New forecasting method of closing time for aerospace relay in storage accelerated degradation testing

Wang, Zhaobin; Sai, Fu; Shang, Shang; Chen, Wenhua

doi:10.1109/icrms.2016.8050118

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…The magnetic field produces the force for the armature. The coil current is essentially constant, so the magnetic force remains unchanged [21]. It can be assumed that relay failure is not caused by the magnetic force.…”

Section: Degradation Modelmentioning

confidence: 99%

A Reliability Assessment Method for High Speed Train Electromagnetic Relays

et al. 2018

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Abstract:The condition of electromagnetic relays is crucial to the safe and normal operation of the high-speed train (HST). At present, there are no proper testing and assessment methods for the condition of electromagnetic relays. Electromagnetic relays experience aging and failure over their service time, which causes many potential security problems. To solve these problems, the failure mechanisms of electromagnetic relays are analyzed. The Fick diffusion theory and the Larson-Miller method are used to deduce the degradation models for contact resistance and closing time. Based on these models, an approach to calculate relay life is proposed. To verify the effectiveness of the proposed method, a test platform was designed and built. The accelerated life test was carried out by using Siemens 3TH electromagnetic relays (3TH4244-OLF4, Siemens, Suzhou, China). Contact resistance and closing time were measured using a test platform. The least squares fitting was used to calculate the residual sum of squares and the unknown parameters in the degradation models with the test data. The optimal degradation model was chosen according to the fitting results. Relay life at four temperature stress levels was predicted by the optimal degradation model and failure threshold of the relays. The result indicates that the life of the electromagnetic relays at ambient temperature is 11,874 days and prediction error is about 5%, which provides important guidance for the maintenance of electromagnetic relays.

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Section: Degradation Modelmentioning

confidence: 99%

A Reliability Assessment Method for High Speed Train Electromagnetic Relays

et al. 2018

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“…The proposed, combined Grey-NN is superior to using either Grey-Theory or NN. A similar approach, employing Grey-NN for aerospace EMR in storage is presented in [106]. However, instead of CR, CT is the predicted DI.…”

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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“…As we know, time series are usually nonlinear, which affect the prediction accuracy of some linear prediction models. In order to address this problem, the back propagation artificial neural networks (BPNN) model has been used in the prediction model, which performs better on nonlinear series [10]. However, traditional single prediction methods can hardly reveal the nonlinearity and non-stationarity properties of time series.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Method for Life Prediction of Railway Relays Based on Multi-Layer Decomposition and RBFNN

Sun

Cao

Zhou³

et al. 2019

IEEE Access

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The railway relay plays an important role in railway systems. Its reliability has a significant effect on the safety of passengers and train operation, which can be reflected using degradation parameters. In this paper, a novel hybrid method based on the multi-layer decomposition and the radial basis function neural network (RBFNN) is proposed for life prediction of railway relays. As the degradation parameter series are usually nonlinear and non-stationary, it is vital to develop an essential method to preprocess the degradation series. In order to improve the prediction accuracy, a multi-layer decomposition method is developed first for data pre-processing, which blends complete ensemble empirical mode decomposition (CEEMD) and an improved variational mode decomposition (IVMD) with a stopping criterion for determining the decomposition modes number. It is noted that IVMD is then used to decompose the highfrequency intrinsic mode functions (IMFs) obtained using CEEMD to improve the prediction accuracy. Furthermore, RBFNN is applied to all the components for prediction. And the prediction results of all the components are reconstructed as the predicted degradation series. Finally, the effectiveness and robustness of the proposed novel hybrid prediction method are verified on one-step prediction and multi-step prediction by comparing other commonly used prediction methods. The experimental results indicate that the proposed hybrid prediction method performs best on the complex degradation parameters of safety relays. INDEX TERMS Railway relays, life prediction, complete ensemble empirical mode decomposition (CEEMD), improved variational mode decomposition (IVMD), radial basis function neural network (RBFNN).

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New forecasting method of closing time for aerospace relay in storage accelerated degradation testing

Cited by 4 publications

References 4 publications

A Reliability Assessment Method for High Speed Train Electromagnetic Relays

A Reliability Assessment Method for High Speed Train Electromagnetic Relays

Prognostics for Electromagnetic Relays Using Deep Learning

A Hybrid Method for Life Prediction of Railway Relays Based on Multi-Layer Decomposition and RBFNN

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