Fault diagnosis based on extremely randomized trees in wireless sensor networks
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DOI: 10.1016/j.ress.2020.107284
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References listed on IDEAS
- Ahmad, Wasim & Khan, Sheraz Ali & Islam, M M Manjurul & Kim, Jong-Myon, 2019. "A reliable technique for remaining useful life estimation of rolling element bearings using dynamic regression models," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 67-76.
- Yoo, Minji & Kim, Taejin & Yoon, Joung Taek & Kim, Yunhan & Kim, Sooho & Youn, Byeng D., 2020. "A resilience measure formulation that considers sensor faults," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
- Jeong, Haedong & Park, Bumsoo & Park, Seungtae & Min, Hyungcheol & Lee, Seungchul, 2019. "Fault detection and identification method using observer-based residuals," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 27-40.
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Keywords
Machine learning; Extremely Randomized Trees; Fault diagnosis; Classification; Wireless Sensor Networks;All these keywords.
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