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...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2078-2489/16/6/496 |
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| Summary: | 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. |
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| ISSN: | 2078-2489 |