## 6 Conclusions and Feature Work  
The proposed methodology was demonstrated through a case study involving the detection of a release fault in a FANUC industrial robot during handling operations. This scenario highlights the potential of our approach for improving predictive maintenance strategies, particularly for industrial robots.  
In addressing a particular problem--detecting a robot's releasing at its base--Mean, Std, RMS, and Peak Value were shown to be the best-performing signal metrics, even under the realistic conditions of manipulation tasks concerning change in robot movement speed within the technological process. Moreover, we confirmed the hypothesis of the experiment: the chosen metrics allow the detection of anomalies in the vibration signal with an accuracy of higher than 95%, and this accuracy has been achieved for several neural network models and ensemble-learning techniques.  
Furthermore, to validate the hypothesis and reveal any weaknesses in the proposed models, a classification problem was solved to detect the anomaly--detection of the robot's release during the reloading process. Based on the Mean, Std, Peak Value, and RMS metrics of the vibration signal sequences, the performance of two machine learning models, Wide Neural Network (WNN) and Subspace K-Nearest Neighbors (KNN), were compared. The evaluation was based on four key metrics: accuracy, precision, recall, and specificity. In the analysis of the results, it was shown that although accuracies above 90% can still be considered high, the amount of incorrectly captured anomalous samples plays a key role in the choice of the right AI model. In fact, in addition to reducing the overall accuracy of the model, low values of the recall and specificity metrics indicate a low discriminative ability of the model, and therefore implementation of a similar model in practice can lead to increased costs and risks in production.  
By leveraging carefully chosen vibration signal properties, our approach facilitates early fault detection, minimizes unplanned downtime, and enhances the reliability of industrial robots. These metrics will play a key role in the early detection of anomalies, enabling timely maintenance interventions and minimizing machine downtime.  
In our future work, we will focus on further validating the proposed models and metrics across various robotic systems and operating conditions. Additionally, the application of these techniques, aimed at developing intelligent real-time fault detection and prediction systems for industrial equipment and improving the system's ability to generalize across different production environments, contribute to the advancement of modern manufacturing.  
Conceptualization, D.F.; funding acquisition, I.K. and I.Z.; investigation, D.F. and V.T.; methodology, D.F. and V.T.; project administration, I.Z. and K.T.; supervision, I.K.; visualization, T.D.; writing--original draft preparation, D.F.; writing--review and editing, V.T. and T.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Research of Implementation Methods and Means of Artificial Intelligence in Automated Quality Control Systems of Products with Volatile Quality Parameters, 1/0470/23 and Transformation and Innovation Consortium of Robotics and Automation of the Slovak Republic (Acronym Robotic), 090102-03-V01-00010.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.