## 6 Open Issues & Future Works  
The dataset examined in this work demonstrates the complicated relation between observed parameters in industrial rotating machinery, which possesses considerable difficulties due to its non-linear structure and strong feature correlations. Reducing unexpected downtime and unscheduled repairs, optimizing maintenance, and preserving operational safety depend on efficient and accurate defect detection. Although it is relatively easy to collect and process data using modern sensors and communication technologies, it can be challenging to retrieve useful information from this multi-modal, inherently noisy data. In a possible pre-processing step, unsupervised learning approaches are crucial because they can increase the SNR, which can improve future analysis and signal clarity [46, 47]. By leveraging labeled data, supervised methods aim to deliver actionable insights that directly support predictive maintenance strategies. These techniques help prepare the data for downstream applications, bridging the gap between analyzing raw sensor data and advanced analytical models. To provide useful insights for predictive maintenance, this paper offers an empirical examination of various supervised learning techniques using real-world industrial data obtained via low-cost, low-power LoRa-WAN devices.  
To improve the accuracy and robustness of the model, future studies could investigate combining data from several frequency bands within the acceleration and ultrasound range. LSTMs and CNNs offer promising approaches for identifying temporal and spatial patterns in the data. LSTMs are well-suited to capture sequential dependencies that show the progression of faults, but CNNs are capable of identifying crucial spatial features. Simplifying sensor positioning and data collection techniques may help to further enhance input data quality and boost model performance as a whole. Combining these methods with localized devices in real-time monitoring systems may result in faster reactions and more immediate on-site insights.  
Furthermore, by using transfer learning, models may be able to generalize across various datasets or types of machinery, decreasing the requirement for large amounts of labeled data while increasing applicability. These developments are anticipated to make a substantial contribution to the field of intelligent detection as well as to the wider industrial use of predictive maintenance solutions.  
Conceptualization, T.K. and S.B.; methodology, S.D.; software, M.Y.; validation, M.Y. and A.K.O.; formal analysis, S.B.; investigation, T.K. and S.B.; resources, T.K. and S.B.; data curation, M.Y.; writing--original draft preparation, T.K., S.B., A.K.O., M.Y., and S.D.; writing--review and editing, T.K., S.B., A.K.O., M.Y., and S.D.; visualization, S.D. and M.Y.; supervision, A.K.O.; project administration, A.K.O. All authors have read and agreed to the published version of the manuscript.  
The APC (Article Processing Charge) for this manuscript is funded by the authors.  
Not applicable.  
The data used in this study are not publicly available due to restrictions imposed by non-disclosure agreements (NDAs).  
The authors declare no conflicts of interest.