Simulation and diagnosis of degradation failure on electromagnetic relay

Ye, Xuerong; Fu, Rao; Wu, Yang; Lin, Yigang; Zhai, Guofu

doi:10.1109/phm.2017.8079301

Cited by 3 publications

(5 citation statements)

References 3 publications

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“…Long-term use impacts the coil resistance. Deposition of evaporated contact material particles on the coil wire or combustion of the insulation material due to excessive heat reduce the coil's insulation resistance [60]. Further, poorly welded coil wires might be a failure root-cause.…”

Section: ) Coil and Mechanical Parts A: Coilmentioning

confidence: 99%

“…The trajectory of time-based reference DIs varies in respect to the experienced failure mechanism and failure mode, the design of the EMR and the operational environment [6], [58], [60]. Thus, as Section III demonstrates, no generally valid non-intrusive DI has been established throughout the body of research.…”

Section: A: Non-intrusive Dismentioning

confidence: 99%

“…The developed fluctuation coefficient improves the performance for these failure mechanisms, though the reported accuracy for contamination related failures remains low. A more recent work highlights two key challenges for a broad application of PHM to EMR [60]: (1) the lack of life cycle data as there are currently only very few deployed online monitoring health management solutions for EMR; (2) the uncertainty associated with the DIs. To address former one, simulation of EMR life cycle data is proposed.…”

Section: Table 2 An Overview Of Classical Emr-dismentioning

confidence: 99%

“…To account for the variance in the derived DIs, the authors propose the use of the Mahalabonis-Distance (cf. [60]) to classify failure modes. For RUL prediction, a NN is employed, achieving 84 % forecasting accuracy.…”

Section: Railway Emrmentioning

confidence: 99%

“…From above analysis various challenges in monitoring and maintaining EMR using data-driven techniques have become evident. Classical DIs do not generalise across different EMR designs, contact types, contact material, operational environments or loading scenarios as each applications results in distinctly different degradation trajectories e.g., [7], [9], [56], [59], [60], [100]. In addition, different failure modes cause classical DIs to exhibit incoherent, opposing trends often even changing within the same batch of components e.g., [6], [60].…”

Section: H Comparison Of Reviewed Literature and Identified Challengesmentioning

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: ) Coil and Mechanical Parts A: Coilmentioning

confidence: 99%