scholarly journals On-Line Remaining Useful Life Estimation of Power Connectors Focused on Predictive Maintenance

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3739
Author(s):  
Jordi-Roger Riba ◽  
Álvaro Gómez-Pau ◽  
Jimmy Martínez ◽  
Manuel Moreno-Eguilaz
Keyword(s):  
Power Systems ◽  
Electrical Resistance ◽  
Voltage Drop ◽  
Moving Average ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Moving Average Model ◽  
On Line ◽  
Useful Life ◽  
Two Parameters

Connections are critical elements in power systems, exhibiting higher failure probability. Power connectors are considered secondary simple devices in power systems despite their key role, since a failure in one such element can lead to major issues. Thus, it is of vital interest to develop predictive maintenance approaches to minimize these issues. This paper proposes an on-line method to determine the remaining useful life (RUL) of power connectors. It is based on a simple and accurate model of the degradation with time of the electrical resistance of the connector, which only has two parameters, whose values are identified from on-line acquired data (voltage drop across the connector, electric current and temperature). The accuracy of the model presented in this paper is compared with the widely applied autoregressive integrated moving average model (ARIMA), showing enhanced performance. Next, a criterion to determine the RUL is proposed, which is based on the inflection point of the expression describing the electrical resistance degradation. This strategy allows determination of when the connector must be replaced, thus easing predictive maintenance tasks. Experimental results from seven connectors show the potential and viability of the suggested method, which can be applied to many other devices.

scholarly journals Time Series RUL Estimation of Medium Voltage Connectors to Ease Predictive Maintenance Plans

2020 ◽  
Vol 10 (24) ◽  
pp. 9041
Author(s):  
Álvaro Gómez-Pau ◽  
Jordi-Roger Riba ◽  
Manuel Moreno-Eguilaz
Keyword(s):  
Time Series ◽  
Electrical Resistance ◽  
Time Series Data ◽  
Critical Role ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Series Data ◽  
Economic Losses ◽  
Medium Voltage ◽  
On Line

The ageing process of medium voltage power connectors can lead to important power system faults. An on-line prediction of the remaining useful life (RUL) is a convenient strategy to prevent such failures, thus easing the application of predictive maintenance plans. The electrical resistance of the connector is the most widely used health indicator for condition monitoring and RUL prediction, even though its measurement is a challenging task because of its low value, which typically falls in the range of a few micro-ohms. At the present time, the RUL of power connectors is not estimated, since their electrical parameters are not monitored because medium voltage connectors are considered cheap and secondary devices in power systems, despite they play a critical role, so their failure can lead to important power flow interruptions with the consequent safety risks and economic losses. Therefore, there is an imperious need to develop on-line RUL prediction strategies. This paper develops an on-line method to solve this issue, by predicting the RUL of medium voltage connectors based on the degradation trajectory of the electrical resistance, which is characterized by analyzing the electrical resistance time series data by means of the autoregressive integrated moving average (ARIMA) method. The approach proposed in this paper allows applying predictive maintenance plans, since the RUL enables determining when the power connector must be replaced by a new one. Experimental results obtained from several connectors illustrate the feasibility and accuracy of the proposed approach for an on-line RUL prediction of power connectors.

scholarly journals Accurate Real Time On-Line Estimation of State-of-Health and Remaining Useful Life of Li ion Batteries

2020 ◽  
Vol 10 (21) ◽  
pp. 7836
Author(s):  
Cher Ming Tan ◽  
Preetpal Singh ◽  
Che Chen
Keyword(s):  
Lithium Ion Batteries ◽  
Real Time ◽  
Estimation Method ◽  
Lithium Ion ◽  
Threshold Level ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
State Of Health ◽  
On Line ◽  
Useful Life

Inaccurate state-of-health (SoH) estimation of battery can lead to over-discharge as the actual depth of discharge will be deeper, or a more-than-necessary number of charges as the calculated SoC will be underestimated, depending on whether the inaccuracy in the maximum stored charge is over or under estimated. Both can lead to increased degradation of a battery. Inaccurate SoH can also lead to the continuous use of battery below 80% actual SoH that could lead to catastrophic failures. Therefore, an accurate and rapid on-line SoH estimation method for lithium ion batteries, under different operating conditions such as varying ambient temperatures and discharge rates, is important. This work develops a method for this purpose, and the method combines the electrochemistry-based electrical model and semi-empirical capacity fading model on a discharge curve of a lithium-ion battery for the estimation of its maximum stored charge capacity, and thus its state of health. The method developed produces a close form that relates SoH with the number of charge-discharge cycles as well as operating temperatures and currents, and its inverse application allows us to estimate the remaining useful life of lithium ion batteries (LiB) for a given SoH threshold level. The estimation time is less than 5 s as the combined model is a closed-form model, and hence it is suitable for real time and on-line applications.

Automotive Battery Prognostics Using Dual Extended Kalman Filter

2009 ◽  
Author(s):  
Matteo Rubagotti ◽  
Simona Onori ◽  
Giorgio Rizzoni
Keyword(s):  
Experimental Data ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Remaining Useful Life ◽  
State Of Charge ◽  
Future Evolution ◽  
On Line ◽  
Automotive Batteries ◽  
Useful Life ◽  
Simulation Results

This paper proposes a strategy for estimating the remaining useful life of automotive batteries based on dual Extended Kalman Filter. A nonlinear model of the battery is exploited for the on-line estimation of the State of Charge, and this information is used to evaluate the actual capacity and predict its future evolution, from which an estimate of the remaining useful life is obtained with suitable margins of uncertainty. Simulation results using experimental data from lead-acid batteries show the effectiveness of the approach.

scholarly journals Analysis of Weibull and Poisson Distribution use in Medium Voltage Circuit Breakers RUL Assessment

2020 ◽  
pp. 1-6
Author(s):  
Dragan Stevanovic
Keyword(s):  
Poisson Distribution ◽  
Voltage Drop ◽  
Probability Distributions ◽  
Short Circuit ◽  
Remaining Useful Life ◽  
Life Assessment ◽  
Circuit Breakers ◽  
Medium Voltage ◽  
Distribution Calculation ◽  
Useful Life

In this paper, Weibull and Poisson distribution calculation are carried out with new data to conclude a conclusion are they suitable for circuit breakers remaining useful life assessment (RUL). Old data are covering a 10 years period consisting of measured voltage drop on CB contacts and number of tripped short circuit faults. In this paper, new data, from the last 3 years, would be used to make a comparison with old data and make conclusions have been probability distributions correctly chosen.

A Big Data implementation of the MANTIS reference architecture for predictive maintenance

2019 ◽  
Vol 233 (10) ◽  
pp. 1361-1375
Author(s):  
Felix Larrinaga ◽  
Javier Fernandez-Anakabe ◽  
Ekhi Zugasti ◽  
Iñaki Garitano ◽  
Urko Zurutuza ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Reference Architecture ◽  
Implementation Support ◽  
Physical Systems ◽  
Root Cause ◽  
Hadoop Distributed File System ◽  
Useful Life

This article presents the implementation of a reference architecture for cyber-physical systems to support condition-based maintenance of industrial assets. It also focuses on describing the data analysis approach to manage predictive maintenance of clutch-brake assets fleet over the previously defined MANTIS reference architecture. Proposals for both the architecture and data analysis implementation support working on Big Data scenarios, due to the usage of related technologies, such as Hadoop Distributed File System, Kafka or Apache Spark. The techniques are (1) root cause analysis powered by attribute-oriented induction clustering and (2) remaining useful life powered by time series forecasting. The work has been conducted in a real use case within the H2020 European project MANTIS.

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