SACM - Qatar
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Item Restricted The Impact of Data Analytics on Optimizing Equipment Reliability and Safety in Industrial Operations(Saudi Digital Library, 2025) Hazzazi, Turki; Liu, AllenEquipment failures result in downtime, safety exposure, and productivity loss in industrial operations, which provides motivation for this dissertation. Predictive maintenance is aligned with Industry 4.0 as it leverages the data from sensors and maintenance records for earlier fault detection and decision-making. The research focuses on reactive maintenance, uncertainty regarding model selection, and the lack of validated frameworks by presenting a data-driven predictive maintenance framework with indicators, comparative modelling, impact quantification, and an implementation roadmap. The methodology is based on a positivist philosophy, a deductive approach and a quantitative archival strategy based on maintenance records. A manufacturing case combines rotational speed, torque, temperature, tool wear, failure events, and meantime between failures and repairs. Modelling: RF and logistic regression - accuracy, AUC, F1 score, confusion matrix, ROC curve, data protection based on pseudonymization and organizational consent The anticipated contribution is a portable framework that links model outputs to planning by measures such as downtime reduction, cost reduction for maintenance, mean time metrics, uptime increase and return on investment. Interpretability and scalability of comparative evidence are discussed, as well as the limitations in data quality of secondary sources. Recommendations provide a roadmap for transition to predictive practice and suggest future work in real-time edge integration, industry validation and extendible Artificial Intelligence explainable.1 0