Exploring the Nexus between Sensor Reliability and System
Performance: A Comprehensive Analysis
Deepak Tripathy
1
, Rahul Gottumukkala
2
, and Derek Kim
3
1,2,3
PHM Technology, North Fitzroy, Victoria, 3068, Australia
deepak.tripathy@phmtechnology.com
rahul.gottumukkala@phmtechnology.com
derek.kim@phmtechnology.com
ABSTRACT
In contemporary technological landscapes, sensors play a
pivotal role in enabling diverse applications across industries,
from healthcare to manufacturing. This paper undertakes a
thorough investigation on system performance (reliability
and availability of a system), focusing on the critical interplay
between baseline performance, performance with integrated
sensors and performance considering sensor reliability,
recognizing the foundational importance of sensors in data-
driven decision-making processes. The research employs a
causation-based approach to systematically develop
functional relations within the system. The failures identified
of each component and functional relationships will then be
analyzed using a simulation technique to understand the
inherent performance of the engineering system. From here,
a genetic algorithm is used to design a sensor set and tailor it
for an engineering system, providing a foundation for
conducting trade studies in the paper's subsequent sections.
Through rigorous quantitative analysis and simulations, we
compare the impacts of the performance of the sensor set
design compared to the baseline performance. The paper then
investigates the complexities of sensor reliability on overall
system performance. Through advanced simulations, we
elucidate the potential cascading effects that variations in
sensor reliability can have on the system's performance. By
exploring these ripple effects, we aim to provide a
comprehensive understanding of how sensor reliability plays
a crucial role in determining the success of complex systems.
Beyond the immediate considerations of sensor
characteristics, the paper analyses the maintenance aspects of
sensors by performing a series of analyses to suggest
maintenance aimed at improving the sensor and hence system
reliability. Highlighting the relationship between sensor
reliability and system performance, this section stresses the
critical role of consistent maintenance practices in ensuring
sustained data quality and system functionality. In
conclusion, this paper aims to highlight the different
perspectives that can be analyzed to understand the reality of
system performance, considering facets such as sensor
maintenance and reliability. It also aims to demonstrate
various approaches that can be applied to engineering
systems to uncover truths about sensor performance and
reliability.
1. INTRODUCTION
The increasing demand for diagnostics has made sensors
essential in planning and managing systems throughout their
lifecycle. Symptoms in a system, which are visible or
detectable indicators, provide crucial information about the
system's state. Identifying and deploying appropriate sensors
to capture these symptoms in real-time can inform
maintenance decisions, prevent failures, and ensure
continuous operation.
A model-based approach helps define functions, simulate
failures, identify critical functions, and diagnose appropriate
sensor responses that can detect failures before they occur,
enabling effective maintenance planning. This methodology
can be applied at any stage—early in development or before
specific missions—to gain valuable insights into system
performance and enhance the decision-making process.
Incorporating sensor analysis into this methodology further
enriches overall system effectiveness and efficiency.
This research develops a virtual model of a generic aircraft,
focusing on one of its key subsystems to assess baseline
system performance. It then evaluates whether all critical
functions have sensors capable of detecting failures before
they occur. This analysis aids in planning for specific
missions, such as those where certain functions are allowed
to fail to stay within constraints, if any, for instance, weight.
Finally, the study assesses system performance with
integrated sensors, highlighting different perspectives in
optimizing system reliability and efficiency.
