A Study on the Effectiveness of Time Series Similarity Measures for the Comparison of Degradation Curves of Similar Engineering Systems
Data-driven methods have been shown to be suitable for the diagnosis and prognosis of the health of engineering systems. However, the training of data-driven methods usually requires a large amount of data, which is rarely available in industry. In addition, the prediction accuracy often degrades wh...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10489940/ |
| _version_ | 1797213608110718976 |
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| author | Marcel Braig Peter Zeiler |
| author_facet | Marcel Braig Peter Zeiler |
| author_sort | Marcel Braig |
| collection | DOAJ |
| description | Data-driven methods have been shown to be suitable for the diagnosis and prognosis of the health of engineering systems. However, the training of data-driven methods usually requires a large amount of data, which is rarely available in industry. In addition, the prediction accuracy often degrades when operating or environmental conditions change or when there are similar systems with different technical characteristics. Transfer learning offers the possibility to transfer knowledge about the degradation behavior between such systems. However, there is a risk that the degradation behavior differs too much, which leads to a so-called negative transfer. Therefore, the authors argue that a similarity assessment of degradation behavior is essential. An assessment based on the operational data of systems seems particularly appropriate. In this paper, the suitability of time series similarity measures for such data-based similarity assessments is investigated. The current state of research is presented. Thereby, no studies on the similarity comparison of degradation curves of engineering systems using time series similarity measures are found. Furthermore, measures for assessing the similarity of degradation curves are identified and categorized. In a case study on filter clogging curves, similarity tests are performed using these measures to find the most similar time series. Various approaches are proposed for evaluation, two of which are used in this paper. The results show that mostly a good selection is made, with some measures performing particularly well. The work presented in this paper represents valuable groundwork for the use of time series similarity measures in transfer learning. |
| first_indexed | 2024-04-24T11:00:59Z |
| format | Article |
| id | doaj.art-a151f6d822cf4d21a84d27547a996e95 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T11:00:59Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a151f6d822cf4d21a84d27547a996e952024-04-11T23:00:46ZengIEEEIEEE Access2169-35362024-01-0112496024962310.1109/ACCESS.2024.338469710489940A Study on the Effectiveness of Time Series Similarity Measures for the Comparison of Degradation Curves of Similar Engineering SystemsMarcel Braig0https://orcid.org/0000-0003-0737-8025Peter Zeiler1https://orcid.org/0000-0002-0904-7302Institute for Technical Reliability and Prognostics (IZP), Faculty of Mechanical and Systems Engineering, Esslingen University of Applied Sciences, Esslingen, GermanyInstitute for Technical Reliability and Prognostics (IZP), Faculty of Mechanical and Systems Engineering, Esslingen University of Applied Sciences, Esslingen, GermanyData-driven methods have been shown to be suitable for the diagnosis and prognosis of the health of engineering systems. However, the training of data-driven methods usually requires a large amount of data, which is rarely available in industry. In addition, the prediction accuracy often degrades when operating or environmental conditions change or when there are similar systems with different technical characteristics. Transfer learning offers the possibility to transfer knowledge about the degradation behavior between such systems. However, there is a risk that the degradation behavior differs too much, which leads to a so-called negative transfer. Therefore, the authors argue that a similarity assessment of degradation behavior is essential. An assessment based on the operational data of systems seems particularly appropriate. In this paper, the suitability of time series similarity measures for such data-based similarity assessments is investigated. The current state of research is presented. Thereby, no studies on the similarity comparison of degradation curves of engineering systems using time series similarity measures are found. Furthermore, measures for assessing the similarity of degradation curves are identified and categorized. In a case study on filter clogging curves, similarity tests are performed using these measures to find the most similar time series. Various approaches are proposed for evaluation, two of which are used in this paper. The results show that mostly a good selection is made, with some measures performing particularly well. The work presented in this paper represents valuable groundwork for the use of time series similarity measures in transfer learning.https://ieeexplore.ieee.org/document/10489940/Condition prognosisdata-driven methodsdegradationfiltrationPHMprognostics and health management |
| spellingShingle | Marcel Braig Peter Zeiler A Study on the Effectiveness of Time Series Similarity Measures for the Comparison of Degradation Curves of Similar Engineering Systems IEEE Access Condition prognosis data-driven methods degradation filtration PHM prognostics and health management |
| title | A Study on the Effectiveness of Time Series Similarity Measures for the Comparison of Degradation Curves of Similar Engineering Systems |
| title_full | A Study on the Effectiveness of Time Series Similarity Measures for the Comparison of Degradation Curves of Similar Engineering Systems |
| title_fullStr | A Study on the Effectiveness of Time Series Similarity Measures for the Comparison of Degradation Curves of Similar Engineering Systems |
| title_full_unstemmed | A Study on the Effectiveness of Time Series Similarity Measures for the Comparison of Degradation Curves of Similar Engineering Systems |
| title_short | A Study on the Effectiveness of Time Series Similarity Measures for the Comparison of Degradation Curves of Similar Engineering Systems |
| title_sort | study on the effectiveness of time series similarity measures for the comparison of degradation curves of similar engineering systems |
| topic | Condition prognosis data-driven methods degradation filtration PHM prognostics and health management |
| url | https://ieeexplore.ieee.org/document/10489940/ |
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