## 4 Conclusions  
This paper presented a comprehensive model for ultrasonic industrial fault detection, targeting diverse applications such as bearings and pipelines using contact sensors. The model's capability to evaluate and monitor the health of industrial equipment was assessed by detecting and classifying ten different ultrasonic signal conditions. Various dimensionality reduction techniques, including PCA, LDA, ICA, and UMAP, were explored, with ICA being selected for its superior performance. Ensemble classifiers such as voting, logistic regression, gradient boosting, Adaboost, stacking, and bagging were tested, demonstrating the effectiveness of the GB classifier based on performance metrics, confusion matrix, and ROC curves.  
The k-fold CV technique was employed to rigorously evaluate the models' performance, ensuring robustness and generalizability. Experimental results confirmed that the GB classifier outperformed the other models in terms of accuracy, precision, recall, and F1-measure. Our results suggest that using ICA for dimensionality reduction can improve model robustness for different faults.  
Advanced techniques like filtering, scaling, dimensionality reductions, and k-fold CV for ensuring evaluation can improve the signal quality for automatic defect recognition, reducing the reliance on skilled operators. Additionally, a data augmentation method is used, which helps improve the model's ability to generalize and perform well on unseen data.  
Furthermore, the model's real-time applicability was demonstrated through deployment on an ARM Cortex-M4F MCU, showcasing its potential for practical industrial applications. In our study, we explored two approaches: The direct classification method proved to be quicker and more precise, whereas the ICA-based method offered greater adaptability due to its signal-dependent nature and independence from specific features. Based on the results, integrating ultrasonic signal processing with ensemble machine learn  
Figure 14: Execution time for the first approach deployed —direct classification.  
Figure 15: Execution time for the second deployed approach—ICA dimensionality reduction prior to classification.  
ing techniques improves the efficiency of industrial fault diagnosis. This study not only provides a robust framework for fault detection but also emphasizes the importance of dimensionality reduction and real-time deployment in industrial settings. However, the ICA computation time on the MCU was significantly longer than direct classification, and the dataset could be expanded for better generalizability. The focus on bearings and pipelines limited the scope of the study. Future work could expand to a greater diversity of industrial data classes, explore further optimization of MCU deployment for faster processing times, as well as developing an optimized PCB board and integrating with IoT for comprehensive fault monitoring management.  
Writing, software, hardware and original draft, A.M.; Supervision, review and editing, F.N. All authors have read and agreed to the published version of the manuscript.  
Collaborative Research and Development Grant number CRDPJ 543712-19 and Discovery Grant RGPIN-2022-04228 from the Natural Sciences and Engineering Research Council of Canada (NSERC).  
Not applicable.  
The data presented in this study are openly available from UE Systems Co. Available online: [[https://www.uesystems.com/resources/sound-library/](https://www.uesystems.com/resources/sound-library/)]([https://www.uesystems.com/resources/sound-library/](https://www.uesystems.com/resources/sound-library/)) (accessed on 1 January 2020) [UE Systems Co.].  
The authors declare no conflicts of interest.