基于人员和环境因素定量化的检测可靠性

doi:10.7527/S1000-6893.2023.28859

Abstract

Abstract:

The establishment of aircraft inspection intervals requires the support of detection reliability， but the evaluation of detection reliability is influenced by many factors during the detection process. Among them， human and environment have the characteristics of multi-dimensional indicators and difficulty in quantifying their levels， making it difficult to quantitatively model them. Based on the quantitative evaluation for the impact of two types of factors using HF factors， a detection reliability model considering the impact of human and environment was established to address this issue. Firstly， feature analysis was conducted on the HF factors and its mathematical model was proposed based on A400M detection data. At the same time， verification was carried out using detection test data that considered work experience and operational lighting. Next， the comprehensive quantification for the levels of two types of factors was carried out through fuzzy comprehensive evaluation. On this basis， a mapping relationship between the comprehensive quantification results and the HF factor model was established. Finally， an application analysis was conducted using visual inspection data of flat plate cracks， verifying the applicability and effectiveness of the model. The proposed detection reliability model has the potential to reduce experimental costs while ensuring the accuracy of the results.

Key words: non-destructive testing, reliability, human factor, environmental factor, HF factor model

CLC Number:

V267

Xiaofeng XUE, Miaoyan ZHAO, Qianyi DU, Yunwen FENG, Junling FAN, Ting JIAO. Detection reliability based on quantification of human and environmental factors[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(6): 228859-228859.

Figures/Tables 21

Table 1

Fig.1

Fig.2

Table 2

Detection data of optimal and A400M aircraft in situ scene

作业场景	POD公式	参数 $μ$	参数 $σ$
最佳	$P O D (a) = Φ (a - μ σ)$	0.169 9	0.261 9
A400M	$P O D (a) = Φ (a - μ σ)$	0.239 0	0.262 4

Table 2

Fig. 3

Fig.4

Fig.5

Fig.6

Table 3

Detection data in scenarios with 5 and 0 years of work experience

工作经验/年限	POD公式	参数 $μ$	参数 $σ$
5	$P O D (a) = Φ l n (a) - μ σ$	-1.447	0.431 87
0	$P O D (a) = Φ l n (a) - μ σ$	-1.429	0.477 21

Table 3

Fig.7

Table 4

Detection data in 1 000LUX and 100LUX sce⁃narios based on horizontal illumination

作业光照/LUX	POD公式	参数 $k$	参数 $w$
1 000	$P O D (a) = e π 3 l n (a) - k w 1 + e π 3 l n (a) - k w$	-1.808	1.039
100	$P O D (a) = e π 3 l n (a) - k w 1 + e π 3 l n (a) - k w$	-1.586	1.188

Table 4

Fig.8

Fig.9

Table 5

Detection data in laboratory， fuselage side， and fuselage bottom scenarios

检测场景	POD公式	参数 $m$	参数 $n$
实验室	$P O D (a) = 1 - 1 1 + a m n$	15.78	1.079
机身侧面		37.15	0.954
机身底面		83.03	1.079

Table 5

Fig.10

Fig.11

Table 6

Table 7

Expert evaluation data table for the member⁃ship level of impact indicators

影响指标	检测场景	评 $v 1$ 人数	评 $v 2$ 人数	评 $v 3$ 人数	评 $v 4$ 人数
作业环境舒适性	实验室	10	2	0	0
	侧面	6	5	1	0
	底面	2	3	6	1
工作情绪	实验室	11	1	0	0
	侧面	5	5	1	1
	底面	1	8	1	2
人员疲劳	实验室	10	2	0	0
	侧面	4	3	3	2
	底面	2	3	4	3
作业环境可达性	实验室	11	1	0	0
	侧面	5	3	3	1
	底面	0	3	3	6

Table 7

Fig.12

Fig.13

Fig.14

References 27

1	宋凯，张弛，晏晨辉，等. 基于双参数表征的航空发动机轮盘模型辅助涡流检测可靠性［J］. 航空学报， 2023， 44（13）： 227866.
	SONG K， ZHANG C， YAN C H， et al. Reliability of aero-engine wheel model assisted eddy current testing based on two-parameter representation［J］. Acta Aeronautica et Astronautica Sinica， 2023， 44（13）： 227866 （in Chinese）.
2	MOHSENI E， BOUKANI H H， FRANÇA D R， et al. A study of the automated eddy current detection of cracks in steel plates［J］. Journal of Nondestructive Evaluation， 2020， 39（1）： 1-12.
3	薛军. 民机维修大纲结构疲劳损伤评估方法研究［D］. 天津：中国民航大学， 2014.
	XUE J. Research on fatigue damage assessment method of civil aircraft maintenance outline structure［D］.Tianjin： Civil Aviation University of China， 2014 （in Chinese）.
4	邵传金. 复合材料结构的检查间隔确定和修理容限评估方法研究［D］. 南京：南京航空航天大学， 2018.
	SHAO C J. Study on inspection interval determination and repair tolerance evaluation method of composite structure［D］.Nanjing： Nanjing University of Aeronautics and Astronautics， 2018 （in Chinese）.
5	ROGERSON A. A Personal perspective on the early developments in inspection qualification and reliability assessment in the UK unclear industry ［C］∥ 18th World Conference on Nondestructive Testing， 2012.
6	ZHANG Z Y， VAN DER MEE V， GOLDING M， et al. Pitting corrosion resistance properties of super duplex stainless steel weld metals and influencing factors［J］. Welding in the World， 2019， 63（3）： 617-625.
7	BLANKSCHÄN M， KANZLER D， LIEBICH R. Visual testing： The influence of selected human factors on probability of detection［J］. Insight-Non-Destructive Testing and Condition Monitoring， 2023， 65（1）： 13-18.
8	MCGRATH B.PANI and the role of the written procedure［C］∥4th European-American Workshop on Reliability of NDE. Berlin： DGZFP， 2009.
9	熊华锋，李磊，沈薇，等. 复合材料结构目视检测影响因素研究［C］∥第17届全国复合材料学术会议（复合材料检测与测试技术分论坛）论文集. 2012.
	XIONG H F， LI L， SHEN W，et al. Research on the influencing factors of visual inspection of composite structures［C］∥Proceedings of the 17th National Academic Conference on Composite Materials （Sub Forum on Composite Material Testing and Testing Technology）， 2012 （in Chinese）.
10	LEE D， KWON K. Dynamic Bayesian network model for comprehensive risk analysis of fatigue-critical structural details［J］. Reliability Engineering & System Safety， 2023， 229： 108834.
11	BATO M R， HOR A， RAUTUREAU A， et al. Impact of human and environmental factors on the probability of detection during NDT control by eddy currents［J］. Measurement， 2019， 133： 222-232.
12	刘明萱. 航空发动机轮盘表面缺陷涡流检测及可靠性研究评价方法研究［D］. 南昌：南昌航空大学， 2021.
	LIU M X. Eddy current detection and reliability evaluation method of aero-engine disk surface defects［D］. Nanchang： Nanchang Hangkong University， 2021 （in Chinese）.
13	WALL M， BURCH S F， LILLEY J. Review of models and simulators for NDT reliability （POD）［J］. Insight-Non-Destructive Testing and Condition Monitoring， 2009， 51（11）： 612-619.
14	WALL M， BURCH S， LILLEY J. Human factors in POD modelling and use of trial data［J］. Insight-Non-Destructive Testing and Condition Monitoring， 2009， 51（10）： 553-561.
15	蒋韵尔. 民航维修目视检测人为因素研究［D］. 上海：上海交通大学， 2014.
	JIANG Y E. Study on human factors in visual inspection of civil aviation maintenance［D］. Shanghai： Shanghai Jiao Tong University， 2014 （in Chinese）.
16	张俊. 超声声场计算与检测可靠性研究［D］. 武汉：武汉大学， 2010.
	ZHANG J. Study on calculation and detection reliability of ultrasonic sound field［D］.Wuhan： Wuhan University， 2010 （in Chinese）.
17	DOMINGUEZ N， RODAT D， GUIBERT F， et al. POD evaluation using simulation： Progress and perspectives regarding human factors［C］∥19th World Conference on Non-Destructive Testing （WCNDT 2016）， 2016.
18	YUSA N， CHEN W X， HASHIZUME H. Demonstration of probability of detection taking consideration of both the length and the depth of a flaw explicitly［J］. NDT & E International， 2016， 81： 1-8.
19	胡卫朋. 目视检测技术在特种设备检验中的应用［J］. 无损检测， 2013， 35（8）： 70-72.
	HU W P. Application of visual testing techrdogy in special equipment inspection［J］. Nondestructive Testing Technologying， 2013， 35（8）： 70-72 （in Chinese）.
20	焦婷，宁宁，樊俊铃，等. 基于激光三维扫描的裂纹可视化表征方法［J］. 无损检测， 2022， 44（10）： 15-19.
	JIAO T， NING N， FAN J L， et al. Visual characterization method of cracks based on laser 3D scanning［J］. Nondestructive Testing Technologying， 2022， 44（10）： 15-19 （in Chinese）.
21	蒋韵尔，黄淑萍，吴奇，等. 目视检测损伤检出概率BP神经网络预测模型［J］. 计算机应用， 2014， 34（）： 172-175.
	JIANG Y E， HUANG S P， WU Q， et al. BP neural network prediction model of damage detection probability in visual inspection［J］. Journal of Computer Applications， 2014， 34（Sup 2）： 172-175 （in Chinese）.
22	HU L H， PAN X， DING S， et al. A quantitative input for evaluating human error of visual neglection：Prediction of Operator’s detection time spent on perceiving critical visual signal［J］. Reliability Engineering & System Safety， 2022， 225： 108582.
23	王桂萍，贾亚洲，周广文. 基于模糊可拓层次分析法的数控机床绿色度评价方法及应用［J］. 机械工程学报， 2010， 46（3）： 141-147.
	WANG G P， JIA Y Z， ZHOU G W. Evaluation method and application of CNC machine tool’s green degree based on fuzzy-EAHP［J］. Journal of Mechanical Engineering， 2010， 46（3）： 141-147 （in Chinese）.
24	崔建国，傅康毅，陈希成，等. 基于灰色模糊与层次分析的多属性飞机维修决策方法［J］. 航空学报， 2014， 35（2）： 478-486.
	CUI J G， FU K Y， CHEN X C， et al. Multiple attribute maintenance decision making of aircraft based on grey-fuzziness and analytical hierarchy process［J］. Acta Aeronautica et Astronautica Sinica， 2014， 35（2）： 478-486 （in Chinese）.
25	KEPRATE A， RATNAYAKE R M C. Probability of detection as a metric for quantifying NDE capability： The state of the art［J］. Journal of Pipeline Syatems Engineering and Practice， 2015， 14（3）： 199-209.
26	CHEN G R， GUO Y J， KATAGIRI T， et al. Multivariate probability of detection （POD） analysis considering the defect location for long-range， non-destructive pipe inspection using electromagnetic guided wave testing［J］. NDT & E International， 2021， 124： 102539.
27	RENTALA V K， MYLAVARAPU P， GAUTAM J P. Issues in estimating probability of detection of NDT techniques-A model assisted approach［J］. Ultrasonics， 2018， 87： 59-70.

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Detection reliability based on quantification of human and environmental factors

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序号	指标	序号	指标
1	人员疲劳	6	作业温度
2	工作情绪	7	监督管理
3	人员专业素质	8	人员态度
4	作业光照	9	工作经验
5	环境舒适性

影响指标	舒适性	工作情绪	人员疲劳	可达性
舒适性	1	1	1/4	1/4
工作情绪	1	1	1/4	1/4
人员疲劳	4	4	1	1
可达性	4	4	1	1