Obstacle-Aware Intelligent Fault Detection Scheme for Industrial Wireless Sensor Networks

Kaur, Gagandeep; Chanak, Prasenjit; Bhattacharya, Mahua

doi:10.1109/tii.2021.3133347

Cited by 17 publications

(1 citation statement)

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“…Sensor and actuator faults in complex distributed electro-mechanical systems are well studied [1,2], and in industrial wireless sensor networks [3]. In [1], a systematically characterized taxonomy of common sensor data faults that occur in deployed sensor networks and the detailed approaches commonly taken to model these faults are provided.…”

Section: Introductionmentioning

confidence: 99%

Wireless Sensor Networks Fault Detection and Identification

Selmic,

Scoggin,

Oonk

et al. 2023

IJRCS

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We have developed and experimentally tested a set of models for the detection and identification of sensor faults that commonly occur in wireless sensor networks. Considered faults include outlier, spike, variance, high-frequency noise, offset, gain, and drift faults. These faults affect the system operations and can endanger operators, final users, and the general public. The fault detection models are divided into two classes: data-centric models, which only analyze a single data stream, and system-centric models, which consider the overall system. For data-centric models, we use the magnitude, the gradient, and the variance of raw sensor data to model faults. For system-centric models, we introduce variogram-based techniques that allow faults to be detected by comparing readings from multiple sensors that measure related phenomena. For data-centric and system-centric sensor fault detection, we show how a few model parameters affect the sensitivity of wireless sensor network fault models. We present simulation and experimental results that illustrate the fault detection and identification models. The system is intended for health monitoring applications of the NASA Stennis Space Center (SSC) test stands and widely distributed support systems, including pressurized gas lines, propellant delivery systems, and water coolant lines. The testbed consists of Coremicro® reconfigurable embedded smart sensor nodes [29] capable of wireless communication, a network-capable application processor, a wireless base station, the software that supports sensor and actuator health monitoring, a database server, and a smartphone running a health monitoring Android application.

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