Summary of Accurate and Fast Anomaly Detection in Industrial Processes and Iot Environments, by Simone Tonini (1) et al.
Accurate and fast anomaly detection in industrial processes and IoT environments
by Simone Tonini, Andrea Vandin, Francesca Chiaromonte, Daniele Licari, Fernando Barsacchi
First submitted to arxiv on: 27 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Applications (stat.AP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel semi-supervised procedure for anomaly detection in industrial and IoT environments called SAnD (Simple Anomaly Detection). This 5-step approach combines well-known statistical tools to identify anomalies and their causes. The procedure tackles technical challenges such as high multicollinearity, unknown distributions, and short-lived noise by smoothing filters, variance inflation factors, the Mahalanobis distance, threshold selection algorithms, and feature importance techniques. SAnD is shown to be effective in a case study with an industrial partner and outperforms existing approaches in both anomaly detection and runtime. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SAnD is a new way to find unusual patterns in data from machines and devices. It’s like a detective tool that uses math to figure out what’s normal and what’s not. The method takes five steps to get the job done, using techniques we already know are helpful for finding anomalies. This helps solve problems that happen when data is messy or has unknown patterns. We tested SAnD on real-world data from a company and it worked well. |
Keywords
» Artificial intelligence » Anomaly detection » Semi supervised
