Explainable AI for Industrial Ultrasonics

工业超声波的可解释人工智能

• 批准号: 10034679
• 金额: $ 17.53万
• 依托单位: SOUND MATHEMATICS LTD
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10034679
• 关键词: Explainable; AI; Industrial; Ultrasonics

Datasets necessary for detection and characterisation of defects in industrial structures are more and more often collected using phased arrays of ultrasonic transducers. Some NDT (Non-Destructive Testing) inspections utilising such arrays are already automated but interpretation of data they collect is not. Automation of data interpretation is desirable, because industry anticipates a severe shortage of suitably qualified and experienced personnel and because there is a pressure for both speeding the NDT inspections up and increasing their reliability.The approach best suited for utilising array data seems to be TFM (Total Focusing Method). This creates visual images of sections of inspected components. When they are contaminated by noise interpretation of such images is not straightforward. Research engineers began to explore the possibility of automating interpretation of NDT data by utilising machine learning techniques. At present, this approach has limited value: firstly, machine learning techniques rely on big data while there are no repositories of big NDT data; and secondly, conclusions reached by modern AIs are frequently unexplainable while safety critical industries, such as nuclear are unlikely to adopt interpretation tools of this nature. Sound Mathematics Ltd. has been working on an alternative solution, an application software that combines a signal processing algorithm based on a simple modification of a TFM with OpenCV image processing algorithms and a decision tree - an AI (Augmented Intelligence) module, which mimicks thought processes employed by human inspectors in writing inspection reports.Compared to machine learning algorithms, Decision Trees have two well-known advantages and one disadvantage: they need orders of magnitude fewer datasets for training, they produce explainable results, but developmental challenge they present is huge. To use AutoNDE as an example, the application relies on about 40 different parameters. It took our researchers years to zero-in on the set of parameters that appear to work well in a variety of configurations.If brought to successful conclusion, the proposed project would decrease operating costs and increase safety of nuclear plants, our initial target market. At a later stage the technology would be transferred to offshore wind turbines. The project is highly timely, in view of the current drive to reduce the world's dependence on oil and gas. Later still the technology can be applied to NDT of various pressure vessels, rails, steel plates used in shipbuilding industry, bolts _etc_. It would reduce the underlying operating costs and increase safety of these industries too.

工业结构中缺陷的检测和表征所需的数据集越来越多地使用超声换能器的相控阵来收集。一些使用这种阵列的NDT（无损检测）检查已经自动化，但对它们收集的数据的解释还没有自动化。自动化的数据解释是可取的，因为行业预期的严重短缺，适当的合格和有经验的人员，因为有一个压力，既加快无损检测和提高其可靠性。最适合利用阵列数据的方法似乎是TFM（总聚焦法）。这将创建被检查组件的部分的视觉图像。当它们被噪声污染时，对这些图像的解释并不简单。研究工程师开始探索通过利用机器学习技术自动解释NDT数据的可能性。目前，这种方法的价值有限：首先，机器学习技术依赖于大数据，而没有大的无损检测数据库;其次，现代人工智能得出的结论往往是无法解释的，而安全关键行业，如核能，不太可能采用这种性质的解释工具。Sound Mathematics Ltd.一直致力于开发一种替代解决方案，这是一种应用软件，该软件将基于TFM的简单修改的信号处理算法与OpenCV图像处理算法和决策树相结合-AI（增强智能）模块，它模仿人类检查员在编写检查报告时使用的思维过程。与机器学习算法相比，决策树有两个众所周知的优点和一个缺点：它们需要数量级更少的数据集进行训练，它们产生可解释的结果，但它们提出的发展挑战是巨大的。以AutoNDE为例，该应用程序依赖于大约40个不同的参数。我们的研究人员花了数年时间才确定了在各种配置下都能正常工作的参数集。如果成功完成，拟议的项目将降低运营成本，提高我们最初的目标市场核电站的安全性。在稍后阶段，该技术将转移到海上风力涡轮机。考虑到目前减少世界对石油和天然气依赖的努力，该项目非常及时。该技术还可应用于各种压力容器、钢轨、造船用钢板、螺栓等的无损检测。这将降低基本的运营成本，并提高这些行业的安全性。