Smart restroom: preventive maintenance system using machine learning algorithm/ Charlin M. Infante, Ryan Rey D, Magdalita, Joey Boy E. Mission, John Michael P. Pontanos, and Rafaela E. Santos.--

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Infante, Charlin M [author]

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TextPublication details: Manila: 2025. Technological University of the Philippines,Description: xii, 168pages: 29cmContent type:

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BTH QA 76.9 I54 2025

Dissertation note: College of Industrial Technology.-- Bachelor of engineering technology major in computer engineering technology: Technological University of the Philippines, 2025. Summary: Traditional restroom maintenance practices are predominantly reactive, leading to inefficiencies, increased operational costs, and user dissatisfaction due to unexpected equipment failures. This thesis aimed to develop the "Smart Restroom," a preventive maintenance system leveraging IoT sensors and machine learning (ML) algorithms to transition from reactive to proactive facility management. The system captures critical parameters predictive of maintenance needs by integrating sensors to monitor real-time data such as usage frequency, environmental conditions, and consumable supply levels. Machine learning models, employing anomaly detection and time series forecasting, analyze this data to anticipate equipment malfunctions and optimize maintenance schedules. The proposed framework not only predicts imminent failures but also prioritizes tasks based on urgency, enhancing resource allocation. Experimental results demonstrate the system’s efficacy in reducing downtime, lowering maintenance costs, and improving hygiene standards through timely interventions. Furthermore, this study highlighted the system’s scalability and adaptability, positioning it as a viable component of smart city infrastructures aimed at sustainable urban management. The innovation lies in its data- driven approach, which transcends conventional threshold-based alerts, offering dynamic insights tailored to specific restroom usage patterns. This thesis underscored the transformative potential of IoT and ML in public infrastructure, advocating for their broader adoption to enhance operational efficiency and user experience in communal spaces.

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Bachelor's thesis

College of Industrial Technology.-- Bachelor of engineering technology major in computer engineering technology: Technological University of the Philippines, 2025.

Includes bibliographic references and index.

Traditional restroom maintenance practices are predominantly reactive, leading to
inefficiencies, increased operational costs, and user dissatisfaction due to unexpected
equipment failures. This thesis aimed to develop the "Smart Restroom," a preventive
maintenance system leveraging IoT sensors and machine learning (ML) algorithms to
transition from reactive to proactive facility management. The system captures critical
parameters predictive of maintenance needs by integrating sensors to monitor real-time
data such as usage frequency, environmental conditions, and consumable supply levels.
Machine learning models, employing anomaly detection and time series forecasting,
analyze this data to anticipate equipment malfunctions and optimize maintenance
schedules. The proposed framework not only predicts imminent failures but also prioritizes
tasks based on urgency, enhancing resource allocation. Experimental results demonstrate
the system’s efficacy in reducing downtime, lowering maintenance costs, and improving
hygiene standards through timely interventions. Furthermore, this study highlighted the
system’s scalability and adaptability, positioning it as a viable component of smart city

infrastructures aimed at sustainable urban management. The innovation lies in its data-
driven approach, which transcends conventional threshold-based alerts, offering dynamic

insights tailored to specific restroom usage patterns. This thesis underscored the
transformative potential of IoT and ML in public infrastructure, advocating for their
broader adoption to enhance operational efficiency and user experience in communal
spaces.

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