# Machine Learning Approaches for Diagnostics and Prognostics of Industrial Systems Using Open Source Data from PHM Data Challenges: A Review

arXiv: https://arxiv.org/abs/2312.16810 · HTML: https://mlsanity.com/paper/2312.16810/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 142 references that are in the map.
- First posted on arXiv 28 Dec 2023.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks](https://mlsanity.com/paper/1711.10485/) (2017)
- [The many Shapley values for model explanation](https://mlsanity.com/paper/1908.08474/) (2019)
- [ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels](https://mlsanity.com/paper/1910.13051/) (2019)
- [Generating Images from Captions with Attention](https://mlsanity.com/paper/1511.02793/) (2015)
- [DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data](https://mlsanity.com/paper/2105.02340/) (2021)
- [Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering](https://mlsanity.com/paper/2305.11541/) (2023)
- [A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework](https://mlsanity.com/paper/2204.03719/) (2022)
- [A Unified Industrial Large Knowledge Model Framework in Industry 4.0 and Smart Manufacturing](https://mlsanity.com/paper/2312.14428/) (2023)
- [ChatGPT-Like Large-Scale Foundation Models for Prognostics and Health Management: A Survey and Roadmaps](https://mlsanity.com/paper/2305.06472/) (2023)
- [A stacked deep convolutional neural network to predict the remaining useful life of a turbofan engine](https://mlsanity.com/paper/2111.12689/) (2021)
- [Multi-modal Machine Learning for Vehicle Rating Predictions Using Image, Text, and Parametric Data](https://mlsanity.com/paper/2305.15218/) (2023)
- [On the Soundness of XAI in Prognostics and Health Management (PHM)](https://mlsanity.com/paper/2303.05517/) (2023)
- [Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management](https://mlsanity.com/paper/2110.04256/) (2021)
- [Fault Prognosis of Turbofan Engines: Eventual Failure Prediction and Remaining Useful Life Estimation](https://mlsanity.com/paper/2303.12982/) (2023)
- [An ensemble of convolution-based methods for fault detection using vibration signals](https://mlsanity.com/paper/2305.05532/) (2023)
- [Neural Network and Particle Filtering: A Hybrid Framework for Crack Propagation Prediction](https://mlsanity.com/paper/2004.13556/) (2020)
- [1-D Residual Convolutional Neural Network coupled with Data Augmentation and Regularization for the ICPHM 2023 Data Challenge](https://mlsanity.com/paper/2304.07305/) (2023)

## Built on by (most cited first, 2 of 2)

- [Data Issues in Industrial AI System: A Meta-Review and Research Strategy](https://mlsanity.com/paper/2406.15784/) (2024)
- [Engineering Artificial Intelligence: Framework, Challenges, and Future Direction](https://mlsanity.com/paper/2504.02269/) (2025)

## Abstract (arXiv)

In the field of Prognostics and Health Management (PHM), recent years have witnessed a significant surge in the application of machine learning (ML). Despite this growth, the field grapples with a lack of unified guidelines and systematic approaches for effectively implementing these ML techniques and comprehensive analysis regarding industrial open-source data across varied scenarios. To address these gaps, this paper provides a comprehensive review of ML approaches for diagnostics and prognostics of industrial systems using open-source datasets from PHM Data Challenge Competitions held between 2018 and 2023 by PHM Society and IEEE Reliability Society and summarizes a unified ML framework. This review systematically categorizes and scrutinizes the problems, challenges, methodologies, and advancements demonstrated in these competitions, highlighting the evolving role of both conventional machine learning and deep learning in tackling complex industrial tasks related to detection, diagnosis, assessment, and prognosis. Moreover, this paper delves into the common challenges in PHM data challenge competitions by emphasizing data-related and model-related issues and evaluating the limitations of these competitions. The potential solutions to address these challenges are also summarized. Finally, we identify key themes and potential directions for future research, providing opportunities and prospects for next-generation ML-PHM development in PHM domain.