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

Saeed, Umer; Lee, Young-Doo; Jan, Sana Ullah; Koo, Insoo


Umer Saeed

Young-Doo Lee

Insoo Koo


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 a Wireless Sensor Network (WSN). In this paper, a Context-Aware Fault Diagnostic (CAFD) scheme is proposed based on an 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: a Support Vector Machine and a Neural Network

Journal Article Type Article
Acceptance Date Jan 14, 2021
Online Publication Date Jan 17, 2021
Publication Date 2021
Deposit Date Oct 21, 2022
Publicly Available Date Oct 21, 2022
Journal Sensors
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 21
Issue 2
Article Number 617
Keywords WSN; Extra-Trees; machine learning; classification; data-driven; context-aware system; sensor faults; fault diagnosis
Public URL


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