D. V. Fedorova

Papers

Total Citations

H-Index

About

D. V. Fedorova is a leading researcher in the digitalization of manufacturing and technical diagnostics, with a particular focus on applying artificial intelligence to condition monitoring. Her work addresses a critical gap in the field: while most methodologies target rotating machinery, Fedorova has pioneered the use of vibration signal analysis for the technical diagnostics of industrial robots. Her most-cited paper, "Technical Diagnostics of Industrial Robots Using Vibration Signals: Case Study on Detecting Base Unfastening" (2024, 6 citations), demonstrates a novel AI-driven approach to identifying structural faults like base unfastening, a common yet underexplored failure mode. This contribution is foundational for advancing predictive maintenance in automated production lines, enhancing both safety and efficiency. Fedorova’s research bridges the divide between traditional vibration analysis and modern machine learning, offering practical solutions for real-world industrial challenges. Her work is particularly impactful for students and engineers seeking to integrate AI into manufacturing diagnostics, as it provides a clear, case-study-driven methodology that can be adapted to various robotic systems. Through her innovative approach, Fedorova is shaping the future of smart manufacturing and reliability engineering.