# Machine learning for anomaly detection and process phase classification to improve safety and maintenance activities

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/machine-learning-for-anomaly-detection-and-process-phase-classification-to/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Elena Quatrini,Francesco Costantino,Giulio Di Gravio,Riccardo Patriarca |
| Citations | 122 |
| DOI | 10.1016/j.jmsy.2020.05.013 |
| Fields | Computer Science,Decision Sciences,Engineering |
| Open Access | true |
| OA Status | green |
| OA URL | http://hdl.handle.net/11573/1413335 |
| OpenAlex ID | https://openalex.org/W3035318737 |
| Type | article |
| Year | 2020 |

## Paper authors

- [Francesco Costantino](https://scholariq.org/researchers/francesco-costantino/)

## Paper journal

- [Journal of Manufacturing Systems](https://scholariq.org/journals/journal-of-manufacturing-systems/)

## Paper primary topic

- [Fault Detection and Control Systems](https://scholariq.org/topics/fault-detection-and-control-systems/)

## Paper topics

- [Fault Detection and Control Systems](https://scholariq.org/topics/fault-detection-and-control-systems/)
- [Anomaly Detection Techniques and Applications](https://scholariq.org/topics/anomaly-detection-techniques-and-applications/)
- [Advanced Statistical Process Monitoring](https://scholariq.org/topics/advanced-statistical-process-monitoring/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.