Remaining storage life prediction for an electromagnetic relay by a particle filtering-based method

Yong, Zhao; Zio, Enrico; Fu, Guicui

doi:10.1016/j.microrel.2017.03.026

Cited by 24 publications

(7 citation statements)

References 25 publications

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“…Alternatively, radioactive tracers can support understanding material transfer among switching, arcing contacts [1]. Further analysis of contact surface at different stages using Energy Dispersive X-Ray (EDX) and Scanning Electron Microscopy (SEM) have been used [54], [92]. In [93] the arc discharge is optically monitored.…”

Section: B: Intrusive Dismentioning

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: B: Intrusive Dismentioning

confidence: 99%

“…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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“…The contact pressure was increased and contact surface and other methods selected to reduce the stable contact resistance. Zhao et al 11 proposed a residual storage life-prediction method of an electromagnetic relay and verified the validity and accuracy of the model. Fu 12 applied fault tree analysis to the fault of a railway signal relay, summarized the main fault modes based on the actual situation, and established a virtual prototype model of a relay to obtain its effectiveness.…”

Section: Introductionmentioning

confidence: 95%

Contact pressure of high-voltage DC power relay change and life prediction and structure optimization

Chiu

Zheng

et al. 2021

Advances in Mechanical Engineering

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To effectively predict the life of high voltage dc power relay, the authors design the structure of the relay to extending its life. The contact failure mechanism of relay products is analyzed by observing the appearance of several contact failures. The electromagnetic-thermal field coupling model is established and the state of heat flow and distribution of temperature increase between electrical and thermal coupling contacts are analyzed through simulations and experiments. An adaptive neural network control system is established to conduct sample training, obtain the best stiffness coefficient of the main spring, and optimize the design of the main spring structure composed of composite springs. The action voltage, release voltage, contact resistance, contact pressure, action time, and release time as characteristic parameters, and the relative error, correlation degree, variance ratio, and small error probability of each characteristic parameter, are calculated. The precision grade index of contact resistance and action time is defined as level 1. The two characteristic parameters are selected as the prediction variables to establish the life-prediction model based on gray theory. The relative error between the predicted and test life is 5.31%–5.6%, indicating that the life-prediction model has high accuracy. The test results show that the combined main spring structure can extend the life of a relay by 18.15%.

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“…The wavelet transform is used to preprocess data, which can improve the prediction accuracy. A method based on particle filter to predict the remaining storage lifetime of electromagnetic relays was proposed to be effective [17].…”

Section: Introductionmentioning

confidence: 99%

Electric Vehicle Relay Lifetime Prediction Model Using the Improving Fireworks Algorithm–Grey Neural Network Model

Pang

Tseng

et al. 2020

Applied Sciences

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The relay reliability has an impact on the reliability of the entire electric vehicle system. This paper contributes to propose the improving fireworks algorithm optimizing the grey neural network model to predict the relay lifetime. This paper shows how the mutation operation and mapping operation in the fireworks algorithm are used to improve the convergence ability and running speed; the convergence performance and running speed of improved fireworks algorithm are tested with standard test function and compared with fireworks algorithm; and the grey neural network model–improved fireworks algorithm is used to predict the relay life and compared with grey model, grey neural network, and grey neural network model–fireworks algorithm. The results show that the convergence accuracy of the improved fireworks algorithm is better than the fireworks algorithm. The running time of improved fireworks algorithm is the shortest; the improved fireworks algorithm–grey neural network model has the best prediction effect and the root mean square error value is 6.75% smaller than the fireworks algorithm–grey neural network model.

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Remaining storage life prediction for an electromagnetic relay by a particle filtering-based method

Cited by 24 publications

References 25 publications

Prognostics for Electromagnetic Relays Using Deep Learning

Prognostics for Electromagnetic Relays Using Deep Learning

Contact pressure of high-voltage DC power relay change and life prediction and structure optimization

Electric Vehicle Relay Lifetime Prediction Model Using the Improving Fireworks Algorithm–Grey Neural Network Model

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