# Time Series Analysis in Compressor-Based Machines: A Survey

arXiv: https://arxiv.org/abs/2402.17802 · HTML: https://mlsanity.com/paper/2402.17802/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 216 references that are in the map.
- First posted on arXiv 27 Feb 2024.

## Builds on (most cited first, 11 of 11)

- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Multimodal Learning with Transformers: A Survey](https://mlsanity.com/paper/2206.06488/) (2022)
- [Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress](https://mlsanity.com/paper/2009.13807/) (2020)
- [Towards artificial general intelligence via a multimodal foundation model](https://mlsanity.com/paper/2110.14378/) (2021)
- [Interpretable Scientific Discovery with Symbolic Regression: A Review](https://mlsanity.com/paper/2211.10873/) (2022)
- [Forecasting: theory and practice](https://mlsanity.com/paper/2012.03854/) (2020)
- [Toward a Foundation Model for Time Series Data](https://mlsanity.com/paper/2310.03916/) (2023)
- [Symbolic Regression as Feature Engineering Method for Machine and Deep Learning Regression Tasks](https://mlsanity.com/paper/2311.06028/) (2023)
- [A Change Point Detection Integrated Remaining Useful Life Estimation Model under Variable Operating Conditions](https://mlsanity.com/paper/2401.04351/) (2024)
- [DeepGraviLens: a Multi-Modal Architecture for Classifying Gravitational Lensing Data](https://mlsanity.com/paper/2205.00701/) (2022)

## Abstract (arXiv)

In both industrial and residential contexts, compressor-based machines, such as refrigerators, HVAC systems, heat pumps and chillers, are essential to fulfil production and consumers' needs. The diffusion of sensors and IoT connectivity supports the development of monitoring systems that can detect and predict faults, identify behavioural shifts and forecast the operational status of machines and their components. The focus of this paper is to survey the recent research on such tasks as FD, FP, Forecasting and CPD applied to multivariate time series characterizing the operations of compressor-based machines. These tasks play a critical role in improving the efficiency and longevity of machines by minimizing downtime and maintenance costs and improving the energy efficiency. Specifically, FD detects and diagnoses faults, FP predicts such occurrences, forecasting anticipates the future value of characteristic variables of machines and CPD identifies significant variations in the behaviour of the appliances, such as a change in the working regime. We identify and classify the approaches to the tasks mentioned above, compare the algorithms employed, highlight the gaps in the current status of the art and discuss the most promising future research directions in the field.