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

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

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 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

Citation

Saeed, U., Lee, Y., Jan, S. U., & Koo, I. (2021). CAFD: Context-Aware Fault Diagnostic Scheme towards Sensor Faults Utilizing Machine Learning. Sensors, 21(2), Article 617. https://doi.org/10.3390/s21020617

• 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
• DOI: https://doi.org/10.3390/s21020617
• Keywords: WSN; Extra-Trees; machine learning; classification; data-driven; context-aware system; sensor faults; fault diagnosis
• Public URL: http://researchrepository.napier.ac.uk/Output/2937068