Presentation Type
Article
Location
Kennesaw, Georgia
Start Date
1-4-2026 9:00 AM
End Date
1-4-2026 10:15 AM
Description
Using vibroacoustic signals to characterize the health and status of industrial equipment is well-used. Many recent studies leverage a black box approach for developing a vibroacoustic machine learning (ML) architecture capable of identifying anomalous behaviors in the robot operation. While useful for routine automation systems, it is not suitable for dynamic robot systems with flexible or behavioral programming, or robots that will experience consistently changing environmental factors. To address these shortcomings, a discretized approach is explored. The proposed Long Short-Term Memory (LSTM)-Transformer Hybrid ML architecture segments its training data by manipulator joints to incorporate the impact of each subsystem on the overall system response. The RMSE & R2 of the initial and final joint position predictions were 22.818 & 0.482 and 37.899 & 0.278, respectively. This indicates positive influence in the model's ability to predict, demonstrating the potential for this novel approach, with room for model improvement in future research.
Vibroacoustic Characterization of Manipulator Robot via Kinematic Discretization
Kennesaw, Georgia
Using vibroacoustic signals to characterize the health and status of industrial equipment is well-used. Many recent studies leverage a black box approach for developing a vibroacoustic machine learning (ML) architecture capable of identifying anomalous behaviors in the robot operation. While useful for routine automation systems, it is not suitable for dynamic robot systems with flexible or behavioral programming, or robots that will experience consistently changing environmental factors. To address these shortcomings, a discretized approach is explored. The proposed Long Short-Term Memory (LSTM)-Transformer Hybrid ML architecture segments its training data by manipulator joints to incorporate the impact of each subsystem on the overall system response. The RMSE & R2 of the initial and final joint position predictions were 22.818 & 0.482 and 37.899 & 0.278, respectively. This indicates positive influence in the model's ability to predict, demonstrating the potential for this novel approach, with room for model improvement in future research.
