Development of sensor-based machinery vibration assessment system for effective faults diagnostic in condition based maintenance using machine learning / Syed Mohd Syafiq Syed Mahmud
Rotating machines such as turbines, motors, pumps, and fans generally generate the vibration when operate under normal condition. However, the presence of excessive force within the component of rotating machine can produce the high level of vibration. Without a systematic monitoring system, a...
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| Format: | Thesis |
| Published: |
2022
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| Online Access: | http://studentsrepo.um.edu.my/14313/1/Syed_Mohd_Syafiq_Syed_Mahmud.jpg http://studentsrepo.um.edu.my/14313/3/syafiq.pdf http://studentsrepo.um.edu.my/14313/ |
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| Summary: | Rotating machines such as turbines, motors, pumps, and fans generally generate the
vibration when operate under normal condition. However, the presence of excessive force
within the component of rotating machine can produce the high level of vibration.
Without a systematic monitoring system, a high vibration can accelerate the machine
wear, consume excess power, and damage the equipment. Consequently, the machine
requires to shut down for maintenance and resulting unplanned downtime and increase
the cost of maintenance. Furthermore, high level of vibration also can affect the machine
performance and even worse affect the safety problems in engineered systems. High
vibration caused by imbalance and misalignment become a major issue and leading to
component damage such as shaft, seals, coupling and bearings in various rotating
engineering application. Misalignments occurred when rotational centerlines are not
collinear while imbalance is occurred when shaft geometric and mass centerline are not
coincided. In a traditional way, the vibration data are present in plot such as orbit, time
waveform, spectrum etc. However, to analysis and diagnose the vibration fault required
a skilled worker who has vast experience, high technical knowledge, and expertise in
vibration analysis. Furthermore, to diagnose the vibration fault would consume more time
before final maintenance decision can be made. Therefore, the advance monitoring
system that can diagnose the machinery fault is developed to supervise machine condition
effectively. The system is build based on sophisticated deep learning program language
by using modern python program to provide an assist and support in supervised the trend,
performance of the machine and provide recommend decision. By using Convolutional
Neural Network (CNN) method in deep learning offer a higher accuracy in prediction and
detecting the vibration faults. The system is develop using 1D Convolutional Neural
Network that can provide very simple in structure, easy to understand and flexible in
design. Based on the validation result, the system successfully to achieve 100% accuracy in predict and detection each vibration faults correctly. In addition to this, the system is
assessed with difference type of input data and the system achieve low accuracy when
reducing the training data and using unprocessed data. However, this method required a
huge number of training and validation dataset in order to get a better accuracy in predict
and detecting the vibration faults. The fewer dataset could provide a result in a poor
approximation and subsequently produce an incorrect interpretation of vibration faults.
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