Development of IoT-based Predictive Maintenance System for Mechanical Equipment

Thesis Abstract

Mechanical equipment failures and unplanned downtimes pose significant operational and financial challenges across manufacturing and industrial sectors, emphasizing the urgent need for efficient maintenance strategies that minimize operational disruptions. This study aims to develop a comprehensive IoT-based predictive maintenance system designed to enhance the reliability and operational efficiency of mechanical equipment. The specific objectives include designing an integrated IoT architecture for real-time data acquisition, developing predictive algorithms for failure detection, and evaluating the system’s performance in a real-world industrial environment. The research adopts a mixed-methods approach, combining quantitative data analysis with qualitative validation to ensure robustness and practical relevance. The study’s methodology involves a case study design within a manufacturing plant operating a fleet of over 200 mechanical machines, including pumps, conveyor belts, and motors. The population encompasses all equipment susceptible to failure, with a purposive sample of 50 critical machines selected based on failure history and operational significance. Data collection instruments include IoT sensors for vibration, temperature, pressure, and acoustic signals, integrated with an industrial IoT platform for continuous data streaming. The reliability and validity of sensor data are ensured through calibration procedures, and data preprocessing techniques are applied to filter noise and anomalies. The analytical framework employs machine learning techniques, particularly Random Forest and Support Vector Machine classifiers, to develop failure prediction models. Time-series analysis and regression models are used to identify key failure indicators and forecast maintenance needs, while the system’s performance is assessed via metrics such as accuracy, precision, recall, and F1 score. Additionally, thematic analysis is utilized to interpret qualitative data obtained from maintenance personnel interviews, providing insights into system usability and operational integration challenges. Expected findings indicate that the proposed IoT-based system can significantly improve failure detection accuracy, reducing unscheduled downtimes by up to 30% and lowering maintenance costs by approximately 20%. The predictive models are anticipated to demonstrate high efficacy, with an F1 score exceeding 85%, validating their suitability for real-time industrial applications. The integration of sensor data analytics with existing maintenance processes is expected to enhance decision-making, leading to more proactive and condition-based maintenance strategies. Furthermore, the qualitative analysis is projected to reveal critical factors influencing system adoption, including ease of use and data transparency, informing recommendations for practical deployment. This research contributes to the existing body of knowledge by advancing the application of IoT and machine learning techniques within predictive maintenance frameworks, specifically tailored to mechanical equipment in industrial settings. It addresses key gaps identified in prior studies, notably the limited exploration of real-time integrated systems and their operational impacts. The developed predictive models and system architecture provide a scalable blueprint that can be adapted across diverse manufacturing environments, facilitating Industry 4.0 transformation efforts. The study concludes that IoT-enabled predictive maintenance models hold considerable promise for enhancing equipment reliability and operational efficiency. It recommends the adoption of the developed system in manufacturing plants, complemented by staff training and continuous system evaluation to optimize performance. Further research suggested includes exploring the integration of advanced analytics such as deep learning, expanding sensor types for comprehensive monitoring, and investigating long-term impacts on maintenance practices and organizational change management. Overall, this thesis demonstrates that leveraging IoT technology for predictive maintenance is both feasible and beneficial, offering a strategic advantage in modern industrial operations.