A similarity-based remaining useful life prediction method using multimodal degradation features and adjusted cosine similarity Article Swipe
Chengcheng Kong
,
Wennian Yu
,
Qiang Zeng
,
Zixu Chen
,
Yizhen Peng
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6501/ace20b
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6501/ace20b
When a large amount of full life-cycle data are available, similarity-based methods are the preferred method for remaining useful life (RUL) prediction due to their reliability and accuracy. Traditional similarity-based RUL prediction methods use a single model and single-scale degradation features, which are incapable of fully capturing the degradation behavior of the system. Additionally, the similarity of spatial orientation is neglected in the similarity-matching process. To fill these research gaps, a novel method is developed based on multimodal degradation features and adjusted cosine similarity (ACS) to tackle complex-system RUL prediction in this paper. Complete ensemble empirical mode decomposition with adaptive noise is employed to decouple global degradation and random fluctuations in run-to-failure sensor data. Slow feature analysis is utilized to obtain local degradation features, and residual terms are used as global degradation features. Then, multimodal degradation features are transformed into one-dimensional health degradation indicators by bidirectional gated recurrent unit autoencoder. An ACS is developed to estimate the matching similarity between the test degradation curve and the training degradation curve. The proposed scheme captures the time-varying multimodal degradation behavior and provides libraries of health curves with multiple degradation patterns. The designed scheme is evaluated on the C-MAPSS dataset and the results illustrate the competitiveness and effectiveness of the proposed method.
Related Topics
Concepts
Cosine similarity
Computer science
Similarity (geometry)
Degradation (telecommunications)
Artificial intelligence
Data mining
Pattern recognition (psychology)
Residual
Reliability (semiconductor)
Hilbert–Huang transform
Algorithm
Power (physics)
Computer vision
Filter (signal processing)
Quantum mechanics
Telecommunications
Physics
Image (mathematics)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1361-6501/ace20b
- OA Status
- hybrid
- Cited By
- 6
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4382199048
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4382199048Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1361-6501/ace20bDigital Object Identifier
- Title
-
A similarity-based remaining useful life prediction method using multimodal degradation features and adjusted cosine similarityWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-27Full publication date if available
- Authors
-
Chengcheng Kong, Wennian Yu, Qiang Zeng, Zixu Chen, Yizhen PengList of authors in order
- Landing page
-
https://doi.org/10.1088/1361-6501/ace20bPublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1088/1361-6501/ace20bDirect OA link when available
- Concepts
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Cosine similarity, Computer science, Similarity (geometry), Degradation (telecommunications), Artificial intelligence, Data mining, Pattern recognition (psychology), Residual, Reliability (semiconductor), Hilbert–Huang transform, Algorithm, Power (physics), Computer vision, Filter (signal processing), Quantum mechanics, Telecommunications, Physics, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 3Per-year citation counts (last 5 years)
- References (count)
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41Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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