Accelerated storage degradation testing and failure mechanisms of aerospace electromagnetic relay

Wang, Zhaobin; Shang, Shang; Wang, Jiawei; Huang, Zhoulin; Sai, Fu

doi:10.17531/ein.2017.4.6

Cited by 18 publications

(13 citation statements)

References 10 publications

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“…The degradation of EMR storage, in particular the degradation of the contacts during storage, has been subject to an evolving field of research over the past decade [87], [103]- [108] and [92], [109]. In [103], the authors subject aerospace EMRs to accelerated degradation testing under elevated temperature conditions.…”

Section: G Storage Of Emrmentioning

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: G Storage Of Emrmentioning

confidence: 99%

Prognostics for Electromagnetic Relays Using Deep Learning

et al. 2022

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“…This situation may also lead to a shortening of the relays’ maintenance time or, in extreme cases, to their complete destruction. Moreover, long-term exposure to higher temperature may also lead to a relay’s degradation, for example, its contact resistance and opening and closing times [16,17]. The corrosion film results in an increase in contact resistance and thus a decline in the contact performance.…”

Section: State Of the Artmentioning

confidence: 99%

Change in Electric Contact Resistance of Low-Voltage Relays Affected by Fault Current

2019

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Contact resistance is an important maintenance parameter for electromagnetic switches, including low-voltage relays. The flow of significant current through electric contacts may influence the contact surface and thus the value of the electric contact resistance (ECR). The change in ECR is influenced not only by the value of current but also by the current phase. Therefore, the impact of the switching short-circuit current’s phase on the ECR was analyzed in this paper. Significant changes in the resistance after each switching cycle were observed. The ECR decreased significantly after each make operation, and a correlation with current amplitude, total let-through energy, and short-circuit time was not observed.

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“…Generally speaking, the performance indicators of the contact materials include contact resistance, resistance to fusion welding, corrosion resistance, hardness, strength and other related parameters [19]. Qualified contact materials should have good electrical and thermal conductivity, and should also have good mechanical, chemical, and arc-resistant properties as well [18]. In the working process of the contacts of the switch apparatus, there are complex mechanical, electrical, thermal and arc energy conversions, so the traditional analysis method cannot effectively analyze the failure mechanism of low-voltage electrical appliances [10].…”

Section: Introductionmentioning

confidence: 99%

Performance degradation comparisons and failure mechanism of silver metal oxide contact materials in relays application by simulation

Wang¹,

Li²,

Shang³

et al. 2020

Eksploatacja i Niezawodność – Maintenance and Reliability

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To evaluate the electrical contact behaviors of silver metal oxide contact materials in relays application more accurately, and to guide the selection of contact materials, the test device and testing method for simulating electrical contact performance in relays application were analyzed in this paper. The electrical contact simulation test system was designed and developed, which can easily simulate contact materials. The contact resistance, static force and rebound energy degradation parameters of AgSnO 2 , AgCdO and AgNi contact materials under the same load conditions were obtained through experimental research, the contact resistance and arcing energy degradation parameters of AgSnO 2 under different opening distances were acquired at the same time. The result indicated that accurate data are received by the electrical contact simulation testing method. Finally, based on the test data, the degradation performance of three selected test materials was tested, and the failure mechanism of AgSnO 2 materials was analyzed.

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Accelerated storage degradation testing and failure mechanisms of aerospace electromagnetic relay

Cited by 18 publications

References 10 publications

Prognostics for Electromagnetic Relays Using Deep Learning

Prognostics for Electromagnetic Relays Using Deep Learning

Change in Electric Contact Resistance of Low-Voltage Relays Affected by Fault Current

Performance degradation comparisons and failure mechanism of silver metal oxide contact materials in relays application by simulation

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