Development of an ML-Based Solution for Parametric Insights in System Performance

Background

A leading test and measurement enterprise collaborated with Tata Elxsi to develop ML algorithms that bring advanced parametric insights and system performance prediction into engineering workflows. The initiative was designed to reduce lengthy test cycles, improve operational efficiency, and enable faster Root Cause Analysis (RCA) by correlating component-level tests with system-level outcomes.

The transformation focused on:

• Component Impact Analysis to identify top contributors influencing system performance.
• Predictive ML Model Development for predicting system-level behavior using component test inputs.
• Root Cause Analysis Insights through tree-based explainability for faster parameter-level fault detection.
• Test Optimization by predicting redundant measurements and reducing calibration cycles.

By integrating AI in testing with predictive modeling, this initiative created a future-ready testing ecosystem, delivering tangible benefits in performance accuracy, RCA speed, and cost efficiency.

Challenge

The industry demands faster system performance prediction, efficient testing, and accurate Root Cause Analysis (RCA). Relying on lengthy calibration cycles and manual tracing, enterprises faced key issues:

• Lengthy Test Cycles – System-level calibration slowed throughput.
• Weak Correlation – Limited linkage between component-level analysis and system outcomes hindered insights.
• Inefficient RCA – Manual tracing delayed fault isolation.
• High Costs – Repetitive testing raised expenses and resource use.
• Scalability Gaps – Conventional methods lacked adaptability for diverse systems.

This initiative introduced an ML solution to deliver parametric insights, enabling faster RCA and optimized test workflows with AI in testing.

Solution

Tata Elxsi collaborated with a leading enterprise to design and implement an ML solution for parametric insights and system performance prediction. The approach integrated AI in testing, advanced analytics, and workflow optimization to minimize test cycles, reduce costs, and enable faster Root Cause Analysis (RCA).

Key Solutions:

• Component Impact Analysis – Identified top parameters influencing system performance.
• Predictive ML Model – Forecasted system behavior using component-level test data.
• RCA Insights – Tree-based explainability enabled faster fault isolation.
• Test Optimization – Automated prediction of redundant tests, reducing calibration efforts.

Impact

Tata Elxsi’s ML solution for parametric insights significantly improved system performance prediction, reduced test cycles, and optimized RCA accuracy. The solution automated key analysis tasks, enhanced component-level correlation, and minimized redundant calibrations, enabling enterprises to achieve faster, more reliable outcomes.

Key Achievements:

• 30% Reduction in Test Cycles – Shortened calibration and validation time.
• 90–95% Accuracy in Prediction – Reliable system-level forecasting from component data.
• 40% Faster RCA – Tree-based explainability accelerated fault isolation.
• Lower Costs – Reduced redundant testing and technician effort.

Services Rendered

• Component Impact Analysis
• Predictive ML Model Development
• Root Cause Analysis Enablement
• Test Optimization
• Workflow Integration