# Anomaly Detection in Industrial Machinery using IoT Devices and Machine Learning: a Systematic Mapping

arXiv: https://arxiv.org/abs/2307.15807 · HTML: https://mlsanity.com/paper/2307.15807/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 138 references that are in the map.
- First posted on arXiv 28 Jul 2023.

## Builds on (most cited first, 8 of 8)

- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery](https://mlsanity.com/paper/2102.11848/) (2021)
- [IoT Anomaly Detection Methods and Applications: A Survey](https://mlsanity.com/paper/2207.09092/) (2022)
- [Real-time ultra-low power ECG anomaly detection using an event-driven neuromorphic processor](https://mlsanity.com/paper/1911.05521/) (2019)
- [OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection](https://mlsanity.com/paper/2104.00528/) (2021)
- [ADEPOS: Anomaly Detection based Power Saving for Predictive Maintenance using Edge Computing](https://mlsanity.com/paper/1811.00873/) (2018)
- [A general anomaly detection framework for fleet-based condition monitoring of machines](https://mlsanity.com/paper/1912.12941/) (2019)
- [Predicting Bearings' Degradation Stages for Predictive Maintenance in the Pharmaceutical Industry](https://mlsanity.com/paper/2203.03259/) (2022)

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

- [Evaluating Anomaly Detectors for Simulated Highly Imbalanced Industrial Classification Problems](https://mlsanity.com/paper/2601.00005/) (2025)
- [Online Continual Learning for Anomaly Detection in IoT under Data Distribution Shifts](https://mlsanity.com/paper/2603.07507/) (2026)

## Abstract (arXiv)

Anomaly detection is critical in the smart industry for preventing equipment failure, reducing downtime, and improving safety. Internet of Things (IoT) has enabled the collection of large volumes of data from industrial machinery, providing a rich source of information for Anomaly Detection. However, the volume and complexity of data generated by the Internet of Things ecosystems make it difficult for humans to detect anomalies manually. Machine learning (ML) algorithms can automate anomaly detection in industrial machinery by analyzing generated data. Besides, each technique has specific strengths and weaknesses based on the data nature and its corresponding systems. However, the current systematic mapping studies on Anomaly Detection primarily focus on addressing network and cybersecurity-related problems, with limited attention given to the industrial sector. Additionally, these studies do not cover the challenges involved in using ML for Anomaly Detection in industrial machinery within the context of the IoT ecosystems. This paper presents a systematic mapping study on Anomaly Detection for industrial machinery using IoT devices and ML algorithms to address this gap. The study comprehensively evaluates 84 relevant studies spanning from 2016 to 2023, providing an extensive review of Anomaly Detection research. Our findings identify the most commonly used algorithms, preprocessing techniques, and sensor types. Additionally, this review identifies application areas and points to future challenges and research opportunities.