Abrupt event monitoring for water environment system based on kpca & svm


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Abrupt event monitoring for water environment system based on kpca & svm

  1. 1. 980 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 61, NO. 4, APRIL 2012Abrupt Event Monitoring for Water EnvironmentSystem Based on KPCA and SVMJianjun Ni, Member, IEEE, Chuanbiao Zhang, Li Ren, and Simon X. Yang, Senior Member, IEEEAbstract—The abrupt event monitoring is a challenging andcritical issue in water environment systems. There are two maindifferent abrupt events in the monitoring system, namely, theemergency water pollution accident and the abrupt sensor fault.The two different abrupt events have similar data characteristics,and few methods can be used to recognize the events. In this paper,a novel abrupt event monitoring approach based on kernel prin-cipal component analysis (KPCA) and support vector machinesis proposed, which is combined with the physical redundancymethod. The trust mechanism is introduced into the proposedapproach to reduce the interference of external noise and improvethe performance of quick response for the abrupt events. A sparedata area is set up to store the data for the KPCA modeling. Thedata in the spare data area are updated continuously, and theKPCA model is updated subsequently to improve the adaptivity ofthe KPCA model for the abrupt event monitoring. The experimen-tal results show that the proposed approach is capable of detectingand recognizing the two different abrupt events efficiently.Index Terms—Abrupt event, kernel principal component (PC)analysis (KPCA), support vector machines (SVMs), water environ-ment system.I. INTRODUCTIONTHE FREQUENT occurrences of water pollution accidentshave attracted more and more attention in the wholehuman society, so it is very important to monitor the waterenvironment automatically by sensor systems [1], [2]. Becauseof the complexity of water environment, there are two maindifferent abrupt events in the water environment sensor system,namely, the emergency water pollution accident and the abruptsensor fault. The emergency water pollution is mostly causedby discharging the polluted water into the water environmentdirectly without any treatments. However, the abrupt sensorfault is one type of sensor fault, which is mostly caused by mon-Manuscript received May 17, 2011; revised August 27, 2011; acceptedSeptember 30, 2011. Date of publication November 22, 2011; date of currentversion March 9, 2012. This work was supported in part by the National NaturalScience Foundation of China under Grant 61074056, by the Open Fund ofJiangsu Key Laboratory of Power Transmission and Distribution EquipmentTechnology under Grant 2010JSSPD02, by the Hohai University InnovationFoundation under Grant XZX/09B002-02, and by the Fundamental ResearchFunds for the Central Universities under Grant 2011B04614. The AssociateEditor coordinating the review process for this paper was Dr. V. R. Singh.J. Ni and C. Zhang are with the College of Computer and Informa-tion, Hohai University, Changzhou 213022, China (e-mail: nijj@hhuc.edu.cn;zhangcbhhuc@163.com).L. Ren is with the College of Hydrology and Water Resources, HohaiUniversity, Nanjing 210098, China (e-mail: renli@hhu.edu.cn).S. X. Yang is with the Advanced Robotics and Intelligent Systems Labo-ratory, School of Engineering, University of Guelph, Guelph, ON N1G 2W1,Canada (e-mail: syang@uoguelph.ca).Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.Digital Object Identifier 10.1109/TIM.2011.2173000itoring objects (such as the heavy corrosive pollutants in thepolluted water) or the design flaws. If it is an emergency waterpollution accident, it should be detected and dealt with timely.Otherwise, it will lead to serious consequences, such as anincrease of fish death, shortage of drinking water, and economiclosses. If it is an abrupt sensor fault, the fault sensor should bedetected and replaced timely to keep the sensor system workingnormally. The treatments of these events are different, so how tomonitor the abrupt event timely and accurately is a challengingand critical issue in water environment sensor systems.Various methods have been proposed to deal with the sensorfault problem [3], [4]. The sensor faults can be classified intodrift faults and abrupt faults. Because the abrupt faults maycause much more serious results than drift faults, researchhas focused on the abrupt sensor fault detection and isolationrecently. For example, Samara et al. [5] proposed a statisticalmethod for sensor abrupt faults. Oosterom et al. [6] devel-oped a sensor management system based on soft computingtechniques, and the abrupt fault of sensor was analyzed byusing the majority voting concept. Zhang and Yan [7] proposeda wavelet-based approach to the abrupt fault detection anddiagnosis of sensors.The problem of monitoring water pollution accidents isa focus recently. Reddy et al. [8] proposed a mathematicalmodel to continuously monitor the impurities that are presentin water. Zhao et al. [9] proposed a novel optical fiber sensorfor remote monitoring of salinity in water. Zhu et al. [10]discussed some roles for emergency pollution accident in drink-ing water sources, including monitoring parameters, methods,and procedures. Kunkel et al. [11] used the interdisciplinarymodel network REGFLUD to predict the actual mean nitrateconcentration in percolation water at the scale of the Weser riverbasin (Germany).There is much research on monitoring the abrupt sensor faultor the emergency water pollution accident, but few consid-ered the two problems together. The change characteristics ofthe measured data during the two different abrupt events aresimilar, so conventional methods cannot recognize the abruptevent efficiently. There are two main tasks in the abrupt eventmonitoring. One task is that the abnormality in the sensorsystem should be detected quickly and accurately. The otherone is that the type of abrupt events should be recognizedcorrectly as soon as the abnormality is detected. Although thosemethods introduced earlier could deal with some problems infault diagnoses, few of them could be used directly for theabrupt event monitoring of water environment systems. Toaccomplish the abrupt event monitoring task efficiently, a noveladaptive approach based on kernel principal component (PC)0018-9456/$26.00 © 2011 IEEE
  2. 2. NI et al.: ABRUPT EVENT MONITORING FOR WATER ENVIRONMENT SYSTEM BASED ON KPCA AND SVM 981analysis (KPCA) [12] and support vector machine (SVM) [13]methods is proposed, which is combined with the physicalredundancy method [14], [15]. In the proposed approach, theKPCA method is used to detect the abrupt event, and the SVMmethod is used to recognize the abrupt event. A spare data areafor KPCA modeling is set up to improve the adaptivity of theKPCA method. Furthermore, a trust mechanism is introducedinto the proposed approach to reduce the false diagnosis andimprove the performance of quick response for the diagnosis.The experimental results show that the proposed approach iscapable of detecting and recognizing the abrupt event efficientlyin water environment sensor systems.The main contributions of this paper are as follows. First,the problem of abrupt event monitoring in water environmentsystems is presented, where the abrupt sensor fault and theemergency water pollution accident are considered together.Second, a novel approach based on KPCA and SVM is pro-posed. The adaptivity of the KPCA method is improved, andjust one additional sensor is needed in the proposed approach.Third, a set of experiments is conducted, and the results showthat the proposed approach can deal with the abrupt event mon-itoring problem efficiently. The proposed approach has severaladvantages, such as dealing with noise and recognizing whichparameter changes abruptly at the water pollution accident.Last, the proposed approach is proved to be more suitablethan general methods for the abrupt event monitoring by thecomparison experiment.This paper is organized as follows. Section II presents theproposed approach. To illustrate the effectiveness of the pro-posed approach, the experiments for various situations are givenin Section III, and a comparison study is shown in Section IV.Finally, the conclusion is given in Section V.II. PROPOSED KPCA-AND-SVM-BASED METHODIn this paper, the abrupt event monitoring problem in waterenvironment sensor systems is studied. The monitoring of waterenvironment is a very difficult task; two abrupt events happenfrequently in the sensor system. One abrupt event comes fromthe sensors, and the other comes from the water environment.The data characteristics of these two conditions are similar, sothe abrupt event cannot be recognized accurately by generalmethods in the sensor fault diagnosis or water environmentmonitoring fields. A novel approach based on physical andanalytical redundancy is proposed for abrupt event detectionand recognition in this paper. In the proposed approach, just oneadditional sensor is used, and a trust mechanism is introduced toimprove the diagnosis accuracy. It is different from the generalphysical redundancy method that needs at least two additionalsensors [16]. There are some limitations of the conventionalKPCA method, such as the computational problem and theadaptivity of the diagnosis model [17]–[19]. In this paper, anadaptive KPCA method is used for the abrupt event detection,and a spare data area (K-SDA) is set up to improve the adap-tivity of the KPCA model. The online data are input into theKPCA model to detect the abnormity. If some abnormities aredetected in the new data, then the SVM method is used toFig. 1. Flow diagram of the proposed approach.recognize these abnormities. The flow diagram of the proposedapproach is shown in Fig. 1.The proposed approach can recognize the abrupt event cor-rectly and quickly by combining the physical and analyticalredundancy methods. The proposed approach is introduced indetail as follows.A. Proposed KPCA and SVM MethodThe KPCA method is the extension of PC analysis in thenonlinear area [12], which is a novel nonlinear multivariateanalytical redundancy method. The input space is mapped tothe feature space F via nonlinear mapping, and the PC analysisis implemented in the feature space. The KPCA method hasexhibited superior performance compared to linear PC analysismethod in processing nonlinear systems [20], [21]. The detailintroduction of the basic KPCA can be viewed in [12], [20],and [22]. In this paper, the KPCA kernel function is defined asa radial basis function.k(xi, xj) = exp− xi − xj2σ2(1)where xi, xj ∈ Rm(i, j = 1, 2, . . . , n) are the ith and jthtraining data for KPCA (m is the dimension of the training data,and n is the number of training data), σ is the parameter of theKPCA kernel function, and k(xi, xj) is the calculation of theinner product of two vectors in the feature space F.In the KPCA based method for fault diagnoses, bothHotelling’s T2statistic and the squared prediction error (SPE)statistic can be used [21]. To simplify the abrupt event moni-toring task and avoid confusion, only the SPE statistic is usedin this paper. The SPE statistic monitors the change of data inresidual space. The SPE is defined asSPE = Φ(x) − ˆΦp(x)2= ˆΦn(x) − ˆΦp(x)2=nj=1t2j −pi=1t2i (2)where Φ(·) is a nonlinear function that maps the input vectorsxk ∈ Rmto the feature space F, and nk=1 Φ(xk) = 0; ti is theith KPCA-transformed feature; and p is the number of PCs. Theconfidence limit for SPE is obtained from the χ2distributionSPEη ∼ gχ2h (3)
  3. 3. 982 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 61, NO. 4, APRIL 2012where η is the confidence degree of χ2distribution, g is aweighting parameter included to account for the magnitude ofSPE, and h accounts for the degrees of freedom. If a and bare the estimated mean and variance of SPE, g and h can beapproximated by g = b/2a and h = 2a2/b, respectively.The SVM method is a new machine learning algorithmbased on statistical learning theory [23], which can solve thesmall sample, nonlinearity, high dimension, and local minimumproblems. The SVM method has been used in classification,prediction, and fault diagnosis [13], [24], [25]. The detailedintroduction of the basic SVM can be viewed in [23] and [25].In this paper, the Gauss function is used as a kernel function ofSVM, i.e.,k (zi, zj) = exp −zi − zj22γ2(4)where zi, zj ∈ Rd(i, j = 1, 2, . . . , l) are the input data forSVM (d is the dimension of the input data, and l is the numberof the input data) and γ is the perception variable of SVM.To make the KPCA-and-SVM-based method more adaptivein monitoring the abrupt event of the water environment, theparameters σ, γ, and PC number p are calculated adaptivelywith the input data information. A spare data area is proposedfor the KPCA modeling. The principles to calculate theseparameters and the updating algorithm for the KPCA modelingare introduced as follows.1) The PC number. In this paper, an adaptive function is usedto determine the number of PCs based on the cumulativepercent variance theory [26]CPV (p) =pi=1λinj=1λj≥ α (5)where λi is the eigenvalue of the ith PC with λ1 ≥ λ2 ≥· · · ≥ λn and α is the threshold of PCs.2) The parameter σ of the KPCA kernel function. The pa-rameter σ of the radial basis kernel function is a very im-portant parameter of KPCA. In much of the literature, thisparameter is fixed and set by the experience [21], [22].In this paper, a new definition of σ is given out, whichis combining with the experience and the characteristicsof input data, and the abrupt event recognition based onthe SVM method is considered also. The parameter σ isdefined asσ2= β max(A, B) (6)where β is a constant and set as from five to ninealways and A and B are the parameters considered asthe characteristics of the input data for KPCA and SVM,respectively, which are calculated as follows:A = meanni=1nj=1xi − xj2 (7)B = meanli=1lj=1zi − zj2 (8)where mean(·) is a function to calculate the averagevalue.3) The parameter γ of the SVM kernel function. The para-meter γ is the perception variable; a new function is usedto calculate the value of γ adaptively in this paper, whichis defined asγ2=12pl2li=1lj=1zi − zj2(9)where p is the dimension of the training data for SVM,namely, the number of PCs.4) The updating algorithm of the KPCA model. In conven-tional KPCA methods, the model is fixed after it is setup, which has some limitations in the water environmentsensor system. The reason is that the relationship betweenthe water quality and the external environment is ambigu-ous [27], [28]. So, it would cause some false diagnosesby the fixed KPCA model. In the proposed approach,the new data collected by the sensor system are detectedonline by the KPCA model. When some abnormities aredetected in these new data, they will be recognized bythe SVM model. If the final diagnosis result is not asensor fault, these data will be inserted into K-SDA toupdate the historical data. The KPCA model normallyis updated periodically, and then, the SVM model ischanged according to the updated information. However,these models are updated immediately when the new dataare diagnosed as an emergency water pollution accident.To save the computational time and solve the overflowproblem, a maximum storage space of K-SDA is setpreviously in the proposed approach (denoted by Mϕ).B. Trust Mechanism in the Proposed ApproachTo increase the diagnosis accuracy of the proposed approachand use as few additional sensors as possible, a trust mechanismis introduced into the proposed approach. Before the introduc-tion of the trust mechanism, it is necessary to define some flags.One flag is denoted by f1(xi) to indicate whether the inputdata xi are abnormal. Another flag is denoted by f2(sj) forthe sensor sj, to indicate whether its measured parameter ofthe input data is abnormal. The flags f1(xi) and f2(sj) aredetermined by the KPCA and the SVM methods, respectivelyf1(xi) =0, If it is normal data1, Otherwise(10)f2(sj) =1, If its measured parameter is abnormal0, Otherwise.(11)Each sensor is assigned a trust value, which indicates the trustdegree of the measured parameter by this sensor. The trust valueof the jth sensor at the ith step is denoted by Tj(i). If there is
  4. 4. NI et al.: ABRUPT EVENT MONITORING FOR WATER ENVIRONMENT SYSTEM BASED ON KPCA AND SVM 983some abnormal information in the new data that detected bythe SVM model, the trust value of this sensor is decreased.Otherwise, the trust value of this sensor will be increased. Thecalculating rule of the trust value for the sensors is defined asTj(i)=Tj(i−1)+e, If the sensor data are normalTj(i−1)−f, Otherwise(12)where e and f are constants.The abnormal information is not diagnosed as an abruptevent immediately to reduce the false diagnosis, which may becaused by noise. Until the trust value of a sensor is lower thanthe trust threshold (defined as Th), the occurrence of an abruptevent is confirmed at this time. Considering the complexity ofwater environment, an additional sensor is used to monitor thesame parameter. If an abnormity is detected on both of the twosensors for the same parameter, it can be confirmed that theemergency water pollution accident happens. Otherwise, it isan abrupt sensor fault. The basic idea of the aforementioneddecision is that the fault probability of two sensors for thesame parameter at the same time will be very low. So, theadditional sensor must be placed at the same place of the sensor,to monitor the same water quality parameter. Then, the effect ofa sensor location in the decision can be removed. If it is a waterpollution accident, the trust values of the affected sensors areincreased to the maximum value at the next step to eliminatethe influence of environment, and the new data are inserted intoK-SDA to update the diagnosis model.The work flow of the proposed approach can be summarizedas follows.1) The initial samples should be obtained from the historicaldata collected from the online monitoring system andnormalized.2) The data with normal information in the initial samplesare used to set up the initial database of K-SDA, and theKPCA model is set up by the data in K-SDA.3) The SVM model is set up by the abnormal data in theinitial samples.4) The proposed approach is used for online monitoring.The new data are analyzed by the KPCA model to detectwhether there is any abnormal information.5) The abnormal data are isolated and recognized by theSVM model.6) Decide which abrupt event occurs by the decision rulesintroduced before, and update the diagnosis model.7) Go to step 4).The pseudocode of the proposed approach for monitoringabrupt events is shown in Fig. 2.Remark:1) The size of the historical data is related to the numberof sensors and the sampling rate. However, the size ofthe historical data will not affect the performance of theproposed approach because we can set up the KPCA andSVM models offline before the monitoring process.2) In the proposed approach, the initial samples are used toset up the KPCA and SVM models, which are a data setcollected from all the sensors of one sensor node in theFig. 2. Pseudocode of the proposed approach for monitoring abrupt events.water environment sensor system (see Fig. 4). The initialsamples contain both the normal and abnormal data.3) Because the parameters of water environment systems arein different units and dimensions, they are normalized toreduce the effects of parameters on the KPCA and SVMmodeling. The linear normalized function is used in thispaper, namely, ˜x = (x − MinV alue)/(MaxV alue −MinV alue).4) In practice application, the KPCA and SVM models areset up offline, and just the abrupt event diagnosis andmodel updating are online, which just need a few seconds.So, the proposed approach can be used for real-timeabrupt event monitoring.III. EXPERIMENTS FOR MONITORING ABRUPTEVENT IN WATER ENVIRONMENTDuring the monitoring, the water pollution accident and theabrupt sensor fault may happen at any time, so the accuraterecognition of the abrupt event is very important. The typicaldata characteristics of the emergency water pollution accidentand the abrupt sensor fault are shown in Fig. 3. Fig. 3(a) showsthe measured data of one temperature sensor. Fig. 3(b) and(c) shows the measured data of one pH sensor and dissolvedoxygen (DO) sensor, respectively. The data in Fig. 3(a) showthat it is obviously a sensor fault because the temperature ofthe water environment cannot change abruptly. However, it isdifficult to decide whether it is a sensor fault or an emergencywater pollution from Fig. 3(b) and (c) because the values ofpH and DO may be changed abruptly by both the sensor faultand the water pollution. At this condition, general approaches
  5. 5. 984 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 61, NO. 4, APRIL 2012Fig. 3. Typical data characteristics of the measured data during the two abruptevents. The data from (a) temperature sensor, (b) pH sensor, and (c) DO sensor.Fig. 4. Water environment management system based on sensor network.cannot recognize whether it is a sensor fault or emergencywater pollution. Furthermore, there are no mathematical modelsbetween the water quality parameters and the abrupt events, andonly the historical data can be used. So, the proposed approachbased on the data collected from the sensor system is suitablefor the abrupt event monitoring, which is different from themethods based on mathematical models.To get the data with high degree of accuracy, a sensornetwork including a lot of sensor nodes is distributed in waterenvironment, such as a lake or a river basin (see Fig. 4, andthe details have been addressed in relative literature [1], [29]).In order to test the performance of the proposed approach,the following experiments are conducted. In this paper, thedata monitored by a sensor node of the water environmentsensor system are analyzed. For simplification without los-ing generality, four types of important water quality parame-ters are monitored in this sensor node, namely, temperature(in degrees Celsius), pH (dimensionless), DO (in milligramsper liter), conductivity (in millisiemens per centimeter). Themeasurement sensors for these parameters are denoted by s1,TABLE ISOME PARAMETERS OF THE PROPOSED APPROACHs2, s3, and s4, respectively. In these experiments, one ad-ditional sensor for each type of water quality parameter isused. So, there are eight sensors used in these experiments,which are denoted by {s11, s12, s21, s22, . . . , s41, s42}. Thedata collected from these sensors are seen as a data set.The corresponding sensor faults and trust values of thesesensors are denoted by {F11, F12, F21, F22, . . . , F41, F42}and {T11, T12, T21, T22, . . . , T41, T42}, respectively. Consider-ing the accuracy of online monitoring, the trust value of eachsensor is set as [0.5, 1], namely, the trust degree of each sensoris between 50% and 100%.The parameters of the proposed approach in all the experi-ments are the same, which are listed in Table I. Other parame-ters are calculated adaptively according to (5), (6), and (9). Inthis paper, three experiments were conducted under differentsituations of abrupt event monitoring for water environmentsensor systems. In these experiments, the frequency of datacollection is 1 time per minute, and 200-times monitoring dataare input into the abrupt event monitoring system. The KPCAmodel of the proposed approach is updated per ten times underthe normal conditions.A. Abrupt Event Diagnosis for Abrupt Sensor FaultIn the first experiment, the water pollution accident does notoccur during the monitoring process. However, the conductivitysensor s42 is faulted abruptly at the 51st time. Under the normalworking condition, the measured data value of sensor s42 is inthe range of [2, 7]. If it is under the abrupt fault condition, thedata coming from the sensor will reduce to a fixed value. At the151st time, s42 is replaced by a new one, and the measureddata return to the accurate value. Fig. 5 shows the chart ofthe measured data by sensors s41 and s42. Fig. 6 shows thediagnosis chart by the proposed KPCA. The data value comingfrom s41 and s42 and the detailed abrupt event diagnosis resultsare listed in Table II.The data chart in Fig. 5 shows that there are some noisydata during the monitoring, which are denoted by some littlecircles. The diagnosis results in Fig. 6 show that the value ofSPEη is changed with the new data information. If the abnormalinformation is not detected in the new data or the final diagnosisresult is not a sensor fault, the new data are used to updateK-SDA (see the first 50 times and the last 50 times in Fig. 6).Then, the KPCA model is updated with the new data in K-SDA.
  6. 6. NI et al.: ABRUPT EVENT MONITORING FOR WATER ENVIRONMENT SYSTEM BASED ON KPCA AND SVM 985Fig. 5. Measured data of s41 and s42.Fig. 6. Diagnosis chart using the KPCA model for sensor fault.During the 51st time and 150th time, the abnormal informationis detected, so the data in K-SDA and the KPCA model are notupdated. After the sensor fault is removed, the data in K-SDAand the KPCA model are updated again.The results in Fig. 6 and Table II show that the abnormalinformation can be detected by the KPCA model as soon as itoccurs, such as the data at the 23rd time. Then, a further analysisis done by the SVM model to locate the abnormal sensor,which measures the abnormal data. The isolation result is{−1, −1, −1, −1, −1, −1, −1, 1}, which means that the sensors42 may be faulted. So, the trust value of s42 is subtracted10% to 90%. At the 24th time, the abnormal information is notdetected, and the trust value of s42 is added 10% to 100%. Bythis way, the influence of noise is removed. The same conditionoccurs at the 48th and 173rd times, where the abnormities arecaused by noise. At the same way, there are not any falsediagnoses when it is a real sensor fault, such as the data at the97th, 98th, and 148th times (see Table II and Fig. 6).When a real sensor fault occurs, the trust value of the sensorwill reduced continually. The trust value of T42 is lower thanTABLE IIDIAGNOSIS RESULTS FOR SENSOR FAULTthe threshold Th at the 53rd time, but the value of T41 is 100%at this time, so the sensor s42 is diagnosed as a fault sensorat this time. In this experiment, the real sensor fault occurs atthe 51st time, and it is diagnosed as a fault sensor at the 53rdtime (which can be seen in Fig. 6 and Table II). After the faultsensor is replaced by a normal one, the trust value of T42 willincrease continually. When the trust value of T42 is higher thanthe threshold Th at the 153rd time, this sensor is regarded asrestored. In this experiment, the fault sensor is replaced at the151st time.The results in this experiment show that the proposed ap-proach can deal with the interference of noise in the input data.Furthermore, the proposed approach can detect and recognizethe abrupt sensor fault correctly and quickly.B. Abrupt Event Diagnosis for Emergency Water PollutionTo test the performance of the proposed approach in themonitoring of emergency water pollution accident, an exper-iment with emergency water pollution is conducted. In thisexperiment, all the sensors are working normally, but thereis an emergency water pollution accident. At this accident,plenty of wastewater that contains high acidity is dischargedinto the monitoring area. The values of pH decrease suddenlyat this time. The measured data of pH sensors deviate from the
  7. 7. 986 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 61, NO. 4, APRIL 2012Fig. 7. Diagnosis chart using the KPCA model for water pollution accident.TABLE IIIDIAGNOSIS RESULTS FOR WATER POLLUTION ACCIDENTnormal values, and other data are within their normal range.The monitored data are analyzed by the proposed KPCA. Thediagnosis chart is shown in Fig. 7. The abrupt event diagnosisresults are listed in Table III.The diagnosis chart in Fig. 7 shows that the value of SPEηis changed continually. The diagnosis results in Fig. 7 andTable III show that the abnormality in the measured data canbe detected by the proposed approach as soon as the accidentoccurs at the 101st time. Both the trust values of the pH sensorss21 and s22 are decreased. After two times, the trust valuesof sensors s21 and s22 are both lower than the threshold Th.By the decision principles introduced in Section II-B, it canbe confirmed that the parameters monitored by these sensorsare changed abruptly, namely, the pH value of the water envi-ronment is changed obviously. It can be confirmed that thereis an emergency water pollution accident. Since it is a waterpollution accident, the measured data need not to be isolatedagain unless the abrupt events occur again. So, the new data ofpollution water information are inserted into K-SDA, and thediagnosis models are updated as soon as the water pollution isdiagnosed. Then, there are no abnormalities in the detected dataagain. The trust values of T21 and T22 are increased to 100%Fig. 8. Diagnosis chart using the KPCA model for complex abrupt event.(see Table III), which means that all of the measured data arereally the reflection of water environment.The results in this experiment show that the proposed ap-proach can detect the emergency water pollution accidentquickly and correctly. Furthermore, the proposed approach canrecognize which parameter changes abruptly at this accident,and then, the accident type of water pollution can be decidedcorrectly. It is very important for the government to solve thisaccident.C. Abrupt Event Diagnosis for Complex Abrupt EventTo further test the proposed approach performance at thecomplex conditions, an experiment with complex abrupt eventis conducted, where both the abrupt sensor fault and the emer-gency water pollution accident happen. In this experiment,plenty of wastewater is discharged into the water environmentabruptly at some time. This water pollution accident causes thevalues of DO sensors decrease, and the values of the conduc-tivity sensors increase suddenly. Furthermore, the temperaturesensor s12 is broken by the corrosive contaminants duringthe water pollution accident. The diagnosis chart by using theproposed approach is shown in Fig. 8. The measured data valueand the diagnosis results are listed in Table IV.The results in Fig. 8 show that the abnormal informationis detected by the KPCA model at the 101st time. Then, theabnormality is analyzed by the SVM model. The isolation resultis {−1, 1, −1, −1, 1, 1, 1, 1}, which means that the measureddata from sensors s12, s31, s32, s41, and s42 are abnormal.However, it is difficult to make sure whether it is an abruptsensor fault or a water pollution accident at this time. Thefollowing analysis results in the 102nd and 103rd times showthat the abnormal information is the same as in the 101st time.So, the trust values of these sensors are decreased continually,and the trust values of the third- and fourth-type sensors (see thevalues of T31, T32, T41, and T42 in Table IV) are less than thethreshold Th at the same time. Based on the decision principles,the water pollution accident can be confirmed at the 103rd time.Furthermore, because just one trust value of the first-type sensor
  8. 8. NI et al.: ABRUPT EVENT MONITORING FOR WATER ENVIRONMENT SYSTEM BASED ON KPCA AND SVM 987TABLE IVDIAGNOSIS RESULTS FOR COMPLEX ABRUPT EVENTSs12 is less than the threshold Th, it can be confirmed that this isan abrupt sensor fault event (see the values of T12 in Table IV).Because it is not a sensor fault in the third- and fourth-typesensors, the trust values of T31, T32, T41, and T42 are increasedto 100% at the 104th time. However, the trust value T12 ofsensor s12 is decreased until to the minimum value of 50%.The results of this experiment show that the proposed ap-proach has the ability to deal with the abrupt event monitoringproblem effectively, even when the two different types of abruptevents happen at the same time.IV. COMPARISON AMONG THE PROPOSED APPROACH,BASIC KPCA, AND BP NEURAL NETWORKTo illustrate the advantages of the proposed approach, acomparison experiment is conducted. In this experiment, theproposed approach is compared with the approach based on thebasic KPCA [21], [22] and that based on BP neural network[28], [30]. In order to have comparability, an additional sensorof each type parameter is used also for the basic KPCA andBP neural network. The data sets used to train and test the BPneural network, the basic KPCA, and the proposed approach aresame. In the test data sets, there are 100 sets of normal data, 100sets of water pollution data, and 100 sets of sensor fault data.The structure of the BP neural network used in this experi-ment is shown in Fig. 9. The number of the input and outputneurons of the BP neural network is eight, and the number ofthe neurons in the hidden layer is ten. The parameters of thebasic KPCA are the same as those of the proposed approach,except σ2= 5m = 40, p = 3, and γ = 1, which are adaptive inthe proposed approach. The diagnosis charts of the basic KPCAand the proposed KPCA methods are shown in Figs. 10 and 11,respectively.The results in Fig. 10 show that the SPEη value of the basicKPCA method is fixed during the whole monitoring process.However, the SPEη value of the proposed KPCA method isfixed only after the 200th time when the sensor fault is detected.Although the abnormal information can be detected by thebasic KPCA, the data will be detected as abnormal based onthe basic KPCA, during the whole water pollution accident(see Fig. 10). So, these abnormal data need be isolated bythe SVM method. However, in the proposed approach, theKPCA model is updated immediately when the water pollutionFig. 9. Structure of BP neural network.Fig. 10. Diagnosis chart of the basic KPCA method.is detected. So, there are no abnormalities during the waterpollution (see Fig. 11). At some time, this performance ofthe proposed approach can save the emergency response time,which is very important in dealing with emergency event ofwater environment (the time to isolate and recognize the abruptevent by a data set is 5.4 s, using the computer with 1.0 GHzphysical memory and 2.8 GHz CPU clock speed).The diagnosis accuracy based on the three methods is shownin Table V, where the lost diagnosis means that the real abruptevent cannot be detected and the error diagnosis means thatthe abrupt event can be detected but the final diagnosis results
  9. 9. 988 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 61, NO. 4, APRIL 2012Fig. 11. Diagnosis chart of the proposed approach.TABLE VDIAGNOSIS ACCURACY BASED ON THREE DIFFERENT METHODSare wrong. The experimental results in Table V show that thediagnosis accuracy of the BP neural network is 89.0%; themain reasons are that the BP neural network easily falls intoa local minimum value and the water environment model isvery difficult to be identified. The basic KPCA method is su-perior to the BP neural network in the abrupt event monitoringfor the water environment sensor system because the kernelmethod can map the input space to the feature space that isvery suitable for solving the nonlinear problem. However, theadaptability and anti-interference of the basic KPCA methodare poor. Furthermore, the model of the basic KPCA cannotbe updated with the change of external environment, so moretime is needed to isolate the abnormal event. The proposedapproach can update the diagnosis model with the change ofexternal environment, and the trust mechanism can eliminatethe effect of noise. Although there are two time delays when anabrupt event occurs (such as the 51st, 52nd, 151st, and 152ndtimes in Table II), the diagnosis accuracy based on the proposedapproach is up to 98.7%. The results of this experiment showthat the proposed approach is more efficient than the basicKPCA and BP neural network in the abrupt event monitoringfor water environment sensor systems.V. CONCLUSIONAbrupt event monitoring for water environment sensor sys-tems has been investigated in this paper. In order to recognizethe abrupt event accurately, a novel approach based on KPCAand SVM methods has been proposed. In the proposed ap-proach, a trust mechanism is used to reduce the interferenceof noise. A spare data area is proposed to store and updatethe data for KPCA modeling. Some important parameters inthe proposed approach are adaptive. The proposed approachcan detect and recognize the abrupt event in water environmentsensor systems quickly and correctly. The proposed approachjust needs one additional sensor to recognize the abrupt event,which is less than the general physical redundancy method. Theproposed approach can deal with various situations, such as justan abrupt sensor fault and the concurrent abrupt sensor fault andwater pollution accident. A comparison among the proposedapproach, basic KPCA method, and BP neural network is con-ducted through experimental studies. The experimental resultsshow that the proposed approach is efficient for the abrupt eventmonitoring of water environment systems.ACKNOWLEDGMENTThe authors would like to thank the editors and the reviewersfor their helpful comments.REFERENCES[1] E. Hatzikos, N. Bassiliades, L. Asmanis, and L. Vlahavas, “Monitoringwater quality through a telematic sensor network and a fuzzy expertsystem,” Expert Syst., vol. 24, no. 3, pp. 143–161, Jul. 2007.[2] P. 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Tkalich, “An ANN application for waterquality forecasting,” Marine Pollution Bull., vol. 56, no. 9, pp. 1586–1597,Sep. 2008.Jianjun Ni (M’11) received the Ph.D. degree fromthe School of Information and Electrical Engineer-ing, China University of Mining and Technology,Xuzhou, China, in 2005.He is currently an Associate Professor with theCollege of Computer and Information, Hohai Uni-versity, Changzhou, China. From November 2009 toOctober 2010, he was a Visiting Professor with theAdvanced Robotics and Intelligent Systems Labora-tory, University of Guelph, Guelph, ON, Canada. Hehas served as a Reviewer of a number of internationaljournals. He has published over 20 papers in related international conferencesand journals. His research interests include fuzzy systems, neural networks,robotics, machine intelligence, and multiagent system.Chuanbiao Zhang received the B.S. degree fromHohai University, Changzhou, China, in 2008, wherehe is currently working toward the M.S. degree inthe Department of Information and CommunicationEngineering, College of Computer and Information.His research interests include intelligent informa-tion processing and fault diagnosis.Li Ren received the Ph.D. degree from the College ofHydrology and Water Resources, Hohai University,Nanjing, China, in 2009.She is currently a Lecturer with the College of Hy-drology and Water Resources, Hohai University. Shehas served as a Reviewer of a number of internationaljournals. She has published over ten papers in relatedinternational conferences and journals. Her researchinterests include ecohydrology, water resource sys-tem optimization, and management and utilization ofwater resources.Simon X. Yang (SM’08) received the B.Sc. de-gree in engineering physics from Beijing University,Beijing, China, in 1987, the M.Sc. degree in bio-physics from the Chinese Academy of Sciences,Beijing, in 1990, the M.Sc. degree in electrical en-gineering from the University of Houston, Houston,TX, in 1996, and the Ph.D. degree in electricaland computer engineering from the University ofAlberta, Edmonton, AB, Canada, in 1999.In 1999, he joined the School of Engineering, Uni-versity of Guelph, Guelph, ON, Canada, where he iscurrently a Professor and the Head of the Advanced Robotics and IntelligentSystems Laboratory. He serves as an Associate Editor of International Journalof Robotics and Automation. His research interests include intelligent systems,robotics, sensors and multisensor fusion, wireless sensor networks, controlsystems, soft computing, and computational neuroscience.Prof. Yang serves as an Associate Editor of IEEE TRANSACTIONS ONNEURAL NETWORKS and IEEE TRANSACTIONS ON SYSTEMS, MAN, ANDCYBERNETICS—PART B and an Associate Editor or Editorial Member ofseveral other journals. He has been involved in the organization of manyconferences.