Citation: Kajmakovic, A.; Diwold, K.;
Römer, K.; Pestana, J.;Kajtazovic, N.
Degradation Detection in a
Redundant Sensor Architecture.
Sensors 2022, 22, 4649. https://
doi.org/10.3390/s22124649
Academic Editor: Hossam A.
Gabbar
Received: 10 May 2022
Accepted: 17 June 2022
Published: 20 June 2022
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Article
Degradation Detection in a Redundant Sensor Architecture
Amer Kajmakovic
1,2,3,
*, Konrad Diwold
1,2
, Kay Römer
2,
*, Jesus Pestana
1
and Nermin Kajtazovic
3
1
Pro2Future GmbH, 8010 Graz, Austria; konrad.diwold@pro2future.at (K.D.); jesus.pestana@pro2future.at (J.P.)
2
Institute of Technical Informatics, Graz University of Technology, 8010 Graz, Austria
3
Siemens AG, 8054 Graz, Austria; nermin.kajtazovic@siemens.com
* Correspondence: amer.kajmakovic@pro2future.at (A.K.); roemer@tugraz.at (K.R.)
Abstract:
Safety-critical automation often requires redundancy to enable reliable system operation.
In the context of integrating sensors into such systems, the one-out-of-two (1oo2) sensor architecture
is one of the common used methods used to ensure the reliability and traceability of sensor readings.
In taking such an approach, readings from two redundant sensors are continuously checked and
compared. As soon as the discrepancy between two redundant lines deviates by a certain threshold,
the 1oo2 voter (comparator) assumes that there is a fault in the system and immediately activates
the safe state. In this work, we propose a novel fault prognosis algorithm based on the discrepancy
signal. We analyzed the discrepancy changes in the 1oo2 sensor configuration caused by degradation
processes. Several publicly available databases were checked, and the discrepancy between redundant
sensors was analyzed. An initial analysis showed that the discrepancy between sensor values changes
(increases or decreases) over time. To detect an increase or decrease in discrepancy data, two trend
detection methods are suggested, and the evaluation of their performance is presented. Moreover,
several models were trained on the discrepancy data. The models were then compared to determine
which of the models can be best used to describe the dynamics of the discrepancy changes. In addition,
the best-fitting models were used to predict the future behavior of the discrepancy and to detect if,
and when, the discrepancy in sensor readings will reach a critical point. Based on the prediction of
the failure date, the customer can schedule the maintenance system accordingly and prevent its entry
into the safe state—or being shut down.
Keywords: degradation; drift; discrepancy; redundant sensors; 1oo2 architecture
1. Introduction
Sensors are an indispensable part of industrial automation systems, as they allow
important environmental information such as light, heat, motion, or pressure to be eval-
uated and integrated into the automation process. As the number of sensors and sensor
types increases, so does the impact of sensor failures on an automation system. Automa-
tion requires trustworthy sensors, because they are at the beginning of the control chain,
and any incorrect information can lead to serious failures in the process. The effects of
sensor failures can propagate throughout the entire system and cause further damage to
machines or even workers. In a safety-critical system, sensors must be fail-safe [
1
]. This
means that any type of instability must be detected and acted upon to prevent catastrophic
failures, indicating that the information they provide must be reliable. The reliability of the
sensor can be increased by using high-quality and safety-certified sensors, which normally
have higher costs. Alternatively, multiple non-safety-certified sensors can be configured
and used in one of the redundant safety architectures [2].
The rising complexity of the automation systems, particularly sensor systems, in-
creased the number of sensor fault occurrences, consequently reducing reliability, safety,
and, in the end, availability parameters of the system. In general, there are many causes
for faults occurrence in sensor systems, but the most common are process variation, on-
Sensors 2022, 22, 4649. https://doi.org/10.3390/s22124649 https://www.mdpi.com/journal/sensors
