The Future Is Organic: A Deep Dive into Techniques and Applications for Real-Time Condition Monitoring in SASO Systems—A Systematic Review
Condition Monitoring (CM) is a key component of Self-Adaptive and Self-Organizing (SASO) systems. By analyzing sensor data, CM enables systems to react to dynamic conditions, supporting the core principles of Organic Computing (OC): robustness, adaptability, and autonomy. This survey presents a stru...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2078-2489/16/6/496 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849471950740520960 |
|---|---|
| author | Tim Nolte Sven Tomforde |
| author_facet | Tim Nolte Sven Tomforde |
| author_sort | Tim Nolte |
| collection | DOAJ |
| description | Condition Monitoring (CM) is a key component of Self-Adaptive and Self-Organizing (SASO) systems. By analyzing sensor data, CM enables systems to react to dynamic conditions, supporting the core principles of Organic Computing (OC): robustness, adaptability, and autonomy. This survey presents a structured overview of CM techniques, application areas, and input data. It also assesses the extent to which current approaches support self-* properties, real-time operation, and predictive functionality. Out of 284 retrieved publications, 110 were selected for detailed analysis. About 38.71% focus on manufacturing, 65.45% on system-level monitoring, and 6.36% on static structures. Most approaches (69.09%) use Machine Learning (ML), while only 18.42% apply Deep Learning (DL). Predictive techniques are used in 16.63% of the studies, with 38.89% combining prediction and anomaly detection. Although 58.18% implement some self-* features, only 42.19% present explicitly self-adaptive or self-organizing methods. A mere 6.25% incorporate feedback mechanisms. No study fully combines self-adaptation and self-organization. Only 5.45% report processing times; however, 1000 Hz can be considered a reasonable threshold for high-frequency, real-time CM. These results highlight a significant research gap and the need for integrated SASO capabilities in future CM systems—especially in real-time, autonomous contexts. |
| format | Article |
| id | doaj-art-056fa228a0de4b8ea6afc4306f9061fe |
| institution | Kabale University |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-056fa228a0de4b8ea6afc4306f9061fe2025-08-20T03:24:39ZengMDPI AGInformation2078-24892025-06-0116649610.3390/info16060496The Future Is Organic: A Deep Dive into Techniques and Applications for Real-Time Condition Monitoring in SASO Systems—A Systematic ReviewTim Nolte0Sven Tomforde1Department of Computer Science, University of Kiel, Herrmann-Rodewald-Str. 3, 24118 Kiel, GermanyDepartment of Computer Science, University of Kiel, Herrmann-Rodewald-Str. 3, 24118 Kiel, GermanyCondition Monitoring (CM) is a key component of Self-Adaptive and Self-Organizing (SASO) systems. By analyzing sensor data, CM enables systems to react to dynamic conditions, supporting the core principles of Organic Computing (OC): robustness, adaptability, and autonomy. This survey presents a structured overview of CM techniques, application areas, and input data. It also assesses the extent to which current approaches support self-* properties, real-time operation, and predictive functionality. Out of 284 retrieved publications, 110 were selected for detailed analysis. About 38.71% focus on manufacturing, 65.45% on system-level monitoring, and 6.36% on static structures. Most approaches (69.09%) use Machine Learning (ML), while only 18.42% apply Deep Learning (DL). Predictive techniques are used in 16.63% of the studies, with 38.89% combining prediction and anomaly detection. Although 58.18% implement some self-* features, only 42.19% present explicitly self-adaptive or self-organizing methods. A mere 6.25% incorporate feedback mechanisms. No study fully combines self-adaptation and self-organization. Only 5.45% report processing times; however, 1000 Hz can be considered a reasonable threshold for high-frequency, real-time CM. These results highlight a significant research gap and the need for integrated SASO capabilities in future CM systems—especially in real-time, autonomous contexts.https://www.mdpi.com/2078-2489/16/6/496self-adaptive systemsself-organization systemsorganic computingcondition monitoringreal-timemachine learning |
| spellingShingle | Tim Nolte Sven Tomforde The Future Is Organic: A Deep Dive into Techniques and Applications for Real-Time Condition Monitoring in SASO Systems—A Systematic Review Information self-adaptive systems self-organization systems organic computing condition monitoring real-time machine learning |
| title | The Future Is Organic: A Deep Dive into Techniques and Applications for Real-Time Condition Monitoring in SASO Systems—A Systematic Review |
| title_full | The Future Is Organic: A Deep Dive into Techniques and Applications for Real-Time Condition Monitoring in SASO Systems—A Systematic Review |
| title_fullStr | The Future Is Organic: A Deep Dive into Techniques and Applications for Real-Time Condition Monitoring in SASO Systems—A Systematic Review |
| title_full_unstemmed | The Future Is Organic: A Deep Dive into Techniques and Applications for Real-Time Condition Monitoring in SASO Systems—A Systematic Review |
| title_short | The Future Is Organic: A Deep Dive into Techniques and Applications for Real-Time Condition Monitoring in SASO Systems—A Systematic Review |
| title_sort | future is organic a deep dive into techniques and applications for real time condition monitoring in saso systems a systematic review |
| topic | self-adaptive systems self-organization systems organic computing condition monitoring real-time machine learning |
| url | https://www.mdpi.com/2078-2489/16/6/496 |
| work_keys_str_mv | AT timnolte thefutureisorganicadeepdiveintotechniquesandapplicationsforrealtimeconditionmonitoringinsasosystemsasystematicreview AT sventomforde thefutureisorganicadeepdiveintotechniquesandapplicationsforrealtimeconditionmonitoringinsasosystemsasystematicreview AT timnolte futureisorganicadeepdiveintotechniquesandapplicationsforrealtimeconditionmonitoringinsasosystemsasystematicreview AT sventomforde futureisorganicadeepdiveintotechniquesandapplicationsforrealtimeconditionmonitoringinsasosystemsasystematicreview |