Integrated vehicle health management: An approach to dealing with lifetime prediction considerations on relays

Wileman, Andrew; Perinpanayagam, Suresh

doi:10.1016/j.microrel.2015.06.013

Cited by 8 publications

(7 citation statements)

References 8 publications

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“…Electrical, contact related failures prevail, such as elevated CR. Making or breaking-contact related failures are the predominant failure modes in EMR applications [10], [55]- [57]. Though one can distinguish failure modes by type, multiple of the failure mechanisms interact, as the following Section demonstrates.…”

Section: A Failure Modesmentioning

confidence: 99%

“…[11] combine a physical-model of erosion related contact degradation with CR measurements obtained from aviation EMR accelerated life cycle experiments using Kalman-Filtering. However, as recognised by the authors in [55] (also compare Appendix E), the feasibility of CR as the sole measure for the advancing EMR degradation is highly depended on the application [6], [95]. A regression model using Grey-System theory is presented in combination with a CR EOL-threshold.…”

Section: B Aviation and Aerospace Emrmentioning

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: A Failure Modesmentioning

confidence: 99%

Section: B Aviation and Aerospace Emrmentioning

confidence: 99%

Prognostics for Electromagnetic Relays Using Deep Learning

et al. 2022

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“…Arcing is the main factor causing the micro-switches to fail [28]. Whenever the switch contacts are separated, the arc will be generated, and the contact voltage will continue to rise until failure [29].…”

Section: The Establishment Of the Test Rigmentioning

confidence: 99%

A Stochastic Deterioration Process Based Approach for Micro Switches Remaining Useful Life Estimation

Zhang

Shao

Chang³

et al. 2019

Applied Sciences

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Real-time prediction of remaining useful life (RUL) is one of the most essential works inprognostics and health management (PHM) of the micro-switches. In this paper, a lineardegradation model based on an inverse Kalman filter to imitate the stochastic deterioration processis proposed. First, Bayesian posterior estimation and expectation maximization (EM) algorithm areused to estimate the stochastic parameters. Second, an inverse Kalman filter is delivered to solvethe errors in the initial parameters. In order to improve the accuracy of estimating nonlinear data,the strong tracking filtering (STF) method is used on the basis of Bayesian updating Third, theeffectiveness of the proposed approach is validated on an experimental data relating tomicro-switches for the rail vehicle. Additionally, it proposes another two methods for comparisonto illustrate the effectiveness of the method with an inverse Kalman filter in this paper. Inconclusion, a linear degradation model based on an inverse Kalman filter shall deal with errors inRUL estimation of the micro-switches excellently.

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“…In short, RUL prediction is an important step to keep equipment and systems operating reliably. By reviewing previous research, the life prediction methods for relays can be summarized as regression analysis [7], grey system model [8], and neural network [9]. A life prediction method based on ARMA and wavelet transform model has been proposed for aerospace electromagnetic relays with good performance [7].…”

Section: Introductionmentioning

confidence: 99%

“…A life prediction method based on ARMA and wavelet transform model has been proposed for aerospace electromagnetic relays with good performance [7]. A. J. Wileman, et al use grey system model to predict relays' lifetime [8]. These methods can perform well on time series which are smoothed and straightforward, and acquire high prediction accuracies.…”

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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Integrated vehicle health management: An approach to dealing with lifetime prediction considerations on relays

Cited by 8 publications

References 8 publications

Prognostics for Electromagnetic Relays Using Deep Learning

Prognostics for Electromagnetic Relays Using Deep Learning

A Stochastic Deterioration Process Based Approach for Micro Switches Remaining Useful Life Estimation

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

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