Abstract
Relying heavily on effective limit checking, Systems for integrated vehicle health management (IVHM) are essential for ensuring launch vehicles are dependable and safe. [1]. The temperature, pressure, and voltage are measured by sensors positioned strategically throughout the vehicle. The data is continuously compared in real time to predefined limitations set by the IVHM system developer. The main task of limit checking is to keep an eye on engine sensor data and spot possible problems when measured values go over or below predetermined boundaries. These problems could result in engine damage or decreased performance. The IVHM system is built to quickly initiate alarms or carry out corrective actions in response, such as modifying engine load, turning on cooling fans, or alerting the driver to fix the issue. Limit checking is optimized by statistical or machine learning algorithms that analyse past sensor readings and operational conditions. By considering variables like normal parameter ranges, the type of vehicle, and external circumstances, this analysis improves upon predefined limits. IVHM systems greatly enhance vehicle performance, reduce failure risk, and enable proactive maintenance interventions through continuous monitoring and analysis within acceptable bounds.
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