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Human Factors and Ergonomics in Manufacturing, Vol. 19 (1) 64–77 (2009) C 2008 Wiley Periodicals, Inc. Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/hfm.20136 Evaluation and Prediction of On-Line Maintenance Workload in Nuclear Power Plants Guo-Feng Liang, Jhih-Tsong Lin, and Sheue-Ling Hwang Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan Fei-hui Huang Oriental Instituteof Technology, Banqiao, Taiwan Tzu-Chung Yenn and Chong-Cheng Hsu Institute of Nuclear Energy Research, Lungtan, Taiwan ABSTRACT This study evaluates engineers’ mental workload while maintaining digital systems in nuclear power plants (NPPs). First, according to the factors affecting the mental workload, a questionnaire was designed to evaluate the mental workload of maintenance engineers at the Second NPP in Taiwan. Then 16 maintenance engineers from the Second NPP participated in the experiment survey. The results indicated that the mental workload was lower in maintaining digital systems than that in analog systems. Finally, a mental workload model based on the neural network technique was established to predict the mental workload of maintenance engineers in maintaining digital systems. Through predicting mental workload, the manager can organize the human resources for each daily task to sustain the appropriate mental workload as well as improve maintenance performance. C 2008 Wiley Periodicals, Inc. 1. INTRODUCTION During the past several decades, there has been rapid growth of digital systems due to the development of automation and the electronic society. As systems increasingly change from analog to digital, so do mental workload and maintenance for more precisely controlled and predictive assessment growth. There have been numerous studies in the literature showing the source of workload and carrying out the maintenance method during an off-line system. Unfortunately, there have been few studies on on-line maintenance, especially for nuclear power plants (NPPs). Taiwan’s thermal electric generating plants have already successfully upgraded from analog systems to digital systems. Not only have Taiwan’s First, Second, and Third NPPs had their generator systems upgraded from analog to digital, but the newest Fourth NPP uses only digital systems. Therefore, the present study compares the mental workload of maintenance engineers between analog and digital on-line maintenance systems to assess and predict on-line maintenance workload for digital systems. Through interviews with Correspondence to: Sheue-Ling Hwang, Department of Industrial Engineering and Engineering Manage- ment, National Tsing Hua University, Hsinchu 300, Taiwan. E-mail: [email protected] 64

Evaluation and prediction of on-line maintenance workload in nuclear power plants

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Human Factors and Ergonomics in Manufacturing, Vol. 19 (1) 64–77 (2009)C© 2008 Wiley Periodicals, Inc.Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/hfm.20136

Evaluation and Prediction of On-Line MaintenanceWorkload in Nuclear Power Plants

Guo-Feng Liang, Jhih-Tsong Lin, and Sheue-Ling HwangDepartment of Industrial Engineering and Engineering Management,National Tsing Hua University, Hsinchu, Taiwan

Fei-hui HuangOriental Institute of Technology, Banqiao, Taiwan

Tzu-Chung Yenn and Chong-Cheng HsuInstitute of Nuclear Energy Research, Lungtan, Taiwan

ABSTRACT

This study evaluates engineers’ mental workload while maintaining digital systems in nuclear powerplants (NPPs). First, according to the factors affecting the mental workload, a questionnaire wasdesigned to evaluate the mental workload of maintenance engineers at the Second NPP in Taiwan.Then 16 maintenance engineers from the Second NPP participated in the experiment survey. Theresults indicated that the mental workload was lower in maintaining digital systems than that in analogsystems. Finally, a mental workload model based on the neural network technique was establishedto predict the mental workload of maintenance engineers in maintaining digital systems. Throughpredicting mental workload, the manager can organize the human resources for each daily task tosustain the appropriate mental workload as well as improve maintenance performance. C© 2008 WileyPeriodicals, Inc.

1. INTRODUCTION

During the past several decades, there has been rapid growth of digital systems due to thedevelopment of automation and the electronic society. As systems increasingly change fromanalog to digital, so do mental workload and maintenance for more precisely controlled andpredictive assessment growth. There have been numerous studies in the literature showingthe source of workload and carrying out the maintenance method during an off-line system.Unfortunately, there have been few studies on on-line maintenance, especially for nuclearpower plants (NPPs).

Taiwan’s thermal electric generating plants have already successfully upgraded fromanalog systems to digital systems. Not only have Taiwan’s First, Second, and Third NPPshad their generator systems upgraded from analog to digital, but the newest Fourth NPPuses only digital systems. Therefore, the present study compares the mental workload ofmaintenance engineers between analog and digital on-line maintenance systems to assessand predict on-line maintenance workload for digital systems. Through interviews with

Correspondence to: Sheue-Ling Hwang, Department of Industrial Engineering and Engineering Manage-ment, National Tsing Hua University, Hsinchu 300, Taiwan. E-mail: [email protected]

64

ON-LINE MAINTENANCE WORKLOAD IN NUCLEAR POWER PLANTS 65

16 experts working in the Second NPP, we obtained the significant changes in mentalworkload when the system upgraded from the analog to digital systems. In addition, toquantify mental workload, we use the group method of data handling (GMDH) neuralnetwork technique to develop a model to predict the level of mental workload. The resultsof this study could be applied in NPPs for human resource arrangement, task scheduling,and other areas to reduce human error.

2. BACKGROUND

The mental workload for maintenance is an important issue for modern digital systems ofNPPs. Previous studies on maintenance tasks, mental workload, factor analysis for mentalworkload, and GMDH are reviewed in this section.

2.1. Maintenance Task

The maintenance for NPPs is at least as demanding as that for transportation services and theaviation industry where managers pay close attention to maintenance operations. Previousstudies have applied quantitative methods such as Petri net, Bayesian network, regressionmodel, and neural network (Georges, 2002; Gyunyoung, 2005; Lee & Seong, 2005; Yang,Yan, & Chen, 2003) to resolve the maintenance problems in NPPs. However, most previousstudies have focused on off-line maintenance operations (Adzakpa, Adjallah, & Yalaoui,2004; Jovanovic, 2003; Masao, 2000; Montes, Gamez, Romero, Mondelo, & Terres, 2007;Sauer, Zimolong, & Ingendoh, 2000). Currently, on-line maintenance for NPPs is performedquite often while the system is in operation, and digital concepts are gradually consideredby various industrial designs (Chang & Wang, 2007; Colin, Vergnon, Guibaud, & Borson,1998). Nevertheless, the mental workload of maintenance engineers has never been studiedin this kind of on-line maintenance.

2.2. Mental Workload

Mental workload has long been recognized as an important factor in human performancein complex systems (Xie & Salvendy, 2000a). Moray (1988) pointed out that optimizingthe allocation of mental workload of engineers could reduce human errors, improve systemsafety, increase productivity, and increase engineers’ job satisfaction (Moray, 1988; Xie &Salvendy, 2000a). Thus, it is important to develop better methods for critical applicationssuch as assessing and predicting mental workload, especially in NPPs.

There are a number of methods for the evaluation and prediction of mental work-load. These methods can be classified into three categories, namely, subjective measures,performance-based measures, and physiological measures (Rubio, Dıaz, Martin, & Puente,2004).

Subjective mental workload can be defined as subject’s direct estimate or comparativejudgment of the mental or cognitive workload experienced at a given moment (Ameersing& Ravindra, 2001), such as the Cooper–Harper scale (Cooper & Harper, 1969), the Modi-fied Cooper–Harper Scale (MCH; Wierwille & Casali, 1983), the consumer mental work-load scale (Owen, 1992), the Subjective Workload Assessment Technique (SWAT; Reid &Nygren, 1988), and the National Aeronautics and Space Administration Task Load Index(NASA-TLX) Scale (Hart & Staveland, 1988).

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66 LIANG ET AL.

Performance-based measures include primary-task and secondary-task measures. In thiscontext, a primary task is a task in which the workload is under consideration (Lysaght,Hill, Dick, Plamondon, & Linton, 1989), whereas a secondary task is one that is artificiallyadded to determine the amount of “spare mental capacity” available when the individual isperforming the primary task (Kantowitz, 1987).

Physiological methods measure the changes in human physical status, such as eye blinkrate, heart rate, oxygen consumption, ongoing electroencephalogram (EEG), and evokedpotentials (Beatty, 1982; Bin & Gavriel, 2000; Jorna, 1993). Eye blink rate contains valuableinformation with regard to the visual demands of tasks. Heart rate is useful to determine thehuman global response to task demands. An EEG provides useful information about highworkload and inattention (Glenn & Christopher, 2003).

Some studies have tried to integrate these three types of measures. For example,Wierwille and Casali (1983) considered three subjective workload measures, seven primary-task and secondary-task workload measures, and six physiological indexes to analyze mentalworkload. The result revealed that the significant indexes on mental workload (p < 0.05)were the two subjective workload measures of Modified Cooper–Harper and WCI/TE(Workload–Compensation–Interference/Technical Effectiveness); the three primary andsecondary tasks of time estimation, mediation error rate (MER), and mediational reac-tion time (MRT); and the two physiological measures of eye blink and fixation fraction(FF). The present study adopted the questionnaire from the subjective method to evaluatea maintenance engineer’s workload in an NPP. Furthermore, physiological assessment wasmade to record the engineers’ physical situation before and after maintenance tasks. Beforedesigning the questionnaire, we surveyed the relative factors that affected mental workload.

2.3. Group Method of Data Handling (GMDH)

To establish a prediction model of mental workload, we surveyed the neural networkmethodologies. One of the better-known neural network methodologies is GMDH, which isone of the recent exact prediction methods. In general, neural networks can be constructedwithout requiring any assumptions of the relationship between predictors and response(Stern, 1996).

Previous studies have focused on GMDH algorithm’s improvements and its applicationsin various fields such as education, business, and the like (Baker & Richards, 1999; Hwanget al., in press; Ivakhnenko, 1995; Kim, Kim, & Lee, 2001). It can also be applied incomplex economies (Sarycheva, 2003). For instance, Tomonori and Shizue (1982) utilizedthe time serial concept for application in a vehicle factory. They collected three groups oftime-series data as input variables and then produced an accurate model. Pavel and Miroslav(2003) used the high school from which students graduated, their age, and their grades todistinguish the rank of freshmen students. Then they combined the neural networks and theGMDH algorithm to make an accurate analysis model to predict the student ranking. In thecurrent study, GMDH was used to predict mental workload for digital systems.

3. METHOD

In the present study, a field experiment was conducted while maintaining both analog anddigital feedwater control systems in the Second Nuclear Power Plant (SNPP). A question-naire was then administered to investigate the significant factors of mental workload, and amodel was established to predict maintenance engineers’ mental workload.

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ON-LINE MAINTENANCE WORKLOAD IN NUCLEAR POWER PLANTS 67

3.1. Participants

Sixteen maintenance engineers from SNPP, who have more than 10 years of maintenanceexperience, served as participants. Each participant was paid NT$500 for taking part in thisstudy.

Teams of four participants performed a test mission on two types of systems. The partic-ipants were randomly assigned to team roles: two of them monitored the feedwater controlsystem in the main control room, and the other two tested corresponding functions onmaintenance equipments.

3.2. Apparatus

The apparatus of this experiment included an ANSWatch TS0411 device to measure pulserate, two video cameras to record experimental process, a stopwatch to record maintenancetime, and two feedwater control systems (one an analog system and the other a digitalsystem; Figure 1 and Figure 2).

3.3. Questionnaire Design

Investigating the factors that affect engineers’ mental workload is an important task in thisstudy. Not only can we find out what factors are critical to engineers, but we can also establisha prediction model to measure mental workload through these critical factors. Therefore,to survey the factors that affected mental workload, we refer to NUREG-0711 (Rev. 2)and some relevant studies (Abdallah, Genaidy, Salem, & Karwowski, 2004; Genaidy &

Figure 1 An analog feedwater control system.

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68 LIANG ET AL.

Channel CChannel BChannel A

Figure 2 A digital feedwater control system.

Karwowski, 2003; Genaidy, Karwowski, & Christensen, 1999; Kantowitz, 1987; Welford,1978; Yamamura, Yata, Yasushi, & Yamaguchi, 1989). Furthermore, we integrated theelements affecting mental workload and classified them into five factors: human workload,machine factors, material factors, method factors, and environment factors. Based on thesefactors, the questionnaire was designed from the fishbone as shown in Figure 3.

Workload

Method

Environment Machine Human

test bymanpower

test by auto-self diagnosis

Standard OperatingProcedure

experiment

how to findtroubles

complexity

Material

gender

experience

age

alarm

the design of

software

how easy to read

tools

backup

noise

temperaturelanewidth

height illumination

maintenance

easy to take apart

analog or

diagial

pressure

the number ofmanpower

interface

working time

auto-self diagnosis

difficulty

knowledge

display

Figure 3 Fishbone of the workload factors.

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The maintenance task questionnaire consisted of twenty-five questions in five parts: sixquestions about human workload in part A, five questions about maintenance tasks in part B,four questions about materials in part C, six questions about methods to accomplish tasksin part D, and four questions about environment in part E. The subjects responded using a6-point scale (strongly disagree to strongly agree).

3.4. Experimental Task and Variables

There are two types of maintenance systems: digital and analog. To execute on-line digitalmaintenance, the participants have to follow specific procedures on testing equipment that isconnected to a personal computer with a graphical interface. To conduct an analog system,the participants have to accomplish a task with a specific procedure and several electronicmeters or specific tools.

A procedure for testing the reactor high water level was selected for this study. That wasbecause there are two feedwater control systems in SNPP: one has been updated to a digitalsystem, and the other one is still a traditional analog system. Also, this is an importantmonthly maintenance procedure for NPPs.

The independent variable in this experiment was the type of maintenance system, includ-ing analog and digital systems; and the dependent variables were testing time, pulse rate,number of testing errors, and subjective measures, which were described as follows:

• Testing time: The total time engineers spent testing either type of system.• Pulse rate: The experimenter measured and recorded each participant’s pulse rate at

the beginning and the end of testing. The pulse rate can show increase of mentalworkload.

• Number of testing errors: The engineers (participants) estimate on number of errorsper 1,000 maintenance tests for each type of system.

• Subjective measures: A questionnaire was designed for subjective evaluation of mentalworkload in a maintenance task. Another two items with 6-point scales were given forparticipants to compare the digital system to the analog system in terms of complexityand familiarization.

The dependent variables would serve as predictors in model development. To collect thedata of dependent variables, the steps of experimental procedure are as follows:

Step 1: Before starting the experiment, 5 minutes of initial physiological data was takenas a baseline record for each participant.

Step 2: According to the standard operating procedure (SOP), each team conducted thedigital maintenance system and then the analog one.

Step 3: After completing each type of system, the maintenance time and the physiolog-ical data of each engineer were recorded, and the maintenance error evaluationand the questionnaire were filled out.

3.5. Data Analysis

The results of the questionnaire and experiment were analyzed by the Statistical Productsand Services Solution (SPSS) software.

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The testing time (in seconds), pulse rate, and number of testing errors between themaintenance of different system types were compared using t tests. As to the subjectivemeasurements—human workload, maintenance task, materials, the way to accomplish task,environment, number of maintenance errors, complexity, and familiarization—the nonpara-metric Wilcoxon matched-pairs signed-rank test was applied for two reasons (Siegel &Castellan, 1988). One is that the data in each question represents a ranked order of obser-vations (ordinal) rather than precise measurements. The other one is that the populationparameter of the experiment was unknown.

From the collected data, the eight dependent variables (testing time, pulse rate, numberof testing errors, complexity, familiarization, materials, means to accomplish task, andenvironment) were applied as input variables for a neural network with the GMDH algorithmto establish a mental workload predicting model. The neural networks can be regarded as astatistical method and be constructed without requiring any assumptions of the relationshipbetween predictors and response (Stern, 1996).

3.6. Quantification of Questionnaire

To quantify the questionnaire, we developed a scaling method to compare the two systems inthis study. First, the rating scales of engineers’ self-assessment were transferred into mentalworkload values. Then the values of mental workload were calculated using Equation 1:

Ym =∑

j

i

Wmij , m = 1, 2, (1)

where Ym is the total mental workload values of system m (m = 1, 2); and Wmij is the mentalworkload value of factor i, question j in system m [i = A,B,C,D,E; j = A1 ∼ A6(A1, A2,A3, A4, A5, A6); B1 ∼ B5(B1, B2, B3, B4, B5); C1 ∼ C4(C1, C2, C3, C4); D1 ∼ D6(D1, D2, D3,D4, D5, D6); E1 ∼ E4(E1, E2, E3, E4)]. The relative mental workload percentage in systemm can be derived by

Rm =∣∣∣∣∣

∑j

∑i Wmij

L1 − L2

∣∣∣∣∣ × 100%, m = 1, 2, (2)

where Rm is the relative mental workload percentage in system m. From the result ofWilcoxon matched-pairs signed-rank test, if the factor i, question j between system m(m = 1, 2) is not significant (p > 0.05), then Wmij = 0. This means the mental workloadvalue is equal to zero. Otherwise, if the factor i, question j between system m (m = 1, 2)is significant (p < 0.05), then Wmij ranges from 1 to 6. Furthermore, to obtain the degreeof mental workload, L1, L2, and Lave can be calculated by

L1 = n1 × 1 × n2, (3)

L2 = n1 × 6 × n2, (4)

Lave = L1 + L2

2, (5)

where L1 is the lower bound of these significant total values from the questionnaire; L2 is theupper bound of these significant total values; Lave is the mean; n1 represents the significant

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questions; and n2 represents the number of engineers responding to the questionnaire. If thevalue of Ym is close to the max (L1, L2), this indicates a high mental workload; if the valueis close to the min (L1, L2), this indicates low mental workload; and if the value is close tothe mean, Lave, this indicates a medium.

3.7. A Network Model for Mental Workload

The systems at the Fourth Nuclear Power Plant (FNPP) use only digital technology. Tomeasure and predict maintenance workloads for each maintenance procedure, a method ofnetwork models was used. In these network simulations, time (X1), pulse rate (X2), numberof maintenance errors (X3), and maintenance self-assessment—that is, complexity (X4),familiarization (X5), materials (X6), the means to accomplish task (X7), and environment(X8)—are represented as the prime predictors of maintenance workload.

3.8. Model Predictors

The model predictors consisted of three parts. They were subjective mental workload,performance-based measure, and physiological method. The number of maintenance errors(X3), complexity (X4), familiarization (X5), materials (X6), the means to accomplish task(X7), and environment (X8) pertained to the subjective mental workload. Time (X1) was aperformance-based measures and pulse rate (X2) was a physiological method.

4. RESULTS

4.1. Testing Time, Pulse Rate, and Subjective Measures

The results of the t test indicated that there was significant difference between maintaining adigital system and maintaining an analog system (p < 0.05) on testing time. No significanteffects were found on pulse rate or number of testing errors. The results of a Wilcoxon signtest indicated that there were significant differences between maintaining a digital systemand maintaining an analog system on some questions (p < 0.05). As shown in Table 1, theparticipants rated higher agreement when maintaining a digital system than that when usingan analog system.

According to the results of subjective rating, the weightings for each question werecalculated, as shown in Table 2. One can see that human workload and materials are themost significant parts that affect engineers’ mental workload when maintaining either typeof system.

The outcome of Y1 was equal to 820, which was higher than Lave (i.e., 520), and Y2

equaled 557, which was also higher than Lave. Obviously, the value of Y1 was higher thanY2. In addition, from Question 2, we can obtain the maintenance engineers’ mental workloadpercentages on analog and digital systems, which are 78% and 54%, respectively:

R1 =∣∣∣∣

820

1248 − 208

∣∣∣∣ × 100% = 78%

and

R2 =∣∣∣∣

557

1248 − 208

∣∣∣∣ × 100% = 54%.

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72 LIANG ET AL.

TABLE 1. The Results of Subjective Rating on Operators’ Agreement

Mental Workload (Scores)

Item (Analog) (Digital) p-Value

A3 The current number of teams for maintaining thissystem is perfect.

65 54 0.039∗

A4 I did not feel stress during maintaining this system. 77 68 0.017∗

A6 I would not forget or skip any step of procedure duringtesting.

58 48 0.024∗

B1 If I hear an alarm, I could immediately tell teammembers the current state.

61 39 0.004∗

B2 The system operates well, and does not need extramaintenance.

40 30 0.020∗

C1 The redundant equipment on the system is sufficient. 61 36 0.006∗

C2 It is easy to handle and deal with events on NPP withcurrent equipment.

58 34 0.002∗

C3 Maintenance tasks could be easily accomplished. 54 34 0.007∗

C4 I do not have to check my action on procedure list allthe time during testing.

76 67 0.042∗

D1 The current maintenance system might assist operatorsto accomplish tasks (e.g., self-diagnosis).

82 31 0.001∗

D6 Follow the system guide, it is easy to accomplishmaintenance tasks.

57 39 0.008∗

E2 The equipment on the maintenance system isunsusceptible to environment.

52 42 0.017∗

X4 Complexity. 62 37 0.005∗

∗A significant difference at the 0.05 levels of significance. A = human workload; B = maintenance task; C =materials; D = the way to accomplish task; E = environment; X4 = complexity; 1 ∼ 6 = the question number.

4.2. Mental Workload Predicting Modeling

Each rating scale for X4, X5, X6, X7, and X8 was given a numerical value from 1 to 6. Thedata X1, X2, . . . , X8 were used as inputs in the network system to get an output Y (i.e.,mental workload) that ranged from 9 to 36 (the maintenance process had nine steps and

TABLE 2. Weightings of Each Question Item for Different Types of Maintenance System

Weighting When Using Weighting When UsingMaintenance Analog System Maintenance Digital System

Human workload 0.227 0.285Maintenance task 0.115 0.116Materials 0.282 0.286The way to accomplish task 0.247 0.176Environment 0.059 0.0704Total 1 1

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TABLE 3. Relational Information of Model

Estimated Real Lower Bound Upper BoundX1 X3 X4 Value Value of 95% C.I. of 95% C.I.

45 11 3 10.061 10 9.307 10.815

the value of each step was from 1 to 4). There were 15 raw data, and NeuroShell2 wasused to construct the model. The results indicated that the factors X1, X3, and X4 affectedmaintenance engineers’ mental workload, while other factors did not. Then given the valuesfor X1, X3, and X4, a model of mental workload was yielded and expressed by the followingequation:

Y = 1.3X3 − 0.0072X1X3 + 0.061X1X4 − 0.27X3X4. (6)

In Equation 6, X1, X3, and X4, respectively, represent testing time (in seconds), the numberof maintenance errors (subjective measure of number of maintenance errors every thousandmaintenance), and complexity (subjective measure ranged from 1 to 6) of compositionalrule. Also, the MSE of the model was 0.114101 and the R2 of the model was 0.978317. Thisequation is expected to provide managers a reference value for the maintenance engineer’smental workload (Y) by giving input testing time (X1), number of maintenance errors (X3),and complexity (X4).

4.3. Model Validation

For validation, we put one datum into the model with the relational information as describedin Table 3. In this model, the estimated value was very close to the real value and was inthe 95% confidence interval. Therefore, the model is suitable and accurate to estimate themental workload.

5. DISCUSSION

5.1. The Effects of Digital Systems

Previous studies on digital systems have been focused on cost analysis, diagnostic accuracy,resource assignment, and workplace evaluation (Adzakpa et al., 2004; Chang & Wang, 2007;Colin et al., 1998). However, few studies have been done on the effect of digital systems.The results of the experiment revealed that maintenance engineers spent less maintenancetime to maintain a digital system than an analog system. Also, the results of subjectivemeasure indicated that the maintenance engineers using a digital system had lower mentalworkload (human workload, maintenance task, materials, the means to accomplish tasks,and environment) than those using an analog system, especially for human workload andmaterials questions (please refer to Table 1). These results prove that using the digitalsystem may effectively help engineers accomplish their mission and reduce their mentalworkload. Moreover, from the weighting for each question in the questionnaire (pleaserefer to Table 2), it is also indicated that the questions of human workload and materials

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74 LIANG ET AL.

weighed the most on maintenance engineers’ mental workload. This implied that the humanworkload of each engineer and the available materials in NPPs should be considered.

5.2. The Network Model

The GMDH algorithm has been widely applied in various fields, including education (Baker,1999; Pavel & Miroslav, 2003), economic systems (Sarycheva, 2003), weather modeling(Kim et al., 2001), manufacturing (Ivakhnenko, 1995), pattern recognition (Ivakhnenko,Ivakhnenko, & Muller, 1993), and physiological experiment (Hwang et al., in press). In thisstudy, the GMDH algorithm provides a reasonable prediction model based on the significantfactors of mental workload.

In the study of Wilson and Russel (2003), several physiological factors were used tocategorize mental workload into baseline, low, and high. However, they did not establish aprediction model based upon those physiological factors. Additionally, Xie and Salvendy(2000b) established a prediction mental workload model by using regression. In our study,the GMDH method was applied on model construction, and one of the significant variablesin the prediction model, task complexity, was consistent with the finding in the model of Xieand Salvendy (2000b). Furthermore, the prediction model by GMDH may obtain a higherR2 value.

In practical implication, this model can be used to predict the maintenance engineers’mental workload and apply for the prealarm system. Although the model developmentprocess is still at an early phase, supervisors might use it to predict the value of a maintenanceengineer’s mental workload on each maintenance procedure.

5.3. Limitations

There are some limitations in this study. One is the limited number of participants. Currently,there are 16 maintenance engineers who have experience in both analog and digital systemsin SNPP, and all of them were invited to participate in the study. In the long run, moreengineers will be skilled in digital maintenance so that the robustness of the model can beimproved. With regard to methodological limits, this study lacks wireless instruments tomeasure physiological data on-line in this experiment. The radio frequency identification(RFID) technology is suggested to be used for further study.

6. CONCLUSIONS AND FUTURE RESEARCH

The mental workload for maintaining a digital system has been compared with that foran analog system. Although there were more confirmative actions during maintaining adigital system than that for an analogy system, the mental workload of digital ones waslower because every confirmative action was easy and quick for maintenance engineers.Apparently, the digital systems used in the NPPs can significantly reduce maintenanceengineers’ mental workload according to the results of this study.

Summarizing the results of the questionnaire survey, the digital systems may be used forthe FNPP of Taiwan. In addition, a prediction model has been established in this study forpredicting maintenance engineers’ mental workload to avoid potential human errors. Thismodel can be integrated into a prealarm system by predicting the mental workload.

In practical applications, the predictive mental model provides managers a referencevalue of the maintenance engineers’ mental workload based on the given database of testing

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time, number of testing errors, and complexity. The number of testing errors and complexityin the database can be collected from historical data and the testing time can be collectedon-line during performing maintenance tasks.

Maintenance is an important issue for the safety of the FNPP operation, and this researchfocuses on on-line maintenance. The results of questionnaire administration show importanton-line factors that would significantly affect the mental workload. These significant factorswere coordinated with GMDH to establish a predictive model of mental workload. Forfuture study, we may use an algorithm such as the simulated annealing algorithm, the antcolony system, or others to generate an optimal model.

NPPs will eventually use digital systems. Although the digital system could reduceengineers’ mental workload, too low mental workload may decrease vigilance of operatorsin the main control room. To establish a balanced mental workload for NPP operators, thevisual or auditory prealarm design is necessary. However, the first step is to develop a modelto predict levels of mental workload.

ACKNOWLEDGMENTS

A portion of this study was supported by a grant from the Institute of Nuclear EnergyResearch (Project No.952001 INER004). The authors also wish to acknowledge the valuableassistance of Liu Tsong-Hsing from the Second Nuclear Power Plant of Taiwan.

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