OBD CHARM
Predictive maintenance is becoming increasingly crucial in the automotive sector. Because of the restricted availability of sensors and some of the design efforts, it is difficult to diagnose failure in advance in the vehicle. However, with the advancements in the automotive industry, it app
2025-06-28 16:28:41 - Adil Khan
OBD CHARM
Project Area of Specialization Software EngineeringProject SummaryPredictive maintenance is becoming increasingly crucial in the automotive sector. Because of the restricted availability of sensors and some of the design efforts, it is difficult to diagnose failure in advance in the vehicle. However, with the advancements in the automotive industry, it appears that analyzing sensor data in conjunction with machine learning approaches for failure prediction is now possible. Our On-Board Diagnostics Car Health Monitoring and Risk Management project aims to create a user-friendly vehicle fault prediction and remote health monitoring system via the dashboard. Through the use of OBD device automated collection of car sensor data is gathered and Artificial Intelligence techniques inclusive of machine learning and deep learning are applied to assess the state-of-wellbeing of car, prediction of faults beforehand and the provision of solutions to the user and the evaluation of residual life of the vehicle parameter.
Project ObjectivesThe objective of the project is to develop a car health diagnostic model with the use of OBD device to fetch car’s engine control unit’s data and AI techniques to gather and assess the real-time car sensors data and then to visualize in the form of dashboard the processed outcomes. The system enables its users to monitor and assess car condition in order to avoid any potential damage.
Project Implementation MethodThe system's development will observe bottom-up approach, different components are developed and then assembled together. Furthermore, agile software development is used in the development. The steps involved in development are as follows:
1 Data Collection and Exploration
The data is being collected via scanners attached to OBD ports in cars. The dataset will be explored in order to figure out the data we are dealing with and understand different parameters that are being collected.
2 Data Cleaning
The dataset collected from cars will be cleaned to eliminate redundant and erroneous data as well as to adjust null and missing values.
3 Data Visualization
The data will be visualized in the form of graphs in order to identify patterns and analyze the relationships between various OBD parameters and how they impact one another.
4 Feature Extraction
Feature Extraction will be used to minimize the number of features needed to describe a large dataset by creating new features from existing ones and then discarding the original ones. The new reduced collection of features should be able to accurately describe the data.
5 Model Building
A variety of machine learning or deep learning algorithms will be used to build a prediction model based on patterns identified between different parameters.
6 Model Validation and Improvement
A number of tests will be executed to evaluate accuracy of the prediction model. The model will be monitored in order to examine its performance efficiency, and changes will be made to improve prediction accuracy.
7. Front-End
The web interface will be designed for users and admin to monitor the car health. The web will be connected to a database in back-end.
Benefits of the Project-
Lower rate of sudden downtime or accidents would occur due to vehicle faults.
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OBD assists the manufacturers to provide better services to the users thus gaining more reach for their sales.
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Earlier prediction of faulty behavior of vehicle parameter results in lower compensation cost and thus economic growth.
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The use of OBD device is highly compatible across vehicles.
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Tracking vehicle’s safety and behavior patterns are all helpful to mitigate the risks of rapid acceleration, speeding or aggressive braking which in future will become a business liability. Thus our project enhances fleet and driver safety.
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Diagnostics and frequent car tuning cost money. Thus our system would provide the easiest and cheaper way of diagnostics.
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Car’s engine control unit’s data fetched through OBD device gives better insights of energy resources which ultimately results in better and responsible consumption for instance of fuel.
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The system enables its users to monitor and assess car condition in order to avoid any potential damage.
The project lies it’s foundation on the obd device to fetch the data through which the prediction is to be made. Also. huge amount of data depict the requirement of efficient GPU system.
As per technical details of deployments are concerned various platforms such as railway, vercel and digital oceans are explored. Since our data is huge and real-time therefore a platform that provides hosting for connectivity of database with real-time data along with unlimited capacity for data storage is required so that our full-stack can be hosted efficiently.
Final Deliverable of the Project HW/SW integrated systemCore Industry TransportationOther Industries IT Core Technology Artificial Intelligence(AI)Other TechnologiesSustainable Development Goals Decent Work and Economic Growth, Industry, Innovation and Infrastructure, Sustainable Cities and Communities, Life on LandRequired Resources| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Total in (Rs) | 79471 | |||
| OBD2 Scanner | Equipment | 3 | 5000 | 15000 |
| Server Fee | Miscellaneous | 11 | 861 | 9471 |
| Laptop | Equipment | 1 | 55000 | 55000 |