An Improved Similarity Trajectory Method Based on Monitoring Data under Multiple Operating Conditions

With the complexity of the task requirement, multiple operating conditions have gradually become the common scenario for equipment. However, the degradation trend of monitoring data cannot be accurately extracted in life prediction under multiple operating conditions, which is because some monitorin...

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Autores principales:	Jiancheng Yin, Yuqing Li, Rixin Wang, Minqiang Xu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	similarity trajectory multiple operating conditions life prediction monitoring data morphological pattern symbolic aggregate approximation Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/2e3ab10801a149cbaa89efdf91bb768c
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spelling	oai:doaj.org-article:2e3ab10801a149cbaa89efdf91bb768c2021-11-25T16:42:06ZAn Improved Similarity Trajectory Method Based on Monitoring Data under Multiple Operating Conditions10.3390/app1122109682076-3417https://doaj.org/article/2e3ab10801a149cbaa89efdf91bb768c2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10968https://doaj.org/toc/2076-3417With the complexity of the task requirement, multiple operating conditions have gradually become the common scenario for equipment. However, the degradation trend of monitoring data cannot be accurately extracted in life prediction under multiple operating conditions, which is because some monitoring data is affected by the operating conditions. Aiming at this problem, this paper proposes an improved similarity trajectory method that can directly use the monitoring data under multiple operating conditions for life prediction. The morphological pattern and symbolic aggregate approximation-based similarity measurement method (MP-SAX) is first used to measure the similarity between the monitoring data under multiple operating conditions. Then, the similar life candidate set, and corresponding weight are obtained according to the MP-SAX. Finally, the life prediction results of equipment under multiple operating conditions can be calculated by aggregating the similar life candidate set. The proposed method is validated by the public datasets from NASA Ames Prognostics Data Repository. The results show that the proposed method can directly and effectively use the original monitoring data for life prediction without extracting the degradation trend of the monitoring data.Jiancheng YinYuqing LiRixin WangMinqiang XuMDPI AGarticlesimilarity trajectorymultiple operating conditionslife predictionmonitoring datamorphological patternsymbolic aggregate approximationTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10968, p 10968 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	similarity trajectory multiple operating conditions life prediction monitoring data morphological pattern symbolic aggregate approximation Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	similarity trajectory multiple operating conditions life prediction monitoring data morphological pattern symbolic aggregate approximation Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Jiancheng Yin Yuqing Li Rixin Wang Minqiang Xu An Improved Similarity Trajectory Method Based on Monitoring Data under Multiple Operating Conditions
description	With the complexity of the task requirement, multiple operating conditions have gradually become the common scenario for equipment. However, the degradation trend of monitoring data cannot be accurately extracted in life prediction under multiple operating conditions, which is because some monitoring data is affected by the operating conditions. Aiming at this problem, this paper proposes an improved similarity trajectory method that can directly use the monitoring data under multiple operating conditions for life prediction. The morphological pattern and symbolic aggregate approximation-based similarity measurement method (MP-SAX) is first used to measure the similarity between the monitoring data under multiple operating conditions. Then, the similar life candidate set, and corresponding weight are obtained according to the MP-SAX. Finally, the life prediction results of equipment under multiple operating conditions can be calculated by aggregating the similar life candidate set. The proposed method is validated by the public datasets from NASA Ames Prognostics Data Repository. The results show that the proposed method can directly and effectively use the original monitoring data for life prediction without extracting the degradation trend of the monitoring data.
format	article
author	Jiancheng Yin Yuqing Li Rixin Wang Minqiang Xu
author_facet	Jiancheng Yin Yuqing Li Rixin Wang Minqiang Xu
author_sort	Jiancheng Yin
title	An Improved Similarity Trajectory Method Based on Monitoring Data under Multiple Operating Conditions
title_short	An Improved Similarity Trajectory Method Based on Monitoring Data under Multiple Operating Conditions
title_full	An Improved Similarity Trajectory Method Based on Monitoring Data under Multiple Operating Conditions
title_fullStr	An Improved Similarity Trajectory Method Based on Monitoring Data under Multiple Operating Conditions
title_full_unstemmed	An Improved Similarity Trajectory Method Based on Monitoring Data under Multiple Operating Conditions
title_sort	improved similarity trajectory method based on monitoring data under multiple operating conditions
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/2e3ab10801a149cbaa89efdf91bb768c
work_keys_str_mv	AT jianchengyin animprovedsimilaritytrajectorymethodbasedonmonitoringdataundermultipleoperatingconditions AT yuqingli animprovedsimilaritytrajectorymethodbasedonmonitoringdataundermultipleoperatingconditions AT rixinwang animprovedsimilaritytrajectorymethodbasedonmonitoringdataundermultipleoperatingconditions AT minqiangxu animprovedsimilaritytrajectorymethodbasedonmonitoringdataundermultipleoperatingconditions AT jianchengyin improvedsimilaritytrajectorymethodbasedonmonitoringdataundermultipleoperatingconditions AT yuqingli improvedsimilaritytrajectorymethodbasedonmonitoringdataundermultipleoperatingconditions AT rixinwang improvedsimilaritytrajectorymethodbasedonmonitoringdataundermultipleoperatingconditions AT minqiangxu improvedsimilaritytrajectorymethodbasedonmonitoringdataundermultipleoperatingconditions
_version_	1718413008685760512

An Improved Similarity Trajectory Method Based on Monitoring Data under Multiple Operating Conditions

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