FAULT DETECTION ON RAILWAYS TURNOUT SYSTEM POINT MACHINE SIMULATOR USING INTERNET OF THINGS AND TINY MACHINE LEARNING TO SUPPORT PREDICTIVE MAINTENANCE
Signaling equipment in the railway industry has a vital role in ensuring trains' safety and smooth operation. A turnout system is a train signaling equipment that plays a critical role in train operations. Problems such as delays, train collisions, and train wheels out from the track are pro...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/68740 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Signaling equipment in the railway industry has a vital role in ensuring trains'
safety and smooth operation. A turnout system is a train signaling equipment that
plays a critical role in train operations. Problems such as delays, train collisions,
and train wheels out from the track are problems caused by fault or failure of the
turnout system. The point machine is one of the critical components and is often
the cause of operational failure in the turnout system. Therefore, a predictive
maintenance (PdM) strategy is needed to detect faults, reduce equipment
downtime and reduce equipment maintenance costs while knowing the useful life
of equipment as an early warning, especially on point machines. PdM can be
combined with the Internet of Things (IoT), but the use of IoT in PdM has other
impacts, such as obtaining, storing, maintaining, and analyzing data requires
costs that increase as the number of data increases. In addition, latency, payload,
and data transfer rate are challenges for PdM implementation using IoT on point
machines. Therefore, an approach is needed to balance the speed of data
transmission and storage and provide up-to-date information about the status of
the point machine. In addition, PdM is a big concept with steps that must be done
before making predictions or making decisions, one of which is the fault detection
phase. In this study, the fault detection phase was simulated on the point machine
using a DC motor by combining IoT technology and Tiny Machine Learning
(TinyML) to find a balance between storage efficiency and the data collected
while providing fast information. The simulation results show that using IoT and
TinyML can reduce 60.9% of data storage on the Thingsboard platform while
maintaining a transfer rate of ? 1 second. |
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