The influence of switching arcs on contact resistance of Ag/SnO2 materials

Mützel, Timo; Niederreuther, Ralf

doi:10.1109/holm.2015.7355092

Cited by 3 publications

(2 citation statements)

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“…However, as pure silver has a high tendency for contact welding, typically silver-composite materials are used. Internal oxidation of these materials can reduce the weld strength, increase the material hardness, reduce contact sticking and material loss [3], [4]. However, an operating temperature increase can be observed relating to a decrease of the electric conductivity as the material's oxidisation rate and its resistance against arcing rises [139].…”

Section: Appendix E Cr Fluctuations In Silver-plated Contactsmentioning

confidence: 99%

“…However, an operating temperature increase can be observed relating to a decrease of the electric conductivity as the material's oxidisation rate and its resistance against arcing rises [139]. Manufactured using internal-oxidisation, EMR applications typically utilise these silver-metal oxides in low power applications of up to 20 A [3]. Up to 15 % oxides are found in these types of contacts [65].…”

Section: Appendix E Cr Fluctuations In Silver-plated Contactsmentioning

confidence: 99%

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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: Appendix E Cr Fluctuations In Silver-plated Contactsmentioning

confidence: 99%