Incremental Clustering for Predictive Maintenance in Cryogenics for Radio Astronomy
Abstract
:1. Introduction
State of the Art
2. Materials and Methods
2.1. Hardware Description
- The first board, named HALL V1, contains Hall effect sensors ACS758xCB, produced by Allegro Microsystems, Manchester, NH, USA [13].
- The second board, named HALL CONDITIONING V1, includes the amplification stage and the voltage acquisition with the Analog-to-Digital Converter (ADC) ADS1015, produced by Texas Instruments, Dallas, TX, USA [14].
- An Arduino LEONARDO produced by Interaction Design Institute, Ivrea, Italy [15] for logic and additional acquisition through digital pins.
- A 230 VAC to 5 VDC power supply for powering the sensors, ADC, and the Arduino itself.
2.2. Firmware 1.0v and Acquisition Software 1.0v
2.3. Clustering Setup
3. Results and Discussion
- The Silhouette score [21] provides a measure of internal cohesion and separation between clusters. The score ranges from −1 to 1, where a higher score indicates better clusters.
- The Calinski–Harabasz index [22] measures the cluster’s compactness. A higher value indicates better clusters.
- The Davies–Bouldin index [23] measures the “compactness” and “separation” of clusters, with lower values indicating better clusters.
Metric | Value | Range |
---|---|---|
Silhouette score | 0.7848 | [−1, 1] |
Calinski–Harabasz index | [0, +∞] | |
Davies–Bouldin index | 0.2163 | [0, +∞] |
Adjusted Rand index | 98% | [0, 100] |
4. Conclusions and Future Works
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
RF | Radio Frequency |
SNR | Signal-to-Noise Ratio |
CHM | Cold Head Monitor |
MTBF | Mean Time Between Failure |
ADC | Analog-to-Digital Converter |
AC–DC | Alternate Current to Direct Current |
API | Application Programming Interface |
AI | Artificial intelligence |
ML | Machine learning |
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Model | Operating Hours | Samples per Channel | Class | |
---|---|---|---|---|
1 | CTI-1020 | >30 k | 161,172 | Maintenance |
2 | CTI-350 | <30 k | 139,098 | Normal Status |
3 | CTI-350 | >30 k | 151,192 | Maintenance |
4 | CTI-350 | >30 k | 130,716 | Maintenance |
5 | CTI-350 | <30 k | 150,415 | Normal Status |
6 | - | - | 150,919 | Compressor Fault |
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Cabras, A.; Ortu, P.; Pisanu, T.; Maxia, P.; Caocci, R. Incremental Clustering for Predictive Maintenance in Cryogenics for Radio Astronomy. Sensors 2024, 24, 2278. https://doi.org/10.3390/s24072278
Cabras A, Ortu P, Pisanu T, Maxia P, Caocci R. Incremental Clustering for Predictive Maintenance in Cryogenics for Radio Astronomy. Sensors. 2024; 24(7):2278. https://doi.org/10.3390/s24072278
Chicago/Turabian StyleCabras, Alessandro, Pierluigi Ortu, Tonino Pisanu, Paolo Maxia, and Roberto Caocci. 2024. "Incremental Clustering for Predictive Maintenance in Cryogenics for Radio Astronomy" Sensors 24, no. 7: 2278. https://doi.org/10.3390/s24072278
APA StyleCabras, A., Ortu, P., Pisanu, T., Maxia, P., & Caocci, R. (2024). Incremental Clustering for Predictive Maintenance in Cryogenics for Radio Astronomy. Sensors, 24(7), 2278. https://doi.org/10.3390/s24072278