Project Abstract

Abstract
In recent years, the rapid advancement of Internet of Things (IoT) technology has revolutionized various industries, including industrial sectors. One of the critical applications of IoT in industries is predictive maintenance, which aims to predict equipment failures before they occur, thereby reducing downtime, maintenance costs, and improving overall operational efficiency. Machine learning algorithms play a crucial role in enabling predictive maintenance by analyzing large volumes of sensor data collected from industrial assets. This research project focuses on the application of machine learning algorithms for predictive maintenance in Industrial IoT systems. The primary objective is to develop a predictive maintenance model that can accurately forecast equipment failures based on historical sensor data. The research will explore various machine learning techniques, such as supervised and unsupervised learning, deep learning, and anomaly detection, to identify patterns and anomalies in sensor data that indicate potential equipment failures. Chapter 1 provides an introduction to the research topic, including the background of the study, problem statement, objectives, limitations, scope, significance, structure of the research, and definition of key terms. Chapter 2 presents a comprehensive literature review on predictive maintenance, machine learning algorithms, IoT applications in industries, and relevant studies in the field. Chapter 3 outlines the research methodology, including data collection, preprocessing, feature selection, model training, evaluation, and validation. The research methodology in Chapter 3 includes discussions on the selection of appropriate machine learning algorithms, data preprocessing techniques, feature engineering, model tuning, and evaluation metrics. The research will utilize real-world sensor data from industrial equipment to train and test the predictive maintenance model. Chapter 4 presents a detailed discussion of the findings, including the performance of different machine learning algorithms, the accuracy of predictions, and the overall effectiveness of the predictive maintenance model. The discussion in Chapter 4 will analyze the results of the experiments, compare different algorithms, and identify the strengths and limitations of the predictive maintenance model. Chapter 5 concludes the research by summarizing the key findings, discussing the implications of the study, and providing recommendations for future research. The research aims to contribute to the advancement of predictive maintenance practices in industrial IoT systems through the application of machine learning algorithms.

Project Overview