Project Abstract

Abstract
This research project focuses on the application of machine learning algorithms for predictive maintenance in building management systems. The utilization of advanced technologies such as machine learning has shown promising potential in enhancing the efficiency and effectiveness of maintenance practices in various industries. In the context of building management systems, predictive maintenance plays a crucial role in ensuring the optimal performance and longevity of building components and systems. The primary objective of this study is to investigate the feasibility and effectiveness of implementing machine learning algorithms for predictive maintenance in building management systems. The research will involve the development of predictive models using historical data related to building maintenance activities, equipment performance, and environmental conditions. Various machine learning algorithms, such as decision trees, random forests, and neural networks, will be explored and evaluated for their predictive capabilities in identifying potential maintenance issues before they escalate into costly failures. The research will be conducted in multiple phases, starting with a comprehensive review of existing literature on predictive maintenance, machine learning algorithms, and their applications in building management systems. The subsequent phase will involve the collection and analysis of relevant data from building maintenance records, sensor data, and historical performance metrics. The data preprocessing and feature engineering steps will be crucial in preparing the dataset for training and testing the machine learning models. In the methodology chapter, the research will detail the selection and implementation of machine learning algorithms, the evaluation metrics used to assess model performance, and the validation techniques employed to ensure the robustness of the predictive models. The research will also address the challenges and limitations associated with implementing machine learning in predictive maintenance, such as data quality, model interpretability, and scalability. In the discussion of findings chapter, the research will present the results of the predictive maintenance models developed using machine learning algorithms. The evaluation of model accuracy, precision, recall, and F1-score will provide insights into the effectiveness of the predictive maintenance approach in building management systems. The discussion will also highlight the key factors influencing the performance of the machine learning models and provide recommendations for further improvements. In conclusion, this research project aims to demonstrate the potential benefits of utilizing machine learning algorithms for predictive maintenance in building management systems. By leveraging advanced technologies and data-driven approaches, building owners and facility managers can enhance the reliability, efficiency, and sustainability of their maintenance practices. The findings of this study will contribute to the growing body of knowledge on predictive maintenance and machine learning applications in the built environment. Keywords Predictive Maintenance, Machine Learning Algorithms, Building Management Systems, Data Analysis, Performance Evaluation, Maintenance Optimization.

Project Overview