Time Series Analysis in Compressor-Based Machines: A Survey

TL;DR

This survey reviews recent research on fault detection, prediction, forecasting, and change point detection in multivariate time series data from compressor-based machines, emphasizing methods to enhance efficiency and maintenance.

Contribution

It provides a comprehensive classification and comparison of algorithms for monitoring compressor-based machines, highlighting gaps and future research directions.

Findings

Various algorithms are used for fault detection and prediction.

Significant gaps exist in current methodologies.

Future research should focus on integrating multiple tasks.

Abstract

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.