Prognostic Framework for failure analysis and smart farming

Prognostics and Health Management (PHM) for electrical components in tractors represents a transformative approach that harnesses cutting-edge monitoring technologies and predictive analytics to elevate the reliability and efficiency of agricultural machinery. By employing advanced data collection and sophisticated analytics, we achieve real-time monitoring of critical performance parameters such as voltage, current, temperature, and operational cycles, along with field data mapped with GPS coordinates. This capability allows for the early detection of anomalies and potential failures, thereby enhancing operational reliability. Data collected from the machine will be pushed to the server periodically, where advanced AI algorithms will analyze the information to identify possible failures or assess the safety of the machine. This proactive monitoring ensures that any potential issues are flagged in real-time, allowing for immediate intervention and maintenance actions. A comprehensive failure mode analysis is conducted to elucidate common failure patterns, thereby facilitating targeted and proactive maintenance strategies. Importantly, the prognostic data generated not only aids in predicting imminent failures but also plays a crucial role in failure cause analysis. By analyzing the conditions and performance metrics leading up to a failure, maintenance teams can identify root causes and implement corrective actions that prevent recurrence, thereby improving system resilience. The advantages of adopting these prognostic approaches are manifold, including a significant reduction in unplanned downtime, lower maintenance costs, and enhanced safety for operators through timely interventions. The findings underscore that the implementation of PHM not only extends the lifespan of electrical components but also advances the principles of precision agriculture by ensuring the unwavering reliability of essential systems in agricultural operations. This contributes to the ongoing evolution of smart farming technologies by gathering field data and harnessing it with machine data.