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.

基于机器学习的预测性诊断和健康监测工具为制造商提供了新的机会，以风险优化的方式运营和维护设备，并在其生命周期内更可持续地利用。然而，在大多数工业环境中，数据往往数量有限，质量也可能不一致，这对开发和运行可靠的机器学习模型至关重要。为了弥补这一实践中的差距，成功工业化的预测性诊断和健康监测工具依赖于引入领域专业知识作为先验，以实现足够准确的预测，并提高其可解释性。因此，发展数据驱动的预测性诊断和健康监测工具的关键挑战之一在于将维护人员、开发人员和服务工程师的经验和过程知识转化为数据结构，该结构不仅必须捕捉到专业知识的多样性和变异性，还必须使这些知识能够让各种数据驱动算法访问。这导致数据模型严重依赖于特定应用程序和开发团队旨在检测或预测的故障模式，缺乏标准化方法限制了开发的可扩展性和可转移性，并阻碍了标准数据管理和 MLOps 工具的利用，增加了开发团队的负担。DeepFMEA 从故障模式与影响分析（FMEA）中汲取灵感，以其结构化的方法分析任何技术系统和由此产生的标准化数据模型，同时考虑到捕捉过程和维护专业知识的关键方面，以对领域专家直观，并使得所得信息可以作为先验引入机器学习算法。

DeepFMEA：融合过程专业知识和数据驱动 PHM 的可扩展框架

DeepFMEA -- A Scalable Framework Harmonizing Process Expertise and  Data-Driven PHM

Failure mode and effects analysis (FMEA) is a systematic approach to identify
and analyse potential failures and their effects in a system or process. The
FMEA approach, however, requires domain experts to manually analyse the FMEA
model to derive risk-reducing actions that should be applied. In this paper, we
provide a formal framework to allow for automatic planning and acting in FMEA
models. More specifically, we cast the FMEA model into a Markov decision
process which can then be solved by existing solvers. We show that the FMEA
approach can not only be used to support medical experts during the modelling
process but also to automatically derive optimal therapies for the treatment of
patients.

通过将 FMEA 模型转化为马尔科夫决策过程，我们提供了一个正式的框架来实现自动计划和操作，不仅可以支持医学专家在建模过程中应用，还可以自动确定对患者治疗的最佳治疗方案。