Time Series Analysis in Compressor-Based Machines: A Survey

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 able to detect and predict faults, identify behavioural shifts and forecast the operational status of machines and of their components. The focus of this paper is to survey the recent research on such tasks as Fault Detection, Fault Prediction, Forecasting and Change Point Detection applied to multivariate time series characterizing the operations of compressor-based machines. Specifically, Fault Detection detects and diagnoses faults, Fault Prediction predicts such occurrences, forecasting anticipates the future value of characteristic variables of machines and Change Point Detection 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 above-mentioned tasks, 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.