Considering dangerous environmental conditions, maintenance of radioactive equipment can be performed by remote handling maintenance (RHM) system. The RHM system is a sophisticated man-machine system. Therefore, human factors analysis is an inevitable aspect considered in guaranteeing successful and safe task performance. This study proposes an approach for integrated analysis of human factors in RHM so as to make the evaluating process more practical. In the approach, indicators of accessibility, health safety, and fatigue are analyzed using virtual human simulation technologies. The human error factors in the maintenance process are analyzed using the human error probability (HEP) based on the success likelihood index method- (SLIM-) analytic hierarchy process (AHP). The psychological factors level of maintenance personnel is determined with an expert scoring. The human factors for the entire RHM system are then evaluated using the interval method. An application example is present, and the application results show that the approach can support the evaluation of the human factors in RHM.
Considering the environmental conditions for maintenance, radioactive equipment maintenance can be completed remotely without the need for any site personnel [
Current human factors engineering studies on RHM need to be improved from the following aspects. Human factors evaluation in RHM is a complicated multi-index evaluation process with certain difficulties in quantitative and qualitative analyses. Therefore, this process requires highly effective evaluation methods. Conventional RHM evaluations usually need to be completed through actual maintenance work. The actual maintenance process is simulated on a physical prototype. Owing to their dependence on a specific physical prototype, conventional RHM evaluations can neither find defects in product design in a timely manner nor ensure the safety of maintenance personnel. RHM personnel inevitably make mistakes in long-term RHM, and their negative emotions may affect the safety and stability of RHM. Thus, reasonably estimating the effects of the errors and psychological factors of the evaluator on RHM is required.
To solve the above problems, the following are considered in this study. The method of fuzzy synthetic evaluating has been applied in various fields. The evaluating index is often specific value number. However the factors of RHM and the indexes are all uncertain. On one hand, the scores the evaluating experts applied are all uncertain. Besides, the evaluating level is often uncertain. It is obviously unsuitable to evaluate the human factors using the method based on specific value number. So the interval method [ An ergonomic analysis is conducted by building a virtual maintenance environment and by introducing a virtual human model, thereby providing technical support for maintainability and maintenance analyses [ The human error probability (HEP) is the well-known parameter for describing human performance [
In this study, an integrated human factors analysis approach is proposed for RHM. An evaluation indicator system of human factors is established for human factors analysis on RHM. In the approach, indicators of accessibility, health safety, and fatigue are analyzed using virtual human simulation technologies. The human error factors in the maintenance process are analyzed using the HEP based on the AHP-SLIM. The psychological cognition level of maintenance personnel is determined with an expert scoring. The human factors for the RHM system are then evaluated using the interval method. With radiation as the application object, the human factors in the maintenance process are analyzed, and corresponding improvement suggestions are provided. The application results show that the approach can support the evaluation of the human factors in RHM.
The remainder of the paper is organized as follows. Section
According to the features of the human factors in RHM, an approach for integrated analysis of human factors in RHM is designed, as shown in Figure
An integrated human factors analysis approach for RHM.
An evaluation indicator system of human factors in RHM is established (shown in Table
Evaluation indicator system of human factors in RHM.
Criterion | Indicator |
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Accessibility | Visual accessibility |
Operation accessibility | |
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Health safety | Physical injuries |
Radiation injuries | |
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Comfort | Fatigue at work |
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Accuracy | HEP |
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Internal factors | Psychological cognition |
The following three methods are adopted to analyze the evaluation data in the proposed approach.
The interval method can effectively overcome the numerical uncertainty caused by fuzziness [
Numerical interval-based indicators.
Criterion | Indicator |
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Accessibility | Visual accessibility | Very poor | Poor | Medium | Good | Very good |
Operation accessibility | Very poor | Poor | Medium | Good | Very good | |
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Health safety | Physical injuries | Very serious | Serious | Medium | Small | Tiniest |
Radiation injuries (distance/m) | 0~0.2 | 0.2~0.4 | 0.4~0.6 | 0.6~0.8 | 0.8~1 | |
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Comfort | Fatigue at work (RULA value) | 7 | 5~6 | 3~4 | 2 | 1 |
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Correctness | HEP | >0.08 | 0.06~0.08 | 0.04~0.06 | 0.02~0.04 | <0.02 |
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Internal factors | Psychological cognition (expert scoring) | 0~1 | 1~2 | 2~3 | 3~4 | 4~5 |
Weighted coefficients for the evaluation indicators.
Grade | Class A | Class B | Class C | Class D | Class E |
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Attribute | Especially important | Very important | More important | Important | General |
Range | |
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The initially weighted interval value is as follows:
Fuzzy mathematics is adopted to process the indicators as follows:
The indicators are normalized as follows:
The weighted interval vector after the processing is
Criteria for evaluation grades.
Evaluation grades | Interval define | Description | |
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Class A | Excellent | |
The RHM scheme has the best safety performance. Equipment design and placed are highly humanized. Operator’s psychological quality is extremely high. It does not need to be improved. |
Class B | Good | |
The RHM scheme has better safety performance. Equipment design and placed are humanized. Operator’s psychological quality is good. It does not need to be improved. |
Class C | Medium | |
The RHM scheme has normal safety performance. Equipment design and placed are accepted. Operator’s psychological quality is normal. It needs to be a bit improved. |
Class D | Poor | |
The RHM scheme has poor safety performance. It needs to be improved. |
Class E | Very poor | |
The RHM scheme has very poor safety performance. It cannot be accepted. |
Figure
Sectional drawing of a radioactive equipment.
Teleoperation environment for the radioactive equipment.
In this study, the simulation-based human factor evaluation platform is developed on the Delmia software [
The simulation-based human factor evaluation platform.
Personnel parameter design for RHM.
Visual range of the operation staff.
According to the RHM scheme, an arm length of 18 cm is designed as the quantitative criterion for accessibility design. Both hands of the operation personnel can reach a 3D area, as shown in Figure
Accessibility coverage of the operation personnel.
Layout of the radiation area.
Simulation of the effects of the contact between both hands of the personnel and the handle.
RULA scoring rules.
Section | Score scope | Relation between score and color | |||||
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1 | 2 | 3 | 4 | 5 | 6 | ||
Upper arm | 1–6 | Green | Green | Yellow | Yellow | Red | Red |
Forearm | 1–3 | Green | Yellow | Red | |||
Wrist | 1–4 | Green | Yellow | Orange | Red | ||
Rotate wrist | 1-2 | Green | Red | ||||
Neck | 1–6 | Green | Green | Yellow | Yellow | Red | Red |
Breast | 1–6 | Green | Green | Yellow | Yellow | Red | Red |
Figure
RULA evaluation report of the RHM personnel.
SLIM is a human error quantification method based on expert scoring. The basic assumption is that HEP is determined by the comprehensive effects of PSF. Analysis process of HEP based on AHP-SLIM is shown in Figure
Analysis process of HEP based on AHP-SLIM.
Six typical human errors in the sample applications.
SN | Human errors | Detailed description of the errors |
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Missing bolts | The RHM personnel forgot to mount the bolts. |
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Torque exceeding the limited value | Excessive torque is applied when using the pneumatic wrench. |
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Parts assembled incorrectly | Other parts are assembled mistakenly. |
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Operations in wrong sequence | The operational sequence is reversed. |
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Tools used wrongly | Tools are incorrectly used. |
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Screws not tightened | Too small torque is applied when using the pneumatic wrench. |
KPSF that affect the errors of the RHM personnel.
Code | KPSF | Interpretation |
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KPSF1 | Experience and knowledge | With extensive knowledge and experience in RHM, the RHM personnel can avoid most of the errors. |
KPSF2 | Safety consciousness | The level of safety consciousness concerns the prudence and importance of RHM. |
KPSF3 | Working environment | The rational placement of tools significantly influences the errors of the RHM personnel. |
KPSF4 | Workload | Work difficulty or work posture that does not match the personnel’s ability may exert physiological and/or psychological pressure on the RHM personnel, thereby increasing HEP. |
Values of relative importance.
Value | Importance |
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1 | Two factors of the same importance |
3 | One of the two factors is not important to the other |
5 | One of the two factors is relatively important to the other |
7 | One of the two factors is very important to the other |
9 | One of the two factors is extremely important to the other |
2, 4, 6, 8 | Median value of two adjacent values |
Evaluation matrix for the evaluation factors.
Estimated value | KPSF1 | KPSF2 | KPSF3 | KPSF4 |
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KPSF1 | 1 | 2 | 2 | 5 |
KPSF2 | 1/2 | 1 | 1/3 | 4 |
KPSF3 | 1/2 | 3 | 1 | 5 |
KPSF4 | 1/5 | 1/4 | 1/5 | 1 |
① Calculated consistency index, CI, is as follows:
② Average random consistency index (RI) is as follows: according to the literature [
③ Weighted value of each factor is as follows:
④ Calculated consistency ratio (CR) of CI is as follows:
Evaluation matrix under the condition of
KPSF1 |
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1 | 2 | 3 | 2 | 4 | 1/2 |
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1/2 | 1 | 2 | 1 | 2 | 1 |
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1/3 | 1/2 | 1 | 1/2 | 3 | 1/2 |
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1/2 | 1 | 2 | 1 | 5 | 1 |
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1/4 | 1 | 2 | 1/5 | 1 | 1/2 |
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2 | 1 | 2 | 1 | 2 | 1 |
Evaluation matrix under the condition of
KPSF2 |
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1 | 2 | 3 | 2 | 3 | 1/3 |
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1/2 | 1 | 2 | 1 | 2 | 1 |
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1/3 | 1/2 | 1 | 1/2 | 3 | 1/2 |
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1/2 | 1 | 2 | 1 | 5 | 1 |
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1/3 | 1 | 2 | 1/5 | 1 | 1/2 |
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3 | 1 | 2 | 1 | 2 | 1 |
Evaluation matrix under the condition of
KPSF3 |
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1 | 3 | 2 | 2 | 3 | 1/3 |
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1/3 | 1 | 1 | 1/2 | 2 | 1 |
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1/2 | 1/2 | 1 | 1/2 | 4 | 1/2 |
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1/2 | 2 | 3 | 1 | 5 | 1 |
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1/3 | 1/3 | 1/4 | 1/5 | 1 | 1/2 |
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3 | 1 | 2 | 1 | 2 | 1 |
Evaluation matrix under the condition of
KPSF4 |
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1 | 1/3 | 2 | 1/2 | 3 | 1/2 |
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3 | 1 | 5 | 1 | 5 | 1 |
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1/2 | 1/2 | 1 | 1/2 | 4 | 1/3 |
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2 | 2 | 2 | 1 | 5 | 1 |
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1/3 | 1/5 | 1/4 | 1/5 | 1 | 1/3 |
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2 | 1 | 3 | 1 | 3 | 1 |
The calculation results are shown in Table
Calculation results of SLI.
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SLI1 = 0.234 |
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SLI2 = 0.162 |
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SLI3 = 0.115 |
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SLI4 = 0.208 |
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SLI5 = 0.060 |
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SLI6 = 0.224 |
Similarly, the values of SLI estimated based on the judgment results of the remaining four experts through the preceding calculation procedures are shown in Table
Values of SLI estimated based on the results of the five experts.
SLI of Expert 1 | SLI of Expert 2 | SLI of Expert 3 | SLI of Expert 4 | SLI of Expert 5 |
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0.234 | 0.230 | 0.227 | 0.236 | 0.225 |
0.162 | 0.124 | 0.142 | 0.153 | 0.135 |
0.115 | 0.108 | 0.105 | 0.104 | 0.162 |
0.208 | 0.221 | 0.211 | 0.117 | 0.207 |
0.060 | 0.103 | 0.072 | 0.095 | 0.083 |
0.224 | 0.225 | 0.230 | 0.302 | 0.241 |
Averages of SLI and HEP.
Number | Item | Average of SLI | Average of HEP | Sorted by error probability |
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Missing bolts | 0.230 | 0.0205 | 2 |
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Torque exceeding the limited value | 0.143 | 0.0078 | 4 |
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Parts assembled incorrectly | 0.119 | 0.0060 | 5 |
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Operations in wrong sequence | 0.193 | 0.0136 | 3 |
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Tools used wrongly | 0.083 | 0.0040 | 6 |
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Screws not tightened | 0.244 | 0.0240 | 1 |
According to the calculation results of SLI, Error
The HEP value of the entire RHM can be obtained by the sum of HEP:
As shown in Table
Internal factor evaluation in RHM mainly refers to evaluating the psychological cognition of the RHM personnel at work. In this study, the RHM personnel’s psychological cognition is quantitatively evaluated by expert scoring, as shown in Table
Psychological cognition scoring system.
Performance | Depressed and discontented, with feelings expressed by body language | Irritable, with feelings expressed by verbal language | Fidgety and depressed | Languid and fatigued | Energetic but actually fatigued |
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Scoring (points) | 1 | 2 | 3 | 4 | 5 |
Expert scoring results.
Expert | Expert 1 | Expert 2 | Expert 3 | Expert 4 | Expert 5 |
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Scoring (points) | 4 | 3 | 4 | 2 | 4 |
The quantized values of the seven indicators are summarized, with the vectors illustrated by the interval method as follows:
By expert scoring, we have the following initially weighted interval vectors:
The weighted interval vectors of the indicators after fuzzy normalization are
The values of the human factor analysis on RHM are as follows:
In contrast to Table
In this paper, an integrated human factors analysis approach is developed to evaluate human factors in the RHM. Compared with the conventional RHM evaluations approach, the proposed approach has the following advantages. Human factors evaluation in RHM based on interval method is introduced to solve the numerical uncertainties arising from the fuzziness in human factors evaluation. Human factors analysis based on simulation and virtual human is used to support human factors evaluation in the RHM design state. And it does not need a physical prototype. The human error factors in the maintenance process are analyzed using the HEP based on the AHP-SLIM. The AHP is used to check the consistency among the experts while the SLIM is used to convert the likelihood into HEPs.
In the approach, the evaluation indicator system of human factors in RHM is the key to the analysis of human factors in RHM. As there are many factors that affect the human factors in RHM, only the perfect evaluation indicator system can get closer to the results of the facts. Furthermore, we will improve the evaluation indicator system according to different application scenarios.
The authors declare that there is no conflict of interests regarding the publication of this paper.
The study was supported by the National Natural Science Foundation of China (Grant no. 71201026), the Natural Science Foundation of Guangdong (no. 2015A030310274, no. 2015A030310415, and no. 2015A030310315), the Project of Department of Education of Guangdong Province (no. 2013KJCX0179, no. 2014KTSCX184, and no. 2014KGJHZ014), the Development Program for Excellent Young Teachers in Higher Education Institutions of Guangdong Province (no. Yq2013156), the Dongguan Universities and Scientific Research Institutions Science and Technology Project (no. 2014106101007), and the Dongguan Social Science and Technology Development Project (no. 2013108101011).