. As shown in Figs. 13 and 14, after informationsharing, the search path and the possibility of information explo-sion of SDG could be reduced, and the reasoning efficiency of SDGwas improved.

4.3. SDG modeling and validation diagnostic reasoning rules

In the SDG validation subsystem, SDG could usually be used tobuild a system model, the abnormal changes of the monitoringcharacteristic parameters could be determined through the anal-ysis of all the factors, and combined with the diagnosis results ofthe double or multiple faults diagnosis subsystem to see whetherdouble or multiple faults occurred or not. The SDG model for theprimary loop of the NPP (based on NPP of Daya bay) is shown inFig. 15.

When SDG was used to do the fault reasoning, it can be carriedout both forward and backward reasoning, that is, the reasoningcould go from the root node of fault to the child nodes of fault, orreasoning from the child nodes of fault to the root node of fault. If

Table 5Diagnosis results of SGTR.

Time (s) Diagnosis results of SGTR

10 0.0203111 0.0203112 0.0203113 0.0203114 0.0203115 0.0203116 0.0203317 0.0203518 0.0203819 0.0204020 0.0213221 0.0630222 0.2049323 0.2049424 0.5958225 0.5958326 0.5958327 0.6695128 0.96333

the root node was in the active (valid) status, the forward reasoningis a way by checking the state of the child nodes connected to theroot node, and to identify whether the state meets the re-quirements of fault mode for the root node. The backwardreasoning is another way of reasoning fault source from the faultsymptoms, by which the reasoning from the child nodes to the rootnode could be used to obtain the information of child nodes fromfault symptoms to check whether the child nodes connected to theroot node were active or not. If so, the fault was sure to occur.

The ultimate goal was to find two types of evidence nodes byusing SDG method to create a rule for reasoning and verification:

The 1st type of evidence node. If the node state of the faultfeatures is abnormal and merely affected by the only compatiblepath, then the node of fault symptoms belongs to the 1st type ofevidence node.

The 2nd type of evidence node. If the node state of the faultsymptoms and its adjacent non-root cause nodes are all normal, orincompatible with the path between its adjacent non-root causenodes, then the node of fault symptoms belongs to the 2nd type ofevidence node.

If there was a 1st type of evidence node, the corresponding faultwas sure to occur, on the contrary, if there was a 2nd type of evi-dence node, the corresponding fault was not sure to occur. Througheither forward or backward reasoning, and searching for two typesof evidence nodes, excess alarms were removed to when double ormultiple faults occurred.

As shown in Fig. 16, Fault “A” reasoning was taken as anexample, and other fault reasoning processes were not considered.The bidirectional reasoning rules are as follows:

Results: The 1st priority nodes are 1, 2, 3, and 4, and the 2ndpriority nodes are 5, 6, and 7.

Step 2: The current abnormal node was selected in accordancewith the priority of the abnormal state node, and then goes toStep 3; if all abnormal nodes had been checked, then go to Step 5.

Results: Node “1”was selected as the current node.

Step 3: Starting from the current abnormal node, backwardreasoning went through the superior nodes. If the adjacentnode’s state was abnormal, the detection method of adjacentnodes was combined to determine whether the path iscompatible. If yes, the node should be marked with “checked”,and the adjacent node was marked with “candidate node”, withthe highest priority, and then go back to Step 2; If no, the nodeshould be marked with ”checked”, and go back to Step 2. If thecurrent node was at the bottom of a compatible path, it shouldbe marked as the cause root node, then go back to Step 2.


Characteristic parameter SG pressure Feedwater flow Main steam flow

Time(s) 31 32 36Mode of change 0.75–>0.25 0.75–>0.25 0.5–>0.25


Characteristic parameter Water level in pit SG pressure Water level in SG Radiation dose in containment Water lever in pressurizer Pressurizer pressure

Time(s) 33 34 34 36 40 42Mode of change 0.75–>0.25 0.75–>0.5 0.5–>0.25 0.5–>0.75 0.75–>0.5 0.5–>0.25

Fig. 17. Data access interface.


Results: Node “7”was selected as the candidate node, go back toStep 2, and node “A” was selected as the cause root node.

Step 4: Extract themarked cause root nodewas extracted in Step3. If the linked paths were compatible, the node was a faultynode; if no, the node was marked as the cause node to bechecked, and then it was stored in the set of nodes. If no node to

Fig. 18. SGTR diagn

be checked in the set of nodes, go to the Step 6, otherwise, go tothe Step 5.

Result: “A” was marked as the cause node to be checked.

Step 5: The forward reasoning started from the cause node to bechecked, and traversed the bottom node of the other linked

ostic interface.

Fig. 19. Double or Multiple faults diagnosis interface.


branch path. If there was a bottom node, which was abnormaland not connected with another compatible path, it could bemarked as a 1st type of evidence node, and then go to the Step 4;If there was a bottom node, which was normal, and the othernodes were connected normally, it could be marked as a 2ndtype evidence node, and then go to Step 4. In other cases, go toStep 4 too.

Result: Node “1” was marked as a 1st type evidence node.

Step6: there was a 1st type of evidence node exists, the causenode could be checked marked as a faulty node, end; if no, end.

Result: Fault “A” occurred.

5. Simulation test

The main faults tested through simulation were SGTR, MFLB,MSLB and LOCA. Among them, SGTR is the abbreviation for steam

Fig. 20. Diagnosis reasoning

generator tube ruptures, MFLB is the abbreviation for the mainfeedwater line breaks, MSLB is the abbreviation for the main steamline break, and LOCA is the abbreviation for loss of coolant accident.The “change mode” values in the changing schedule of the faultcharacteristic parameters (in Table 1e8) for experimental test are asfollows: “0.25” indicates that the parameter is below the thresholdor has a decrease trend; “0.5” indicates that the parameter isnormal; “0.75” indicates that the parameter exceeds the thresholdor has an increase trend; “0” indicates that no fault occurred, and“1” indicates that a fault occurred.

SGTR is the loss of coolant accident (LOCA) with a small break.Now, SGTR could be taken as an example to describe the test pro-cess in detail. The working condition of the experiment was asteady-state operation, the SGTR was inserted 10 s later, and nooperator intervened, and the break size of the SGTR insertedwas 5%of the pipe cross-sectional area. Data access interface is shown inFig. 17.

As the fault symptoms appeared, the single diagnostic subsys-tem gradually identified the type of fault. The system sent an alarm

of SGTR based on SDG.

Fig. 21. Double faults diagnosis process of LOCA superimposed by SGTR.


Characteristic parameter Water level in SG Main steam flow

Times(s) 21 26Mode of change 0.75–>0.25 0.75–>0.25


at the 28th second when the fault credibility exceeded 75%. Thefault alarm was locked, and the fault credibility did not changefurther. As shown in Fig. 18, at this time, the user could click thegreen bar column which corresponded to the fault of SGTR. In the

Fig. 22. (a). Double faults diagnosis process of LOCA superimposed by MFLB. (b).Double faults diagnosis process of MFLB superimposed by LOCA.

right historical diagnosis window, the dynamic historical diagnosisrecords of SGTR would be drawn in the form of a line chart todisplay the fault diagnosis process of SGTR. The diagnosis resultsare shown in Table 5.

When a single fault occurred, the local fault diagnosis subsystemwould be out of service, and the double or multiple faults diagnosissubsystem would be chosen by the module controller.

Example (1-a). As shown in Fig. 19, the SGTR was inserted in theleft loop of NPP at the 10th second, the LOCA in the left loop cold leg(12% break size) would be inserted 20 s later. The system diagnosedthe occurrence of SGTR and LOCA at the 35th second, and sent thediagnostic credibility feedback to the human-machine interface(HMI).

After the double or multiple faults diagnosis subsystemaccomplished the identification of double faults, the SDG validationsubsystem was selected to test the fault propagation path, and theoperator could also use this subsystem for further analysis. Asshown in Fig. 20.

SGTR and LOCAwere both confirmed by searching the existenceof 1st type evidence nodes. For example, it could be inferredthrough the radiation dose on the secondary side of SG, that SGTRactually occurred, and there was no MSLB through the water levelin the pit and the radiation dose in the containment, it could beinferred that LOCA actually occurred. For the MSLB, there was acompatible path, such asmain steam line break/water level dropsin SG. However, the node of the water level drop in SG was not the1st type of evidence node. The LOCA had the same influence on thenode, and other paths were not compatible, and so, no conclusioncould be drawn onMSLB. The final SDG reasoning results were bothSGTR and LOCA faults.

Example (1-b). While the system was operating steadily, theLOCA was inserted with a break size of 14cm2 at the 10th second,and the SGTR was inserted with a break size was 5% of the pipecross-sectional area at the 25th second. After the SGTR was inser-ted, the main contribution of the SGTR fault credibility in thissuperimposed mode were water level in SG, SG pressure, and ra-diation dose on the secondary side of SG. Schedule of changes incharacteristic parameters is shown in Table 6, and the diagnosisprocess is shown in Fig. 21.

Example (2-a). While the system was operating steadily, theLOCA was inserted with a break size of 17cm2 at the 10th second,and theMFLBwas insertedwith a break size of 5% of the pipe cross-sectional area at the 25th second. Only the LOCA occurred at theearlier stage, and the fault credibility of MFLB was less than 10%.After the MFLB was inserted, the main contributions of the MFLB’sfault credibility in this superimposed mode were SG pressure,feedwater flow, and main steam flow. Schedule of changes incharacteristic parameters is shown in Table 7, and the diagnosisprocess is shown in Fig. 22(a).

Example (2-b). While the system was operating steadily, theMFLB was inserted with a break size of 5% of the pipe cross-sectional area at the 10th second, and the LOCA was insertedwith a break size of 19cm2 at the 30th second. After the LOCA wasinserted, the main contributions of the LOCA’s fault credibility inthis superimposed mode were water level in pit, pressurizer pres-sure, water level in pressurizer, and radiation dose in containment.

Fig. 24. (a). Double faults diagnosis process of MSLB superimposed by SGTR. (b).Double faults diagnosis process of SGTR superimposed by MSLB.

Fig. 23. (a). Double faults diagnosis process of SGTR superimposed by MFLB. (b).Double faults diagnosis process of MFLB superimposed by SGTR.


Schedule of changes in characteristic parameters is shown inTable 8, and the diagnosis process is shown in Fig. 22(b).

Example (3-a). While the systemwas operating steadily, the SGTRwas inserted with a break size of 30% of the pipe cross-sectionalarea at the 10th second, and the MFLB was inserted with a breaksize was 1% of the pipe cross-sectional area at the 20th second.After the MFLB was inserted, the main contributions of the MFLB’sfault credibility in this superimposed mode were water level in SGand main steam flow. Schedule of changes in characteristic pa-rameters is shown in Table 9, and the diagnosis process is shown inFig. 23 (a).

Example (3-b). While the system was operating steadily, theMFLB was inserted with a break size of 5% of the pipe cross-sectional area at the 10th second, and the SGTR was inserted with



Radiation doseon the secondaryside

Pressurizerpressure

Water levelin pressurizer

Pressurizerpressure

Time(s) 34 45 46 51Change mode 0.5–>0.75 0.75–>0.5 0.75–>0.5 0.5–>0.25

a break size of 18% of the pipe cross-sectional area at the 30thsecond. After the SGTR was inserted, the main contributions of theSGTR’s fault credibility in this superimposed mode were Pressur-izer pressure, radiation dose on the secondary side of SG, and waterlevel in pressurizer. Schedule of changes in characteristic parame-ters is shown in Table 10, and the diagnosis process is shown inFig. 23 (b).

Example (4-a). While the system was operating steadily, theMSLB was inserted with a break size of 5% of the pipe cross-sectional area at the 20th second, and the SGTR was insertedwith a break size of 5% of the pipe cross-sectional area at the 35thsecond. At the beginning of SGTR, there were some disturbances inthe data, which caused the fluctuation of output diagnostic results,the main contribution characteristic parameter of the SGTR’s faultcredibility in this superimposed mode was the radiation dose onthe secondary side of SG only, which occurred at 44th second. Thediagnosis process is shown in Fig. 24(a).



Water level in SG SG pressure Main steam flow

Times 21 26 25Mode of change 0.75–>0.25 0.75–>0.25 0.5–>0.75

Fig. 25. Fault path reasoning interface of SGTR superimposed by MSLB based on SDG.


Example (4-b). While the system was operating steadily, theSGTR was inserted with a break size of 4% of the pipe cross-sectional area at the 10th second, and the MSLB was insertedwith a break size of 6% of the pipe cross-sectional area at the 40thsecond. After the MSLB was inserted, the main contributions of theMSLB’s fault credibility in this superimposed mode were waterlevel in SG, SG pressure, and main steam flow. Schedule of changesin characteristic parameters is shown in Table 11, and the diagnosisprocess is shown in Fig. 24 (b).

The fault credibility of the LOCA was 36.72% in this super-imposed mode, and the SDG validated subsystem could be acti-vated at this time for the fault path reasoning, as shown in Fig. 25.

Because of the 2nd type of evidence node, the water level in thepit did not rise, which indicated no LOCA occurred.

6. Conclusions

There are many methods available for FDD, but they all havepros and cons. In order to satisfy the stringent safety requirementsfor NPPs, it is of great necessity to combine these as appropriate todeal with double or multiple faults at a local or global level.Therefore, a hybrid intelligence approach was proposed for thefault diagnoses in nuclear power plants.

(1) For the tight coupling between double or multiple faults,alarm information was effective all the time. The fault diag-nostic strategy of double or multiple faults diagnosis sub-system was activated and classified, and so, double ormultiple faults could be classified into different modes toenhance the direction of diagnosis process.

(2) The SDG validated subsystem could be used to verify the faultpropagation path and the fault result could be obtainedmoreintuitively and credibly through the fault path reasoning.

(3) Different diagnostic methods were used in the hybrid intel-ligence approach so that their advantages could be broughtinto fully play and better diagnostic results could be even-tually achieved.

In conclusion, the proposed approach has been used to achievethe initial progress in the simulator, and the faults are just the faultsinserted in the nuclear power simulator. So, the diagnosis knowl-edge is not complete. Our future work is of course to further studythe fault diagnosis methods under the actual operating conditionsin the NPPs.

Acknowledgment

This work is funded by the National Natural Science Foundationof China (51379046) and Heilongjiang Postdoctoral Science-research Foundation of China (LBH-Q12119).

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Improvement of fault diagnosis efficiency in nuclear power plants using hybrid intelligence approach