Time series analysis in compressor-based machines: a survey

Abstract In both industrial and residential contexts, compressor-based machines, such as refrigerators, heat, ventilation, and air conditioning systems, heat pumps, and chillers, are essential to fulfil production and consumers’ needs. The diffusion of sensors and internet of things connectivity support 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 fault detection (FD), fault prediction (FP), forecasting, and change point detection (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 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 state of the art, and discuss the most promising future research directions in the field.