Sensor Fault Diagnosis Using Spectral Principal Component Analysis and CNN Deep Learning
Mou Jianqiang, Cui Shan
Abstract:
A data driven methodology for sensor fault diagnosis in sensor network using principal component analysis (PCA) of coherence spectrum and convolutional neural network (CNN) deep learning is proposed. The methodology was evaluated with the measurement data of a sensor network for ambient relative humidity (RH) monitoring of a chemical laboratory. The results demonstrated accuracy up to 99% for sensor fault diagnosis in the sensor network functioning across a large spectrum of frequencies for environmental monitoring.
Download:
IMEKO-TC6-2025-024.pdf
DOI:
10.21014/tc6-2025.024
Event details
IMEKO TC:
TC6
Event name:
TC6 M4Dconf2025
Title:

2025 IMEKO TC-6 International Conference on Metrology and Digital Transformation

Place:
Benevento, ITALY
Time:
03 September 2025 - 05 September 2025
 Back