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Introduction

Oil refineries are complex facilities that require a great deal of maintenance and monitoring to ensure smooth operations. Equipment failures in oil refineries can have severe consequences, including costly repairs, production downtime, and even safety hazards. Predictive maintenance strategies have been shown to be effective in mitigating equipment failures by identifying potential issues before they occur.

This thesis aims to investigate the use of predictive maintenance techniques to predict equipment failures in oil refineries. By analyzing historical data and incorporating predictive analytics, this study seeks to develop a model that can accurately predict when equipment failures are likely to occur, allowing for proactive maintenance planning.

This introduction will provide an overview of the background of the study, the problem statement, objectives, limitations, scope, significance, structure of the thesis, and definitions of key terms.

1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Introduction to predictive maintenance
2.2 Current approaches to predicting equipment failures in oil refineries
2.3 Data analytics in predictive maintenance
2.4 Machine learning techniques for predictive maintenance
2.5 Case studies on predictive maintenance in oil refineries
2.6 Challenges and limitations in predictive maintenance
2.7 Best practices in predictive maintenance
2.8 Importance of predictive maintenance in oil refineries
2.9 Summary of literature review
2.10 Gaps in existing literature

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection
3.3 Data preprocessing
3.4 Feature selection
3.5 Model development
3.6 Model evaluation
3.7 Validation techniques
3.8 Ethical considerations

Chapter 4: Discussion of Findings
4.1 Data analysis results
4.2 Model performance evaluation
4.3 Comparison with existing methods
4.4 Implications of findings
4.5 Recommendations for future research
4.6 Practical implications for oil refineries

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Conclusion
5.3 Contributions to knowledge
5.4 Practical implications
5.5 Limitations of the study
5.6 Recommendations for future research

Overall, this thesis will contribute to the field of predictive maintenance by providing insights into predicting equipment failures in oil refineries and offering practical recommendations for industry stakeholders. The study aims to enhance the efficiency and reliability of maintenance processes in oil refineries, ultimately leading to improved operational performance and cost savings.

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