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.

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

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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