Enhancing Energy Management through Machine Data Insights using Leverage Fleet Intelligence Data Analysis

In the evolving landscape of energy efficiency and sustainability, understanding machine behavior in real-world operating conditions is essential. This solution introduces a data-driven Energy Management Dashboard designed to analyze and report critical machine parameters by leveraging LFI (Leverage Fleet Intelligence) and LFI Data (Local Field Intelligence Data). The tool serves as a robust solution for engineering and operations teams to gain actionable insights into machine performance and exposure. By tracking key parameters—such as engine fan speed, coolant temperature, and machine speed—across a fleet of machines (with support for over 1100 unique signals), the solution enables real-time monitoring and historical analysis. It helps identify when parameters go outside their specified limits and assesses the resulting impact on overall machine performance. The core functionality includes: - Monitoring machine operating conditions under real field environments. - Correlating parameter anomalies with performance degradation. - Identifying exposure and usage trends based on location and operating conditions. This solution architecture integrates seamlessly with existing data pipelines and leverages LFI data for contextual insights. The development process involved collaboration with the Ruse squad to ensure relevance to on-field challenges. The expected outcomes include improved visibility into machine usage, early detection of potential issues, and enhanced data-driven decision-making for field operations and energy management. By transforming raw machine data into clear visual insights, this solution empowers teams to take proactive measures in improving efficiency and reliability.