Smart predictive maintenance system for industrial machinery using IoT sensors

Thesis Abstract

The increasing complexity and operational costs associated with industrial machinery necessitate the development of advanced maintenance strategies to enhance efficiency, reduce downtime, and optimize resource allocation. This study addresses the challenge of predictive maintenance in industrial settings by leveraging Internet of Things (IoT) sensors to enable real-time monitoring and early fault detection. The primary aim is to design, implement, and evaluate a smart predictive maintenance system that integrates IoT sensor data with machine learning algorithms to forecast equipment failures with high accuracy, thereby minimizing unscheduled downtimes and maintenance costs. The specific objectives include 1) identifying key machine health indicators measurable via IoT sensors; 2) developing an IoT-enabled data acquisition infrastructure for continuous monitoring of industrial machinery; 3) employing data analytics and machine learning models—such as regression analysis and support vector machines—to predict failure modes; 4) validating the predictive accuracy and reliability of the system through empirical testing; and 5) assessing the practical impact of the system on maintenance planning and overall operational efficiency. Methodologically, the research adopts a mixed-methods approach grounded in the pragmatic research paradigm. The study targets a manufacturing plant with a sample population comprising 50 industrial machines across different operational categories. A stratified random sampling technique ensures the selection of a representative subset of 20 critical machines for detailed monitoring. Data collection involves deploying IoT sensors—vibration, temperature, pressure, and acoustic sensors—attached to each machine, with data gathered continuously over a six-month period. Instrument validation includes calibration of sensors and pilot testing to ensure data accuracy, along with reliability checks such as Cronbach’s alpha for sensor-based data consistency. Quantitative data analysis employs statistical techniques including multiple regression analysis and support vector machine classification to develop predictive models of machine failure. Model performance is evaluated using metrics such as accuracy, precision, recall, and F1-score, with cross-validation to prevent overfitting. Qualitative insights are obtained through semi-structured interviews with maintenance personnel, analyzed via thematic analysis, to understand operational challenges and user acceptance of the proposed system. Ethical considerations involve obtaining informed consent from participating personnel and ensuring data privacy and security in compliance with industrial standards. Expected findings suggest that IoT sensor data, when processed through robust machine learning models, can achieve failure prediction accuracy exceeding 85%, significantly reducing unexpected breakdowns and optimizing maintenance schedules. The research anticipates demonstrating that integrating real-time sensor data with predictive analytics enhances decision-making capabilities, leading to operational cost savings, increased equipment lifespan, and improved safety standards in an industrial context. This study contributes novel insights into the deployment of IoT-based predictive maintenance systems in manufacturing environments, addressing the current gap in scalable, cost-effective solutions that combine sensor technology, data analytics, and operational practices. It advances the theoretical understanding of IoT integration within maintenance management frameworks, supported by the application of the Dynamic Maintenance Model and the Technology Acceptance Model to assess system usability and acceptance. The main conclusion emphasizes that IoT-enabled predictive maintenance systems can transform traditional maintenance practices into proactive, data-driven processes, thereby fostering Industry 4.0 implementation. Recommendations include scaling the system for broader application across diverse industrial sectors, integrating it with existing enterprise resource planning (ERP) systems, and developing standardized protocols for sensor deployment and data management. Future research should explore the integration of predictive maintenance with autonomous machinery and expand the dataset to include more complex failure modes, thereby refining predictive capabilities and operational resilience in industrial environments.