Abstract:
To replace imported Resistance Temperature Detectors (RTDs) in heavy duty engines
such as those found in power plants construction and mining machinery and tracked
vehicles this thesis describes the development and validation of a locally made
temperature sensor. The study aims to lessen reliance on expensive imported sensors
that are susceptible to interruptions in the supply chain. The study created a prototype
utilizing locally sourced materials by reverse-engineering a typical automotive coolant
sensor. To validate the sensors performance in real time a complex test bench was built
and outfitted with cutting edge data acquisition systems and artificial intelligence
algorithms. In terms of response time and stability under different conditions, in
particular the results showed that the native sensor matched the accuracy and
dependability of imported RTDs. Performance analysis was improved by using AI in
the validation process giving rise to the possibility of using AI in sensor development
in the future. As a first step toward incorporating artificial intelligence (AI) into the
verification of critical components this thesis provides a workable substitute for a
crucial import dependency and opens the door for further advancements in sensor
technology.
