Research on Modeling Method of Life Prediction for Satellite Lithium Battery Based on SVR

Pang, Bo; Feng, Wenquan; Zhao, Hongbo; Li, Wenjuan; Chen, Shijie

doi:10.1109/phm-chongqing.2018.00179

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…In previous research, a large number of machine learning approaches have been proposed, most of which construct prognostics models by analyzing correlative sensor sequential data and associating the discovered hierarchical patterns with a definite prognostics task [16]. These prediction models provide effective evidence to the manufacturers [17,18] and include, for instance, auto-regressive integrated moving average-based (ARIMA) models [19,20], hidden Markov models (HMM) [21][22][23], support vector regression (SVR) models [24][25][26], artificial neural networks (ANNs) [27,28], radial basis functions (RBFs) [28], random forest (RF) regression [29], among others [12,30].…”

Section: Introductionmentioning

confidence: 99%

Convolution and Long Short-Term Memory Hybrid Deep Neural Networks for Remaining Useful Life Prognostics

et al. 2019

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Reliable prediction of remaining useful life (RUL) plays an indispensable role in prognostics and health management (PHM) by reason of the increasing safety requirements of industrial equipment. Meanwhile, data-driven methods in RUL prognostics have attracted widespread interest. Deep learning as a promising data-driven method has been developed to predict RUL due to its ability to deal with abundant complex data. In this paper, a novel scheme based on a health indicator (HI) and a hybrid deep neural network (DNN) model is proposed to predict RUL by analyzing equipment degradation. Explicitly, HI obtained by polynomial regression is combined with a convolutional neural network (CNN) and long short-term memory (LSTM) neural network to extract spatial and temporal features for efficacious prognostics. More specifically, valid data selected from the raw sensor data are transformed into a one-dimensional HI at first. Next, both the preselected data and HI are sequentially fed into the CNN layer and LSTM layer in order to extract high-level spatial features and long-term temporal dependency features. Furthermore, a fully connected neural network is employed to achieve a regression model of RUL prognostics. Lastly, validated with the aid of numerical and graphic results by an equipment RUL dataset from the Commercial Modular Aero-Propulsion System Simulation(C-MAPSS), the proposed scheme turns out to be superior to four existing models regarding accuracy and effectiveness.

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