International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024
p-ISSN: 2395-0072
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DEVELOPMENT OF AN ECOSYSTEM TO ALERT THE USERABOUT THE SERVICE OF THE PART Sahil Sarvankar 1, Gauri Badade 2, Krishnaprasad 3, Prasad Baravkar 4, Shivapradeep 5 1,2,3,4Student, Dept. of Automobile Engineering, Pillai College of Engineering, New Panvel, India 5Professor, Dept. of Automobile Engineering, Pillai College of Engineering, New Panvel, India
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Abstract - The unfortunate reality is that a lot of car
Sheets), which facilitates data management and analysis. Second, the collected data is analyzed through a multilogistic regression model. This model uses machinelearning techniques to predict the wear condition and replacement need for each part. Third, the results of these analyzes are provided to users through web applications developed using Python(Python) and flask(Flask). Users can check in real time the status of their vehicle parts and when they need to be replaced through the landing page of this web application. The development of this system aims to significantly improve the efficiency of vehicle maintenance. The user can prevent unexpected vehicle failures and the resulting high cost of repair by monitoring the wear status of vehicle parts in real time through this system and planning replacement at the right time. In addition, the system opens up the possibility of extending the life of the vehicle by providing economic benefits to vehicle owners. For the implementation of these systems, the project team has actively utilized the latest data analysis techniques and web development technologies. Machine-learning algorithms were used for data analysis and predictive modeling, and flask frames optimized for web development were used to provide a user-friendly interface. This technical approach played a key role in the successful implementation of this project.
owners don't know how well their parts are wearing, which can cause unanticipated breakdowns and expensive repairs as a result. Specifically, it is critical to repair the parts as soon as possible because a single malfunction might have a domino effect that harms other parts. The study's solution to these problems is our ecosystem which is composed of two main components: a data acquisition module and a predictive analytics module. The data acquisition module is responsible for collecting sensor data from various car parts, such as engine temperature, oil pressure, and brake wear. The predictive analytics module uses machine learning algorithms, implemented in Python, to analyze the collected data and identify patterns and anomalies. This work created a system that can precisely forecast when to replace parts by analyzing the relationship between automotive part life cycle data and deceptive reading. This made it possible for car owners to schedule the necessary replacement work ahead of time and track the wear status of their parts in real time. In the long run, it is anticipated that the established system will lessen the financial burden on car owners by greatly increasing the efficiency of vehicle maintenance. This study investigates the viability of real-time component wear prediction systems and builds on previous research on vehicle maintenance. It also outlines the path for further research and addresses the possible effects this system may have on the auto repair sector.
1.1 SCOPE OF THE PROJECT The project focuses on the development of a system that monitors and evaluates the vehicle's worn parts in real time to inform the driver. The system continuously detects the condition of the main parts of the vehicle through sensors based on IoT technology, and uses data analysis algorithms to assess the degree of wear. This information is communicated to the driver through a user-friendly interface, helping to perform the necessary maintenance tasks in a timely manner. The main purpose of the project is to improve the safety of the vehicle and the efficiency of maintenance. This project is designed taking into account various use cases. For example, in addition to a basic wear detection system for personal vehicle users, it may include advanced monitoring and data analysis services for companies or organizations managing large-scale vehicle operations. This helps to maximize operational efficiency by processing large amounts of vehicle data and quickly
Key Words: Vehicle maintenance, data acquisition module, machine learning algorithms automotive, predictive analytics module
1. INTRODUCTION The project was developed to address the major problems facing vehicle owners regarding vehicle parts wear. In many cases, vehicle owners do not know exactly when their vehicle parts should be replaced. This leads to unexpected vehicle breakdowns, which leads to high cost repairs. To address these issues, the system serves to predict the expected life of each part based on the vehicle's misleading data, and inform the user when to replace it. The core functions of the system are. First, we collect the vehicle's misleading data and the life cycle data for each part. This data is stored in the Google sheet (Google
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