Summary of Time Series Analysis in Compressor-based Machines: a Survey, by Francesca Forbicini et al.
Time Series Analysis in Compressor-Based Machines: A Survey
by Francesca Forbicini, Nicolò Oreste Pinciroli Vago, Piero Fraternali
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper surveys recent research on multivariate time series analysis for compressor-based machines, such as refrigerators, HVAC systems, heat pumps, and chillers. The tasks examined include fault detection (FD), forecasting (FP), forecasting, and change point detection (CPD). These tasks aim to improve machine efficiency and longevity by minimizing downtime and maintenance costs while optimizing energy usage. The paper identifies approaches, compares algorithms, highlights gaps in current research, and discusses promising future directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we can use computers to analyze the data from machines like refrigerators and air conditioners. These machines are important for our daily lives, but they sometimes break down or change how they work. By studying patterns in the data, researchers hope to predict when these problems will happen and find ways to fix them quickly. The paper shows what different methods have been tried so far and where there is still more work to be done. |
Keywords
* Artificial intelligence * Time series
