CAFD: Context-Aware Fault Diagnostic Scheme Towards Sensor Faults Utilizing Machine Learning
- Alternative Title
- CAFD: Context-Aware Fault Diagnostic Scheme Towards Sensor Faults Utilizing Machine Learning
- Abstract
- Sensors’ existence as a key component of Cyber-Physical Systems makes it susceptible to failures due to complex environments, low-quality production, and aging. When defective, sensors either stop communicating or convey incorrect information. These unsteady situations threaten the safety, economy, and reliability of a system. The objective of this study is to construct a lightweight machine learning-based fault detection and diagnostic system within the limited energy resources, memory, and computation of the Wireless Sensor Networks (WSN). In this paper, Context-Aware Fault Diagnostic (CAFD) scheme is proposed based on ensemble learning algorithm called Extra-Trees. To evaluate the performance of the proposed scheme, a realistic WSN scenario composed of humidity and temperature sensor observations is replicated with extreme low-intensity faults. Six commonly occurring types of sensor fault are considered: drift, hard-over/bias, spike, erratic/precision degradation, stuck, and data-loss. The proposed CAFD scheme reveals the ability to accurately detect and diagnose low-intensity sensor faults in a timely manner. Moreover, the efficiency of the Extra-Trees algorithm in terms of diagnostic accuracy, F1-score, ROC-AUC, and training time is demonstrated by comparison with cutting-edge machine learning algorithms: Support Vector Machine and Neural Network.
- Author(s)
- 구인수; 사이드 우메르; 이영두; 잔 사나 울라
- Issued Date
- 2021
- Type
- Article
- Keyword
- WSN; Extra-Trees; machine learning; classification; data-driven; context-aware system; sensor faults; fault diagnosis
- DOI
- 10.3390/s21020617
- URI
- https://oak.ulsan.ac.kr/handle/2021.oak/9041
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_722dff13ad484096a00820995f59306d&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,CAFD:%20Context-Aware%20Fault%20Diagnostic%20Scheme%20Towards%20Sensor%20Faults%20Utilizing%20Machine%20Learning&offset=0&pcAvailability=true
- Publisher
- SENSORS
- Location
- 스위스
- Language
- 영어
- ISSN
- 1424-8220
- Citation Volume
- 21
- Citation Number
- 2
- Citation Start Page
- 617
- Citation End Page
- 617
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- Engineering > IT Convergence
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