Prognostics and Health Management (PHM) is framework for electrical/mechanical components in heavy machines represents a transformative approach that harnesses cutting-edge sensing technologies and analytics to predict and elevate reliability and efficiency of agricultural/construction machinery. By using advanced data collection and sophisticated analytics, PHM achieves real-time monitoring of critical performance parameters such as voltage, current, temperature, and operational cycles, along with field data mapped with GPS coordinates as well as environmental conditions. 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 and whenever any failure is detected advanced AI algorithms on machine and server will analyze the information and link to collected data which will be used to identify possible failures or assess the safety of the machine for future instances. 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, followed by facilitating targeted and proactive maintenance strategies. Importantly, the prognostic data generated not only aids in predicting failures but also plays a crucial role in failure cause analysis.
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/mechanical components but also advances the principles of precision agriculture and construction.