# An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery

arXiv: https://arxiv.org/abs/2102.11848 · HTML: https://mlsanity.com/paper/2102.11848/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 67 references that are in the map.
- First posted on arXiv 23 Feb 2021.

## Builds on (most cited first, 9 of 9)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Techniques for Interpretable Machine Learning](https://mlsanity.com/paper/1808.00033/) (2018)
- [PyOD: A Python Toolbox for Scalable Outlier Detection](https://mlsanity.com/paper/1901.01588/) (2019)
- [WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis](https://mlsanity.com/paper/1911.07925/) (2019)
- [Pre-Processing-Free Gear Fault Diagnosis Using Small Datasets with Deep Convolutional Neural Network-Based Transfer Learning](https://mlsanity.com/paper/1710.08904/) (2017)
- [Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature Importance](https://mlsanity.com/paper/2007.11117/) (2020)
- [Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python](https://mlsanity.com/paper/2006.15261/) (2020)
- [A general anomaly detection framework for fleet-based condition monitoring of machines](https://mlsanity.com/paper/1912.12941/) (2019)

## Built on by (most cited first, 22 of 22)

- [Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities](https://mlsanity.com/paper/2401.07871/) (2024)
- [Explainable Artificial Intelligence (XAI) for Internet of Things: A Survey](https://mlsanity.com/paper/2206.04800/) (2022)
- [Fault Diagnosis using eXplainable AI: a Transfer Learning-based Approach for Rotating Machinery exploiting Augmented Synthetic Data](https://mlsanity.com/paper/2210.02974/) (2022)
- [Enhancing Interpretability and Generalizability in Extended Isolation Forests](https://mlsanity.com/paper/2310.05468/) (2023)
- [Explainable Predictive Maintenance](https://mlsanity.com/paper/2306.05120/) (2023)
- [PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies](https://mlsanity.com/paper/2401.09489/) (2024)
- [Scalable Concept Extraction in Industry 4.0](https://mlsanity.com/paper/2306.03551/) (2023)
- [Anomaly Detection in Industrial Machinery using IoT Devices and Machine Learning: a Systematic Mapping](https://mlsanity.com/paper/2307.15807/) (2023)
- [AcME-AD: Accelerated Model Explanations for Anomaly Detection](https://mlsanity.com/paper/2403.01245/) (2024)
- [Enabling Efficient and Flexible Interpretability of Data-driven Anomaly Detection in Industrial Processes with AcME-AD](https://mlsanity.com/paper/2404.18525/) (2024)
- [Fault Detection in Induction Motors using Functional Dimensionality Reduction Methods](https://mlsanity.com/paper/2306.09365/) (2023)
- [Generative Pre-Training of Time-Series Data for Unsupervised Fault Detection in Semiconductor Manufacturing](https://mlsanity.com/paper/2309.11427/) (2023)
- [Explainable Artificial Intelligence Techniques for Accurate Fault Detection and Diagnosis: A Review](https://mlsanity.com/paper/2404.11597/) (2024)
- [Classifier-Free Diffusion-Based Weakly-Supervised Approach for Health Indicator Derivation in Rotating Machines: Advancing Early Fault Detection and Condition Monitoring](https://mlsanity.com/paper/2409.01676/) (2024)
- [ExMo: Explainable AI Model using Inverse Frequency Decision Rules](https://mlsanity.com/paper/2205.10045/) (2022)
- [Explanation Method for Anomaly Detection on Mixed Numerical and Categorical Spaces](https://mlsanity.com/paper/2209.04173/) (2022)
- [A new transformation for embedded convolutional neural network approach toward real-time servo motor overload fault-detection](https://mlsanity.com/paper/2304.04005/) (2023)
- [Identifying contributors to supply chain outcomes in a multi-echelon setting: a decentralised approach](https://mlsanity.com/paper/2307.12157/) (2023)
- [Towards Transparent and Efficient Anomaly Detection in Industrial Processes through ExIFFI](https://mlsanity.com/paper/2405.01158/) (2024)
- [ShaTS: A Shapley-based Explainability Method for Time Series Artificial Intelligence Models applied to Anomaly Detection in Industrial Internet of Things](https://mlsanity.com/paper/2506.01450/) (2025)
- [Explainable Condition Monitoring via Probabilistic Anomaly Detection Applied to Helicopter Transmissions](https://mlsanity.com/paper/2603.08130/) (2026)
- [An explainable hybrid deep learning-enabled intelligent fault detection and diagnosis approach for automotive software systems validation](https://mlsanity.com/paper/2603.08165/) (2026)

## Abstract (arXiv)

The monitoring of rotating machinery is an essential task in today's production processes. Currently, several machine learning and deep learning-based modules have achieved excellent results in fault detection and diagnosis. Nevertheless, to further increase user adoption and diffusion of such technologies, users and human experts must be provided with explanations and insights by the modules. Another issue is related, in most cases, with the unavailability of labeled historical data that makes the use of supervised models unfeasible. Therefore, a new approach for fault detection and diagnosis in rotating machinery is here proposed. The methodology consists of three parts: feature extraction, fault detection and fault diagnosis. In the first part, the vibration features in the time and frequency domains are extracted. Secondly, in the fault detection, the presence of fault is verified in an unsupervised manner based on anomaly detection algorithms. The modularity of the methodology allows different algorithms to be implemented. Finally, in fault diagnosis, Shapley Additive Explanations (SHAP), a technique to interpret black-box models, is used. Through the feature importance ranking obtained by the model explainability, the fault diagnosis is performed. Two tools for diagnosis are proposed, namely: unsupervised classification and root cause analysis. The effectiveness of the proposed approach is shown on three datasets containing different mechanical faults in rotating machinery. The study also presents a comparison between models used in machine learning explainability: SHAP and Local Depth-based Feature Importance for the Isolation Forest (Local- DIFFI). Lastly, an analysis of several state-of-art anomaly detection algorithms in rotating machinery is included.