Citation: Zhang, D.; Lin, Z.; Gao, Z.
Detecting Enclosed Board Channel of
Data Acquisition System Using
Probabilistic Neural Network with
Null Matrix. Sensors 2022, 22, 5559.
https://doi.org/10.3390/s22155559
Academic Editors: Hamed Badihi,
Tao Chen and Ningyun Lu
Received: 25 May 2022
Accepted: 21 July 2022
Published: 25 July 2022
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Article
Detecting Enclosed Board Channel of Data Acquisition System
Using Probabilistic Neural Network with Null Matrix
Dapeng Zhang
1
, Zhiling Lin
2,
* and Zhiwei Gao
3
1
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China; zdp@tju.edu.cn
2
School of Electrical Engineering, Tianjin University of Technology, Tianjin 300384, China
3
Faculty of Engineering and Environment, University of Northumbria, Newcastle upon Tyne NE2 8ST, UK;
zhiwei.gao@northumbria.ac.uk
* Correspondence: linzl2002@163.com
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.
Keywords: fault detection and diagnosis; board channel; probabilistic neural network
1. Introduction
Data acquisition systems play a vital role in the data collection of industry [
1
]. Among
them, the board tunnel, which is usually classified as analog input (AI), analog output
(AO), digital input (DI), and digital output (DO) modules, is a bridge between the proces-
sor and sensors, which ensures the data conversion at the physical level [
2
]. The tunnel
board is made up of enclosed circuit boards that are convenient to be replaced immedi-
ately once they are found to have any faults occur due to security reasons. In order to
detect the inertial faults of these circuit boards in time, most famous products, such as
Siemens,
Honeywell, etc.,
have provided error codes to help operators [
3
–
5
]. However,
these codes are limited to meeting the requirements of board channel diagnosis in a practical
complex application.
Different kinds of methods for fault detection and diagnosis (FDD) have been de-
veloped, which are classified as model-based approaches, signal-based approaches, and
data-driven approaches [
6
,
7
]. In model-based approaches, the fault diagnosis algorithms
are developed to monitor the consistency between the measured outputs of the practi-
cal systems and the model-predicted outputs, which are based on an appropriate model,
whether a physical model or equivalent model. Reference [
8
] proposed a new method by
combining the model-based FDD method and the support vector machine (SVM) method.
In reference [
9
], the spindle modes are determined through a three-step procedure in
order to overcome these issues of the low number of sensors and the presence of many
harmonics in the measured signals and to extract the characteristics of the system. In refer-
ence [
10
], based on the information of fault-free data series, fault detection was promptly
implemented by comparison with the model forecast and real-time process. Signal-based
approaches include time-domain analysis, frequency-domain analysis, and both together.
Reference [
11
] proposed a novel “frequency-domain damping design” using a high-pass
filter for acceleration-based bilateral control (ABC) based on modal space analysis. In
reference [
12
], a unified measurement model was utilized to simultaneously characterize
Sensors 2022, 22, 5559. https://doi.org/10.3390/s22155559 https://www.mdpi.com/journal/sensors
