Authors :
Abdullah Al Khudhayr; Turki Al Mutairi; Nassr Al Sadeg; Abdulaziz Bokhamseen; Abdulrhman Al Mulhim; Anas Al Marashi
Volume/Issue :
Volume 11 - 2026, Issue 9 - September
Google Scholar :
https://tinyurl.com/38pjt4cw
DOI :
https://doi.org/10.38124/ijisrt/26sep118
Note : A published paper may take 4-5
working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and
ResearchGate.
Abstract :
Vibration analysis remains one of the most reliable techniques for monitoring rotating equipment health, yet in
most plants it still depends on manual spectrum interpretation by a small pool of specialists, creating bottlenecks and
inconsistent diagnoses. This paper presents the Vibration Analysis and Diagnostic (VAD) platform, a rule-based decisionsupport tool that standardizes and accelerates first-level fault diagnosis for pumps, compressors, and turbines. Built in
Python with a Tkinter interface, NumPy/SciPy for frequency-domain calculation, Matplotlib for visualization, and FPDF
for reporting, the platform applies diagnostic rules drawn from the Mobius Institute vibration guide. Technicians enter
equipment specifications and the dominant observed frequency and receive a structured report of probable fault causes and
recommended actions, while the platform simultaneously builds a verified historical database of confirmed cases. This paper
describes the platform's architecture, operational workflow, and expected benefits in diagnostic speed and consistency, and
outlines a roadmap toward a machine-learning-based diagnostic model.
Keywords :
Vibration Analysis, Rotating Equipment, Predictive Maintenance, Fault Diagnosis, Rule-Based Expert System, Condition Monitoring.
References :
- Mobius Institute, Vibration Analysis Body of Knowledge, Mobius Institute Training Materials.
- ISO 20816-1:2016, Mechanical vibration — Measurement and evaluation of machine vibration, ISO, Geneva.
- R. K. Mobley, An Introduction to Predictive Maintenance, 2nd ed. Butterworth-Heinemann, 2002.
- J. S. Mitchell, Introduction to Machinery Analysis and Monitoring, 2nd ed. PennWell, 1993.
- [Add any additional Aramco-internal or course reference material used in the original project report.]
Vibration analysis remains one of the most reliable techniques for monitoring rotating equipment health, yet in
most plants it still depends on manual spectrum interpretation by a small pool of specialists, creating bottlenecks and
inconsistent diagnoses. This paper presents the Vibration Analysis and Diagnostic (VAD) platform, a rule-based decisionsupport tool that standardizes and accelerates first-level fault diagnosis for pumps, compressors, and turbines. Built in
Python with a Tkinter interface, NumPy/SciPy for frequency-domain calculation, Matplotlib for visualization, and FPDF
for reporting, the platform applies diagnostic rules drawn from the Mobius Institute vibration guide. Technicians enter
equipment specifications and the dominant observed frequency and receive a structured report of probable fault causes and
recommended actions, while the platform simultaneously builds a verified historical database of confirmed cases. This paper
describes the platform's architecture, operational workflow, and expected benefits in diagnostic speed and consistency, and
outlines a roadmap toward a machine-learning-based diagnostic model.
Keywords :
Vibration Analysis, Rotating Equipment, Predictive Maintenance, Fault Diagnosis, Rule-Based Expert System, Condition Monitoring.
