Detecting Enclosed Board Channel of Data Acquisition System Using Probabilistic Neural Network with Null Matrix

Zhang, Dapeng, Lin, Zhiling and Gao, Zhiwei (2022) Detecting Enclosed Board Channel of Data Acquisition System Using Probabilistic Neural Network with Null Matrix. Sensors, 22 (15). p. 5559. ISSN 1424-8220

[img]
Preview
Text
sensors-22-05559-v2.pdf - Published Version
Available under License Creative Commons Attribution 4.0.

Download (1MB) | Preview
Official URL: https://doi.org/10.3390/s22155559

Abstract

The board channel is a connection between a data acquisition system and the sensors of a plant. A flawed channel will bring poor-quality data or faulty data that may cause an incorrect strategy. In this paper, a data-driven approach is proposed to detect the status of the enclosed board channel based on an error time series obtained from multiple excitation signals and internal register values. The critical faulty data, contrary to the known healthy data, are constructed by using a null matrix with maximum projection and are labelled as training examples together with healthy data. Finally, the status of the enclosed board channel is validated by a well-trained probabilistic neural network. The experimental results demonstrate the effectiveness of the proposed method.

Item Type: Article
Uncontrolled Keywords: fault detection and diagnosis, board channel, probabilistic neural network, Neural Networks
Subjects: F300 Physics
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Rachel Branson
Date Deposited: 03 Aug 2022 10:35
Last Modified: 03 Aug 2022 10:45
URI: http://nrl.northumbria.ac.uk/id/eprint/49708

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics