Remaining useful life prediction of ultrasonic motor based on Elman neural network with improved particle swarm optimization

Yang, Lin; Wang, Feng; Zhang, Jiaojiao; Ren, Weihao

doi:10.1016/j.measurement.2019.05.013

Cited by 60 publications

(22 citation statements)

References 20 publications

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“…As the context layer units have a storage effect on the hidden layer at a delayed time, ENN has dynamic memory and time-varying ability. Hence, ENN is widely used in time-series prediction [40,41]. As it discussed above, ENN is selected as the neural network type in this paper.…”

Section: Elman Neural Networkmentioning

confidence: 99%

A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion

Yang

Yao

et al. 2019

Energies

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Photovoltaic (PV) power generation is greatly affected by meteorological environmental factors, with obvious fluctuations and intermittencies. The large-scale PV power generation grid connection has an impact on the source-load stability of the large power grid. To scientifically and rationally formulate the power dispatching plan, it is necessary to realize the PV output prediction. The output prediction of single power plants is no longer applicable to large-scale power dispatching. Therefore, the demand for the PV output prediction of multiple power plants in an entire region is becoming increasingly important. In view of the drawbacks of the traditional regional PV output prediction methods, which divide a region into sub-regions based on geographical locations and determine representative power plants according to the correlation coefficient, this paper proposes a multilevel spatial upscaling regional PV output prediction algorithm. Firstly, the sub-region division is realized by an empirical orthogonal function (EOF) decomposition and hierarchical clustering. Secondly, a representative power plant selection model is established based on the minimum redundancy maximum relevance (mRMR) criterion. Finally, the PV output prediction for the entire region is achieved through the output prediction of representative power plants of the sub-regions by utilizing the Elman neural network. The results from a case study show that, compared with traditional methods, the proposed prediction method reduces the normalized mean absolute error (nMAE) by 4.68% and the normalized root mean square error (nRMSE) by 5.65%, thereby effectively improving the prediction accuracy.

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Section: Elman Neural Networkmentioning

confidence: 99%

A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion

Yang

Yao

et al. 2019

Energies

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“…A long short-term memory (LSTM) structure is proposed for predicting the robustness of short sequence monitoring with random initial wear [24]. A data-driven prediction method based on Elman neural network is proposed by Yang et al [25]. A method that uses deep learning tools and curve matching technology is proposed to estimate the robustness of the system [26].…”

Section: New Faultmentioning

confidence: 99%

An Improved PF Remaining Useful Life Prediction Method Based on Quantum Genetics and LSTM

Yang

Sun

2019

IEEE Access

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Remaining useful life (RUL) is the premise and basis of the equipment health management plan. As accurate as possible life prediction is of great significance to reliability and economy of equipment maintenance. In this paper, a data-driven improved particle filter (PF) RUL prediction method is proposed. A health indicator extraction method based on multi-feature fusion is introduced for the RUL prediction, which can visually show the degradation trend of the healthy state of the equipment. The degradation model and observation model of equipment health indicators are established, and the PF algorithm is used to track parameters of the model. A quantum genetic algorithm is employed to improve the problem of particle degradation in PF. On the basis of filter tracking, long short term memory (LSTM) network is used to predict the trend of model coefficients, which further improves the accuracy of RUL prediction. The experiment using the C-MAPSS data set shows the proposed method has a better prediction accuracy than other methods. INDEX TERMS Remaining useful life, particle filter, quantum genetic algorithm, long short term memory.

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“…The innovation of the proposed method is to use a separate ESN memory capacity to aggregate ESNs results in a dynamic process and an additional ESN to estimate RUL by the mean variance estimation (MVE) method. Yang et al [12] proposed an Elman neural network (ENN) based method to predict the RUL of an ultrasonic motor. The principal component analysis (PCA) was used to extract the motor degradation index from the monitoring data.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Optimization AdaBN-DCNN Method With Self-Optimized Structure and Hyperparameters for Domain Adaptation Remaining Useful Life Prediction

2020

IEEE Access

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The prediction of remaining useful life (RUL) of mechanical equipment provides a timely understanding of the equipment degradation and is critical for predictive maintenance of the equipment. In recent years, the applications of deep learning (DL) methods to predict equipment RUL have attracted much attention. There are two major challenges when applying the DL methods for RUL prediction: (1) It is difficult to select the prediction model structure and hyperparameters such as network depth, learning rate, batch size, and etc. (2) The developed prediction model is domain dependent, i.e., it can only give good prediction performance in one data domain (one particular type of working conditions and fault modes). In order to meet the challenges, a novel RUL prediction method developed using a deep convolutional neural network (DCNN) combined with Bayesian optimization and adaptive batch normalization (AdaBN) is presented in this paper. The proposed RUL prediction model is validated by the turbofan engine degradation simulation dataset provided by NASA. The prediction results show that the proposed prediction model provides better prediction results than model structures obtained by random search and grid search. The results also show that the domain adaptation capability of the prediction model has been improved. INDEX TERMS Remaining useful life prediction, Bayesian optimization, adaptive batch normalization, domain adaptation.

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Remaining useful life prediction of ultrasonic motor based on Elman neural network with improved particle swarm optimization

Cited by 60 publications

References 20 publications

A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion

A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion

An Improved PF Remaining Useful Life Prediction Method Based on Quantum Genetics and LSTM

A Bayesian Optimization AdaBN-DCNN Method With Self-Optimized Structure and Hyperparameters for Domain Adaptation Remaining Useful Life Prediction

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