# Time Series RUL Estimation of Medium Voltage Connectors to Ease Predictive Maintenance Plans
## 1 Introduction  
Power connectors are widely used in transmission and distribution grids. Although power connectors are simple elements, they are often placed in critical links, thus playing a key role for the reliable, stable, and long-term operation of power systems. However, like in many other power devices, their performance tends to degrade progressively during the operating condition [1], which may result in power system failures, sometimes with catastrophic and costly effects [2]. The expected lifetime of power connectors under continuous operation is several years, but the occurrence of premature faults can shorten this period [3]. Therefore, there is an imperious need to develop strategies to analyze the degradation process of power connectors, while predicting their RUL, i.e., the estimated operating time before the connector must be replaced. Remaining Useful Life (RUL's) accurate prediction enables effective maintenance strategies to be applied by anticipating when connectors will be replaced, thereby minimizing the risk of premature failure and the associated unwanted effects [4]. This is an issue for power utilities and system operators, as they go to great lengths to ensure a reliable, uninterrupted, and safe power delivery to their customers [5]. Medium voltage connectors are predominantly made of aluminum or copper [6], usually of compressed type, and designed to guarantee steady and reliable electrical connections between two bus bars or conductors with reduced voltage drop and power loss [7; 8]. It is a recognized fact that the electrical resistance of the electrical connectors plays a key role on the correct behavior of the connectors [9]. The electrical resistance has two main components, i.e., the bulk and the contact resistance. The bulk resistance is determined by the geometry of the connector and the electrical conductivity of its constitutive materials, whereas the contact resistance depends on several factors including the state, the roughness, and the pressure of the contact interfaces as well as the presence of debris and dirt. The degradation of the connector is reflected in an increase in its electrical resistance, which tends to increase the working temperature. It overheads the connector, which, in turn, increases the electrical resistance, thus shortening the expected life [9]. Different causes such as poor installation or the daily peak and off-peak temperature cycles, which generate contraction and expansion patterns, tend to loosen the contact, thus increasing the contact resistance. When the electrical resistance surpasses a limit value, the connector must be replaced to avoid possible hot spots and failures. Electrical resistance is the most critical parameter to determine the health status of power connectors, which tends to increase over time [10], since the different degradation mechanisms tend to produce negative effects on contact resistance, thus affecting negatively the electrical and thermal performances of the connectors. Therefore, the electrical resistance can be effectively used to predict the RUL of such devices. Although RUL strategies are much needed, the information found in the technical literature about RUL prediction methods for power connectors is scarce, in particular, when considering realistic operating conditions. The joint effect of temperature and vibrations on the resistance of automotive connectors was evaluated in [10], whereas in [11], the fatigue lifespan of microconnectors was studied. The effects of vibrations in the RUL of aviation connectors were evaluated in [12] by applying a particle filtering-based method and accelerated degradation tests (ADTs). ADTs were also applied in [13] to determine the failure rate and reliability of electrical connectors when subjected to temperature and particulate contamination stresses. In [14], the lifetime distribution of circular electrical connectors was approximated by a parametric Weibull distribution, its parameters being identified from degradation data.