# Time Series RUL Estimation of Medium Voltage Connectors to Ease Predictive Maintenance Plans
## 1 Introduction
In [15], the RUL of microswitches was estimated by applying different methods, including Bayesian updating, expectation maximization, and strong tracking filtering [15]. In [16], the RUL of socket connectors was determined by analyzing the change in the resistance. ADTs are often applied to determine long-term effects of expected stress levels [17] by investing a shorter time. Many authors predict the RUL from the analysis of experimental data acquired from ADTs [12; 18; 19] to predict the long-term behavior of the analyzed devices. Although ADTs can generate reliable data, they are costly in terms of invested time, hours spent by technician, energy consumption and consumed materials. In addition, the obtained data are often specific of the tested sample, so they suffer from the lack of generalizability. RUL prediction is mainly based on four approaches, i.e., model-based, data-driven, fusion-based, and hybrid models. According to [1; 20], data-based RUL approaches usually provide superior on-line predictive performance than model-based approaches, although the accuracy of such approach can be enhanced by applying a fusion strategy based on combining different data-driven algorithms. Hybrid strategies combine model-based and data-driven algorithms, thus taking advantage of the benefits of both approaches. Model-based approaches or white-box models are based on describing the physics of the analyzed system and its failure modes using a set of mathematical equations, so this approach does not require historical performance data. Data-driven or black-box approaches progressively learn the behavior of the studied system directly from the collected data. This approach assumes that system data presents a reasonably deterministic statistical behavior of the system, unless it operates under fault conditions. Since data-driven approaches methods rely on collected data, they can predict the behavior of the system in the near future and especially at the end of its useful life due to the amount of data acquired [20]. Data-driven approaches include methods based on time-series, statistical, and artificial intelligence. It is possible to predict the degradation of power connectors by analyzing the degradation trajectory, which can be effectively applied to determine the RUL. It requires identifying the most influencing degradation factors and their influence in the prediction process. Although the analysis of the degradation trajectory is gaining attention, there is still little research in this area [21], and thus, a precise RUL prediction from the degradation trajectory is a challenging task. This paper develops a quite simple method based on an on-line acquisition of the voltage drop, electric current, and temperature of power connectors, so that these data are used to determine the degradation trajectory of the electrical resistance, which is the baseline to determine the RUL. The RUL is inferred by applying a fast and simple approach based on the autoregressive integrated moving average (ARIMA) method [22], which is based on actual and past values of the electrical resistance of the connectors. This paper contributes in different ways. First, it deals with the RUL of power connectors, an area requiring more research works. Second, the method proposed in this work has superior capabilities with respect to other methods found in the technical literature, since it is simple, fast, and easy to apply, so that the acquired data can be processed in low-power inexpensive microcontrollers that in a near future will be integrated into the power connectors for an on-line RUL prediction. Third, this approach is fully aligned with digital substations, smart grids, and Internet of Things (IoT), where predictive maintenance, RUL, and fault diagnosis are hot topics, and nowadays, power connectors are still far from these trends. Fourth, the approach proposed in this paper avoids the application of accelerated degradation tests (ADTs), which are costly and often lack generalizability.