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https://hdl.handle.net/10216/153047| Author(s): | Gabriel Copolecchia Carvalhal |
| Title: | Anomaly Detection on Multivariate Time Series from CNC Machining using Machine Learning techniques |
| Issue Date: | 2023-09-26 |
| Abstract: | The industry 4.0 paradigm paves the way that manufacturing factories develop their real-time monitoring capabilities. In this context, this study investigates machine learning applications in real-world data from CNC machining sensors in Jasil, Portugal, for an anomaly detection task as industrial quality control. In order to improve the availability of systems, reduce maintenance costs, increase operational performance, and support decision-making. |
| Description: | The research makes a significant contribution by applying a Machine Learning method capable of interpreting anomalous behaviors in unlabeled multivariable time series data from Jasil's CNC machines. The project primarily focuses on identifying machine cycles, where each interval corresponds to the manufacturing of a product, allowing for a better understanding of the temporal patterns and dependencies within the data. To enable more effective and in-depth analysis of anomalous behaviors within each cycle, feature extraction techniques are employed to capture relevant information from the dataset. This process helps identify features that hold higher representability, thereby enhancing the accuracy, precision and recall of the detection process. Furthermore, the research utilizes a range of Machine Learning techniques to identify anomalous points, harnessing the power of algorithms to detect patterns and outliers. By employing different Machine Learning techniques, the research aims to enhance the identification of anomalies, leveraging the strengths of each method and creating a more robust anomaly detection system for the industry. |
| Subject: | Outras ciências da engenharia e tecnologias Other engineering and technologies |
| Scientific areas: | Ciências da engenharia e tecnologias::Outras ciências da engenharia e tecnologias Engineering and technology::Other engineering and technologies |
| DOI: | 10.34626/9gdz-vr58 |
| TID identifier: | 203423518 |
| URI: | https://hdl.handle.net/10216/153047 |
| Document Type: | Dissertação |
| Rights: | embargoedAccess |
| Embargo End Date: | 2026-09-25 |
| Appears in Collections: | FEUP - Dissertação |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 644538.pdf Restricted Access | Anomaly Detection on Multivariate Time Series from CNC Machining using Machine Learning techniques | 4.13 MB | Adobe PDF | View/Open |
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