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CAFD: Context-Aware Fault Diagnostic Scheme Towards Sensor Faults Utilizing Machine Learning

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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)
구인수사이드 우메르이영두잔 사나 울라
Issued Date
2021
Type
Article
Keyword
WSNExtra-Treesmachine learningclassificationdata-drivencontext-aware systemsensor faultsfault 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
Appears in Collections:
Engineering > IT Convergence
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