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

Pang, Xuelian; Li, Zhuo; Tseng, Ming‐Lang; Liu, Kaihua; Tan, Kim Hua; Li, Hengyi

doi:10.3390/app10061940

Cited by 13 publications

(7 citation statements)

References 28 publications

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“…At the same time, the weight matrix w ih and who are initialized with random numbers. Second, the sample x and expected vector d were input, and the output vector ho and the actual output value yo of the hidden layer were calculated with (2)-( 5) [16].…”

Section: E Bp Neural Network Algorithmmentioning

confidence: 99%

Big Data’s Analysis and Prediction Method of Art Education Based on the BP Neural Network

Zhu

2022

Security and Communication Networks

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The traditional art education analysis and prediction are mainly based on the evaluation of human supervisors, and the evaluation results given by different evaluators vary greatly. The traditional analysis and prediction is difficult to reflect the objectivity and fairness of art education evaluation. This paper proposes a new idea of using the BP neural network model to analyze and forecast art education to improve the objectivity and impartiality of art education evaluation by big data. The work first constructed the art education evaluation index system covering the content of art education comprehensively and extensively. Then the BP neural network of art education analysis and prediction model was established. According to the content of art education evaluation system, the BP neural network’s input vector contains 30 evaluation indexes that affect art education, and its output vector is the art education evaluation results. The BP neural network with the structure of 30 × 10 × 1 was trained using the collected data. Finally, the work verified the scientificity of the evaluation model. The results of empirical analysis show that the established art education evaluation index system is reasonable and can reflect the artistic level of students, and the model of BP neural network has reliability in the analysis and prediction of art education big data and can objectively evaluate the level of art education.

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Section: E Bp Neural Network Algorithmmentioning

confidence: 99%

Big Data’s Analysis and Prediction Method of Art Education Based on the BP Neural Network

Zhu

2022

Security and Communication Networks

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“…Relay reliability has an impact upon the reliability of electric vehicle systems. Pang [35] proposed an improved FWA that optimized the grey neural network model to predict relay lifetimes. The permutation and hybrid flow shop-scheduling problems represent important production scheduling problem commonly found in industrial production fields.…”

Section: A Research Progress Regarding Theory and Applications Of Fir...mentioning

confidence: 99%

A survey on several new popular swarm intelligence optimization algorithms

Wei

Huang

2023

Preprint

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Traditional optimization methods rely on parameter selection and a require high mathematical performance of the objective function. Thus, swarm intelligence optimization algorithms have attracted extensive attention as modern optimization methods in recent years, achieving remarkable results in the fields of machine learning, engineering optimization, process control, and elsewhere. Swarm intelligence optimization algorithms are a form of computing technology built upon the laws of biological group behavior, they are simple, fast, and place low requirements upon the objective functions. The traditional swarm intelligence algorithm offers new ideas for solving certain practical problems, however, it suffers from shortcomings in several experiments. In recent years, numerous scholars have proposed new swarm intelligence optimization algorithms, this paper selects several of the more typical swarm intelligence algorithms proposed in recent years at home and abroad, including the Whale Optimization Algorithm, Moth-Flame Optimization Algorithm, Fireworks Algorithm, Dragonfly Algorithm, Crow Search Algorithm, Butterfly Optimization Algorithm, and Pigeons Algorithm. Furthermore, the experimental performances of these algorithms are compared with respect to their convergence speed, accuracy, and stability, using 18 standard test functions, and the relative improvement methods are compared and analyzed. Finally, the characteristics of the swarm intelligence optimization algorithm are summarized, and its future development potential is discussed.

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“…The failure mode of the tested automotive EMR is not specified. In [89] an alternative model to predict EMR life in automotive applications is proposed, using the Improved-Fireworks-Algorithm Grey-NN -a swarm optimisation based algorithm. The method is evaluated with life cycle tests at different temperatures predicting the EMR-RUL based on the initial state of the EMR.…”

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

Cited by 13 publications

References 28 publications

Big Data’s Analysis and Prediction Method of Art Education Based on the BP Neural Network

Big Data’s Analysis and Prediction Method of Art Education Based on the BP Neural Network

A survey on several new popular swarm intelligence optimization algorithms

Prognostics for Electromagnetic Relays Using Deep Learning

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