Comparison of resampling algorithms for particle filter based remaining useful life estimation

Limeng Guo; Yu Peng; Datong Liu; Yue Luo

doi:10.1109/ICPHM.2014.7036395

Comparison of resampling algorithms for particle filter based remaining useful life estimation

Limeng Guo
, Yu Peng
, Datong Liu
, Yue Luo

School of Electronics and Information Engineering

Harbin Institute of Technology
Beijing System Design Institute of Electro-Mechanic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

16 Scopus citations

Abstract

Due to the high performance on state tracking and predicting, particle filter (PF) algorithm has been utilized for diagnosis and prognosis in a variety of areas. Especially, PF can provide uncertainty representation and management on estimating the remaining useful life (RUL) of components and systems. However, particle degeneracy phenomenon limits its performance and application in most of the situations. Therefore, several re-sampling algorithms are proposed to alleviate this problem. Thus, different re-sampling algorithms should be focused and studied for the adaptability and applicability in RUL estimation. This work aims to compare the capabilities of different re-sampling algorithms and evaluate the performance in lithium-ion battery RUL prediction. Four re-sampling algorithms including multinomial re-sampling, residual re-sampling stratified re-sampling and systematic re-sampling are involved and analyzed. Actual battery test data sets from NASA PCoE are used to conduct experiments for evaluation and comparison. Moreover, some quantitative analysis metrics are applied to compare the results of battery RUL estimation.

Original language	English
Title of host publication	2014 International Conference on Prognostics and Health Management, PHM 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781479959426
DOIs	https://doi.org/10.1109/ICPHM.2014.7036395
State	Published - 9 Feb 2015
Event	2014 International Conference on Prognostics and Health Management, PHM 2014 - Cheney, United States Duration: 22 Jun 2014 → 25 Jun 2014

Publication series

Name	2014 International Conference on Prognostics and Health Management, PHM 2014

Conference

Conference	2014 International Conference on Prognostics and Health Management, PHM 2014
Country/Territory	United States
City	Cheney
Period	22/06/14 → 25/06/14

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Comparison and evaluation
Lithium-ion battery
Particle filter
Re-sampling algorithm
Remaining useful life estimation

Access to Document

10.1109/ICPHM.2014.7036395

Cite this

Guo, L., Peng, Y., Liu, D., & Luo, Y. (2015). Comparison of resampling algorithms for particle filter based remaining useful life estimation. In 2014 International Conference on Prognostics and Health Management, PHM 2014 Article 7036395 (2014 International Conference on Prognostics and Health Management, PHM 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPHM.2014.7036395

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title = "Comparison of resampling algorithms for particle filter based remaining useful life estimation",

abstract = "Due to the high performance on state tracking and predicting, particle filter (PF) algorithm has been utilized for diagnosis and prognosis in a variety of areas. Especially, PF can provide uncertainty representation and management on estimating the remaining useful life (RUL) of components and systems. However, particle degeneracy phenomenon limits its performance and application in most of the situations. Therefore, several re-sampling algorithms are proposed to alleviate this problem. Thus, different re-sampling algorithms should be focused and studied for the adaptability and applicability in RUL estimation. This work aims to compare the capabilities of different re-sampling algorithms and evaluate the performance in lithium-ion battery RUL prediction. Four re-sampling algorithms including multinomial re-sampling, residual re-sampling stratified re-sampling and systematic re-sampling are involved and analyzed. Actual battery test data sets from NASA PCoE are used to conduct experiments for evaluation and comparison. Moreover, some quantitative analysis metrics are applied to compare the results of battery RUL estimation.",

keywords = "Comparison and evaluation, Lithium-ion battery, Particle filter, Re-sampling algorithm, Remaining useful life estimation",

author = "Limeng Guo and Yu Peng and Datong Liu and Yue Luo",

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Guo, L, Peng, Y , Liu, D & Luo, Y 2015, Comparison of resampling algorithms for particle filter based remaining useful life estimation. in 2014 International Conference on Prognostics and Health Management, PHM 2014., 7036395, 2014 International Conference on Prognostics and Health Management, PHM 2014, Institute of Electrical and Electronics Engineers Inc., 2014 International Conference on Prognostics and Health Management, PHM 2014, Cheney, United States, 22/06/14. https://doi.org/10.1109/ICPHM.2014.7036395

Comparison of resampling algorithms for particle filter based remaining useful life estimation. / Guo, Limeng; Peng, Yu ; Liu, Datong et al.
2014 International Conference on Prognostics and Health Management, PHM 2014. Institute of Electrical and Electronics Engineers Inc., 2015. 7036395 (2014 International Conference on Prognostics and Health Management, PHM 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Due to the high performance on state tracking and predicting, particle filter (PF) algorithm has been utilized for diagnosis and prognosis in a variety of areas. Especially, PF can provide uncertainty representation and management on estimating the remaining useful life (RUL) of components and systems. However, particle degeneracy phenomenon limits its performance and application in most of the situations. Therefore, several re-sampling algorithms are proposed to alleviate this problem. Thus, different re-sampling algorithms should be focused and studied for the adaptability and applicability in RUL estimation. This work aims to compare the capabilities of different re-sampling algorithms and evaluate the performance in lithium-ion battery RUL prediction. Four re-sampling algorithms including multinomial re-sampling, residual re-sampling stratified re-sampling and systematic re-sampling are involved and analyzed. Actual battery test data sets from NASA PCoE are used to conduct experiments for evaluation and comparison. Moreover, some quantitative analysis metrics are applied to compare the results of battery RUL estimation.

AB - Due to the high performance on state tracking and predicting, particle filter (PF) algorithm has been utilized for diagnosis and prognosis in a variety of areas. Especially, PF can provide uncertainty representation and management on estimating the remaining useful life (RUL) of components and systems. However, particle degeneracy phenomenon limits its performance and application in most of the situations. Therefore, several re-sampling algorithms are proposed to alleviate this problem. Thus, different re-sampling algorithms should be focused and studied for the adaptability and applicability in RUL estimation. This work aims to compare the capabilities of different re-sampling algorithms and evaluate the performance in lithium-ion battery RUL prediction. Four re-sampling algorithms including multinomial re-sampling, residual re-sampling stratified re-sampling and systematic re-sampling are involved and analyzed. Actual battery test data sets from NASA PCoE are used to conduct experiments for evaluation and comparison. Moreover, some quantitative analysis metrics are applied to compare the results of battery RUL estimation.

KW - Comparison and evaluation

KW - Lithium-ion battery

KW - Particle filter

KW - Re-sampling algorithm

KW - Remaining useful life estimation

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Guo L, Peng Y , Liu D, Luo Y. Comparison of resampling algorithms for particle filter based remaining useful life estimation. In 2014 International Conference on Prognostics and Health Management, PHM 2014. Institute of Electrical and Electronics Engineers Inc. 2015. 7036395. (2014 International Conference on Prognostics and Health Management, PHM 2014). doi: 10.1109/ICPHM.2014.7036395

Comparison of resampling algorithms for particle filter based remaining useful life estimation

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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