CAFD: context-aware fault diagnostic scheme towards sensor faults utilizing machine learning

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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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.
Original languageEnglish
Article number617
Number of pages15
JournalSensors
Volume21
Issue number2
DOIs
Publication statusPublished - 17 Jan 2021

Keywords

  • WSN
  • extra-trees
  • machine learning
  • classification
  • data-driven
  • context-aware system
  • sensor faults
  • fault diagnosis

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