<p>Sensors are being used in many industrial applications for equipment health mon-itoring and anomaly detection. However, sometimes operation and maintenanceof these sensors are costly. Thus companies are interested in reducing the num-ber of required sensors as much as possible. The straightforward solution is tocheck the prediction power of sensors and eliminate those sensors with limitedprediction capabilities. However, this is not an optimal solution because if we dis-card the identified sensors. Their historical data also will not be utilized anymore.However, typically such historical data can help improve the remaining sensors’signal power, and abolishing them does not seem the right solution. Therefore, wepropose the first data-driven approach based on tensor completion for re-utilizingdata of removed sensors, in addition to remaining sensors to create virtual sensors.We applied the proposed method on vibration sensors of high-speed separators,operating with five sensors. The producer company was interested in reducing thesensors to two. But with the aid of tensor completion-based virtual sensors, weshow that we can safely keep only one sensor and use four virtual sensors thatgive almost equal detection power compared to when we keep only two physicalsensors.</p>
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:hh-45916 |
| Date | January 2021 |
| Creators | Raeeji Yaneh Sari, Noorali |
| Publisher | Högskolan i Halmstad, Akademin för informationsteknologi |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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