DeepFMEA——一个融合流程专业知识与数据驱动PHM的可扩展框架
DeepFMEA -- A Scalable Framework Harmonizing Process Expertise and Data-Driven PHM
AI总结：
针对工业场景数据有限且质量不均导致ML驱动PHM开发受限的问题，提出DeepFMEA框架，借鉴FMEA结构化方法构建标准化数据模型，融合领域专业知识作为ML先验，提升PHM工具的可扩展性与可迁移性。
摘要：
基于机器学习（ML）的预测与健康监测（PHM）工具为制造商提供了新的契机，使其能够以风险优化的方式运行和维护设备，并在设备全生命周期内更可持续地利用设备。然而，在大多数工业场景中，数据的数量往往有限，且质量可能参差不齐——这两点对于开发和运行可靠的ML模型都至关重要。为了在实践中弥补这一差距，成功实现工业化应用的PHM工具依赖于将领域专业知识作为先验知识引入，以实现足够准确的预测，同时增强模型的可解释性。\n 因此，开发数据驱动的PHM工具时的一个关键挑战在于，将维护人员、开发人员和服务工程师的经验与流程知识转化为一种数据结构。这种结构不仅必须捕捉专业知识的多样性和可变性，还必须让各类数据驱动算法都能获取这些知识。这导致数据模型高度针对特定应用以及开发团队旨在检测或预测的故障模式进行定制。标准化方法的缺失限制了开发成果向新故障模式的可扩展性、向新应用的可迁移性，还阻碍了标准数据管理和MLOps工具的使用，加重了开发团队的负担。\n DeepFMEA的灵感来源于故障模式与影响分析（FMEA）——一种用于分析任意技术系统的结构化方法及由此产生的标准化数据模型，同时它还兼顾了对捕捉流程与维护专业知识至关重要的特性，既能让领域专家直观理解，又能将所得信息作为先验知识引入ML算法。
英文摘要：
Machine Learning (ML) based prognostics and health monitoring (PHM) tools provide new opportunities for manufacturers to operate and maintain their equipment in a risk-optimized manner and utilize it more sustainably along its lifecycle. Yet, in most industrial settings, data is often limited in quantity, and its quality can be inconsistent - both critical for developing and operating reliable ML models. To bridge this gap in practice, successfully industrialized PHM tools rely on the introduction of domain expertise as a prior, to enable sufficiently accurate predictions, while enhancing their interpretability. Thus, a key challenge while developing data-driven PHM tools involves translating the experience and process knowledge of maintenance personnel, development, and service engineers into a data structure. This structure must not only capture the diversity and variability of the expertise but also render this knowledge accessible for various data-driven algorithms. This results in data models that are heavily tailored towards a specific application and the failure modes the development team aims to detect or predict. The lack of a standardized approach limits developments' extensibility to new failure modes, their transferability to new applications, and it inhibits the utilization of standard data management and MLOps tools, increasing the burden on the development team. DeepFMEA draws inspiration from the Failure Mode and Effects Analysis (FMEA) in its structured approach to the analysis of any technical system and the resulting standardized data model, while considering aspects that are crucial to capturing process and maintenance expertise in a way that is both intuitive to domain experts and the resulting information can be introduced as priors to ML algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
