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

arXiv: https://arxiv.org/abs/2405.08041 · HTML: https://mlsanity.com/paper/2405.08041/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 13 May 2024.

## Builds on (most cited first, 5 of 5)

- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [Multivariate Time-series Anomaly Detection via Graph Attention Network](https://mlsanity.com/paper/2009.02040/) (2020)
- [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://mlsanity.com/paper/2205.02302/) (2022)
- [Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process](https://mlsanity.com/paper/2110.10234/) (2021)
- [Fusing Physics-based and Deep Learning Models for Prognostics](https://mlsanity.com/paper/2003.00732/) (2020)

## Abstract (arXiv)

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.