A Statistical Framework for Improved Automatic Flaw Detection in Nondestructive Evaluation Images

Nondestructive evaluation (NDE) techniques are widely used to detect flaws in critical components of systems like aircraft engines, nuclear power plants, and oil pipelines to prevent catastrophic events. Many modern NDE systems generate image data. In some applications, an experienced inspector performs the tedious task of visually examining every image to provide accurate conclusions about the existence of flaws. This approach is labor-intensive and can cause misses due to operator ennui. Automated evaluation methods seek to eliminate human-factors variability and improve throughput. Simple methods based on peak amplitude in an image are sometimes employed and a trained-operator-controlled refinement that uses a dynamic threshold based on signal-to-noise ratio (SNR) has also been implemented. We develop an automated and optimized detection procedure that mimics these operations. The primary goal of our methodology is to reduce the number of images requiring expert visual evaluation by filtering out images that are overwhelmingly definitive on the existence or absence of a flaw. We use an appropriate model for the observed values of the SNR-detection criterion to estimate the probability of detection. Our methodology outperforms current methods in terms of its ability to detect flaws. Supplementary materials for this article are available online.

无损检测（Nondestructive Evaluation，NDE）技术被广泛用于检测航空发动机、核电站、输油管道等关键系统组件中的缺陷，以防范灾难性事故的发生。诸多现代无损检测系统可生成图像数据。在部分应用场景中，经验丰富的检测人员需逐一目视检视所有图像，以精准判定缺陷是否存在，此类工作繁重枯燥。该方法属于劳动密集型方案，且因检测人员的倦怠情绪可能引发漏检情况。自动化检测方案旨在消除人为因素导致的结果偏差，并提升检测通量。部分场景会采用基于图像峰值幅度的简易检测方法，同时也已落地由经培训的操作人员管控、基于信噪比（signal-to-noise ratio，SNR）的动态阈值优化流程。本研究开发了一种模拟上述操作流程的自动化优化检测程序。本方法的核心目标是，通过过滤掉可明确判定是否存在缺陷的图像，减少需经专家目视评估的图像数量。本研究针对信噪比检测准则的观测值采用适配模型，以估算缺陷检出概率。在缺陷检出性能方面，本方法优于现有检测手段。本文的补充材料可在线获取。