The Effect of Material Composition on Dynamic Welding of Electrical Contacts

Mützel, Timo; Bender, Michael; Niederreuther, Ralf

doi:10.1109/holm.2013.6651432

Cited by 11 publications

(2 citation statements)

References 5 publications

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“…In particular, when testing EMR it is important not to induce uncharacteristic failure modes through poor choice of test-parameters. Hence, a popular approach to test only the contacts is a model switch i.e., a device which allows to precisely control the switching parameters [120], [121]. However, whilst yielding results applicable to the contact material, carefully tuning such replica to match the properties of an EMR under test is challenging [1].…”

Section: A Emr Life Cycle Experimentsmentioning

confidence: 99%

Prognostics for Electromagnetic Relays Using Deep Learning

et al. 2022

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Electromagnetic Relays (Electromagnetic Relay (EMR)s) are omnipresent in electrical systems, ranging from mass-produced consumer products to highly specialised, safety-critical industrial systems. Our detailed literature review focused on EMR reliability highlighting the methods used to estimate the State of Health or the Remaining Useful Life emphasises the limited analysis and understanding of expressive EMR degradation indicators, as well as accessibility and use of EMR life cycle data sets. Prioritising these open challenges, a deep learning pipeline is presented in a prognostic context termed Electromagnetic Relay Useful Actuation Pipeline (EMRUA). Leveraging the attributes of causal convolution, a Temporal Convolutional Network (TCN) based architecture integrates an arbitrary long sequence of multiple features to produce a remaining useful switching actuations forecast. These features are extracted from raw, high volume life cycle data sets, namely EMR switching data (Contact-Voltage, Contact-Current). Monte-Carlo Dropout is utilised to estimate uncertainty during inference. The TCN hyperparameter space, as well as various methods to select and analyse long sequences of multivariate time series data are investigated. Subsequently, our results demonstrate improvements using the developed statistical feature-set over traditional, time-based features, commonly found in literature. EMRUA achieves an average forecasting mean absolute percentage error of ±12 % over the course of the entire EMR life. INDEX TERMSElectromagnetic relay, prognostics, prognostics and health management, predictive maintenance, remaining useful life, artificial intelligence, deep learning, temporal convolutional networks, Monte-Carlo dropout. ABBREVIATIONS AT Arcing time. BT Bounce time. CAE Convolutional auto encoder. CC Coil current. CI Contact current. CNN Convolutional neural network. CR Contact resistance. CT Closing time. CV Contact voltage. DCR Dynamic contact resistance. DI Degradation indicator. EI Exponential indexing. The associate editor coordinating the review of this manuscript and approving it for publication was Sajid Ali . EMR Electromagnetic relay. EMRUA Electromagnetic relay useful actuation pipeline. EOL End of life. FC Fully connected layer. GI Growing-sequence indexing. LI Linear indexing. LSTM Long-short-term-memory network. MAE Mean absolute error. MAPE Mean absolute percentage error. MCD Monte-carlo dropout. MVTD Multivariate time series data. NN Neural network. OT Over-travel time.

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Section: A Emr Life Cycle Experimentsmentioning

confidence: 99%

Prognostics for Electromagnetic Relays Using Deep Learning

et al. 2022

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“…K. Hotta demonstrated that the increase of arc energy on the contact surface would increase the welding force, and the real welding area accounted for about 25% to 40% of the melting area [5]. T. Mutzel found that different base metal oxides and their total content are of significant influence for weld break forces [6]. R. Wanbin observed the substantial decrease and final disappearance of sliding distance accelerated the contact welding failure process [7].…”

Section: Introductionmentioning

confidence: 99%

Preliminary Study on Making Arc Characteristics of AgSnO2 Contact

Guo

Zhang

Wang

et al. 2019

PPT

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In order to analyze the making arc characteristics of AgSnO<sub>2</sub> contact, experiments were carried out with a self-developed experimental equipment. It was found that contact resistance had no obvious change with the increase of the number of experiments.In the later stage of the experiment, the contact bounces occurred during the contact closing process, which not only prolonged the making arc duration, but also increased making arc energy. When the contact was eroded to a certain extent by arc, making welding occurs.

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Effect of electric load characteristics on the arc-erosion behavior of Ag-4 wt.%TiB2-4 wt.%Ni electrical contact material

Wang

Liu

et al. 2021

Appl. Phys. A

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The Effect of Material Composition on Dynamic Welding of Electrical Contacts

Cited by 11 publications

References 5 publications

Prognostics for Electromagnetic Relays Using Deep Learning

Prognostics for Electromagnetic Relays Using Deep Learning

Preliminary Study on Making Arc Characteristics of AgSnO2 Contact

Effect of electric load characteristics on the arc-erosion behavior of Ag-4 wt.%TiB2-4 wt.%Ni electrical contact material

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