Vladyslav Temchur Successfully Defends PhD in Predictive Equipment Diagnostics

On 2 September 2026, Vladyslav Temchur, a doctoral student at the Department of Automation of Energy Processes, successfully defended his PhD dissertation in specialty 151, Automation and Computer-Integrated Technologies. The ad hoc dissertation council DF 26.002.445 unanimously voted to award him the degree.

His dissertation, “Predictive Diagnostics of Rotating Energy Equipment Based on Neural Network Analysis of Vibration Signals,” was supervised by Taras Bahan, Candidate of Technical Sciences and Associate Professor at the department.

From Vibration Signals to Condition Diagnosis

Electric motor faults can lead to unplanned downtime and repair costs. The research explores how vibration signals can reveal a motor’s technical condition and help assess degradation, supporting timely maintenance decisions.

The study combines spectrogram analysis with neural-network methods that capture signal patterns and their evolution over time. The model was tested on experimental datasets covering several motor conditions, including rotor imbalance, shaft misalignment, bearing defects and broken rotor bars. An interactive diagnostic dashboard was also developed to present the results.

This approach supports condition-based maintenance by giving engineers additional evidence for evaluating faults and planning repairs.

Congratulations to Vladyslav Temchur and his supervisor on the successful defense! We wish them continued success in intelligent diagnostics and industrial automation research.

Dissertation materials and the council’s decision · Defense recording