Recent digital advances have popularized predictive maintenance (PMx),
offering enhanced efficiency, automation, accuracy, cost savings, and
independence in maintenance. Yet, it continues to face numerous limitations
such as poor explainability, sample inefficiency of data-driven methods,
complexity of physics-based methods, and limited generalizability and
scalability of knowledge-based methods. This paper proposes leveraging Digital
Twins (DTs) to address these challenges and enable automated PMx adoption at
larger scales. While we argue that DTs have this transformative potential, they
have not yet reached the level of maturity needed to bridge these gaps in a
standardized way. Without a standard definition for such evolution, this
transformation lacks a solid foundation upon which to base its development.
This paper provides a requirement-based roadmap supporting standardized PMx
automation using DT technologies. A systematic approach comprising two primary
stages is presented. First, we methodically identify the Informational
Requirements (IRs) and Functional Requirements (FRs) for PMx, which serve as a
foundation from which any unified framework must emerge. Our approach to
defining and using IRs and FRs to form the backbone of any PMx DT is supported
by the track record of IRs and FRs being successfully used as blueprints in
other areas, such as for product development within the software industry.
Second, we conduct a thorough literature review spanning fields to determine
the ways in which these IRs and FRs are currently being used within DTs,
enabling us to point to the specific areas where further research is warranted
to support the progress and maturation of requirement-based PMx DTs.

这篇论文提出利用数字孪生解决预测性维护（PMx）面临的挑战，并促进大规模的自动化 PMx 应用，为此提供了一个基于需求的路线图，包括信息需求和功能需求的系统方法，并进行了文献综述以确定进一步的研究方向。

基于需求的数字孪生技术标准化预测性维护自动化路径规划的最新综述与综合

State-of-the-Art Review and Synthesis: A Requirement-based Roadmap for  Standardized Predictive Maintenance Automation Using Digital Twin  Technologies

This work focuses on the novel problem setting of generating graphs
conditioned on a description of the graph's functional requirements in a
downstream task. We pose the problem as a text-to-text generation problem and
focus on the approach of fine-tuning a pretrained large language model (LLM) to
generate graphs. We propose an inductive bias which incorporates information
about the structure of the graph into the LLM's generation process by
incorporating message passing layers into an LLM's architecture. To evaluate
our proposed method, we design a novel set of experiments using publicly
available and widely studied molecule and knowledge graph data sets. Results
suggest our proposed approach generates graphs which more closely meet the
requested functional requirements, outperforming baselines developed on similar
tasks by a statistically significant margin.

本研究基于图的功能要求生成的问题设置进行了研究，并提出了一种在预训练语言模型的基础上微调以生成图的方法，该方法引入了关于图结构的归纳偏置，通过将消息传递层整合到语言模型的架构中。通过在公开可用和广泛研究的分子和知识图数据集上进行实验，结果表明我们的方法生成的图与所需功能要求更为契合，并且在相似任务的基准实验中显著优于其他方法。

形式随功能：基于功能要求的文本到文本条件图生成

Form follows Function: Text-to-Text Conditional Graph Generation based  on Functional Requirements

The Directorate General for Parliamentary Research Services of the European
Parliament has prepared a report to the Members of the European Parliament
where they enumerate seven main risks of Artificial Intelligence (AI) in
medicine and healthcare: patient harm due to AI errors, misuse of medical AI
tools, bias in AI and the perpetuation of existing inequities, lack of
transparency, privacy and security issues, gaps in accountability, and
obstacles in implementation.
In this study, we propose fourteen functional requirements that AI systems
may implement to reduce the risks associated with their medical purpose: AI
passport, User management, Regulation check, Academic use only disclaimer, data
quality assessment, Clinicians double check, Continuous performance evaluation,
Audit trail, Continuous usability test, Review of retrospective/simulated
cases, Bias check, eXplainable AI, Encryption and use of field-tested
libraries, and Semantic interoperability.
Our intention here is to provide specific high-level specifications of
technical solutions to ensure continuous good performance and use of AI systems
to benefit patients in compliance with the future EU regulatory framework.

欧洲议会议会研究服务总局准备了一份报告，列举了医疗和健康领域人工智能的七个主要风险：由于人工智能错误导致患者受伤、医疗人工智能工具的误用、人工智能的偏见和现有不平等的延续、缺乏透明度、隐私和安全问题、问责的漏洞以及实施中的障碍。该研究提出了十四个功能要求，可以降低与医疗目的相关的风险，并保证遵守未来的欧盟监管框架，供人工智能系统的实施。