About this project
This project applies vibration analysis to predictive maintenance of rotating machinery. It collects vibration signatures from bearings and shafts under healthy and seeded-fault conditions, performs FFT spectrum analysis to characterise fault frequencies, and demonstrates how condition monitoring prevents unplanned downtime compared with scheduled maintenance.
Suggested tech stack
- Accelerometer / DAQ
- MATLAB or Python
- FFT analysis
Chapters 1–5 outline
Chapter 1
Introduction: background to predictive maintenance by vibration analysis, statement of the problem (reactive maintenance causes costly unplanned downtime in plants), aim and objectives, research questions, scope, significance of the study, and definition of terms.
Chapter 2
Literature Review: theoretical framework, review of related works on predictive maintenance by vibration analysis, gaps in existing studies, and a summary positioning this project.
Chapter 3
Methodology / System Analysis and Design: experimental design; vibration data acquisition; FFT analysis. Includes data collection method, system requirements, use-case and architecture diagrams (or population, sample size, and instrument).
Chapter 4
Implementation and Results: fault signature catalogue and maintenance decision framework. Presentation of findings, testing, evaluation, and discussion of results.
Chapter 5
Summary, Conclusion and Recommendations: key findings, contribution to knowledge, limitations, and recommendations for further research.
Get this project done — chapters, code, defence support
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Preview sample