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@article{177261,
        author = {Dev Modi and Aayushi Nagare and Harshal Patil and Rakhi Khedkar and Vaibhav Pathrikar and Jyoti Deshmukh},
        title = {An Efficient Data-Driven Suspension And Chassis Prognosis System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {1435-1442},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177261},
        abstract = {This research focuses on developing a data-driven system for predicting the health and remaining lifespan of vehicle suspension and chassis components. By leveraging advanced data analytics techniques, the system aims to process real-time data from various sensors to identify anomalies, degradation patterns, and potential failures. The proposed data driven suspension and chassis prognosis system identifies any anomalies in both the suspension and chassis by analyzing real-time data from various sensors. To achieve and control this system, a TriSense fault detection algorithm is proposed in the paper. The outputs from various sensors like vibration, accelerometer and acoustic sensors are plotted in the form of waveforms on different axis, and if the waveforms exceed a certain threshold value, then the user is automatically notified that a fault has been detected in the suspension and chassis. The user is successfully notified about the fault through an automated message so that the appropriate control action can be taken and any major maintenance cost or failure can be avoided. The proposed system mainly focuses on vibration, acceleration, and sound parameters. This proactive approach enhances vehicle safety and reliability and contributes to significant cost savings by preventing unexpected breakdowns and minimizing downtime. For future enhancements, machine learning algorithms can build predictive models that accurately forecast component lifespan and recommend optimal maintenance schedules.},
        keywords = {Future prediction; MATLAB simulation; Prognosis of suspension and chassis: real-time detection, etc.},
        month = {May},
        }