## 5 Conclusions and Future Works  
In this study, a vibration-based multi-sensor intelligent wear monitoring framework is proposed to solve the compound health diagnosis of high-frequency micro drill bit ARIS using spectral isolation and deep learning models. Because DL models are popular for disparaging the hectic (and less efficient) process of manual hand-crafted feature extraction process with automated (and more reliable) efficiencies, we exploited the 1D-CNN and MLP models on the vibration signals acquired from the frame and spindle of the regrinding unit of a micro drill bit ARIS (model TGM-1011) for monitoring the wear severity of the grinding wheel. A major contribution of the study is the spectral isolation module, which helps prioritize only the critical spectral segments of the vibration signals via an FFT-based signal processing technique for improved monitoring accuracies at reduced computational costs. Results from deploying the DL models on the different input/source data channels--raw and critical segments--of the signals reveal 4.6% and 7.5% improved diagnostic accuracy by the 1D-CNNs and MLP s, respectively, at about 1.3% and 5.71% reduced computational costs, respectively. Moreover, the study shows that as the models become more shallow, the false alarm rate of predictions becomes higher; especially when using the raw vibration signals. While the sensors under the spindle provide more resourceful information for monitoring, the sensors on the frame provide supplementary information for a more comprehensive monitoring process and are recommended (even though the overall accuracy of using the spindle only is marginally higher). On the extreme side, the study reveals that using very deep models, such as the FCN, comes with significant computational costs regardless of the data sources employed and should be considered for cases where computational resources are a major decision-making factor. While a more shallow model (in comparison with the FCN) offers better computational efficiencies, its optimal use may rely on the proposed spectral isolation module to minimize the probability of false predictions. While the findings validate the efficiencies of the proposed monitoring framework, the inherent issues of optimal sensor selection still pose a significant concern because accelerometers may not be the most appropriate for the equipment. No doubt, alternative sensors, such as acoustic emission sensors and cameras, may offer better monitoring solutions, either as stand-alone or hybrid sensing mechanisms. Although our choice of accelerometers was fundamentally motivated by their strong magnetic properties, cost efficiency, sensor localization efficiencies, and the comprehensive monitoring opportunities offered by combining the measurements from the spindle and frame (which, by the way, are major advantages over the alternative sensing methods since the regrinding is always in motion), we believe accelerometers only provide inferred knowledge of the regrinding unit as mechanical responses (which may be compromised by other unrecognizable sources). The authors agree that the use of indirect monitoring methods, such as vibration sensors, may not be as efficient as direct monitoring methods. However, our research offers significant success in the proposed case study, which unfortunately has not been studied in the past. We believe our study provides a strong paradigm for bench-marking our continued research in the case study as we continue to investigate other sensing methods for improved health monitoring. On the flip side, we believe cameras are more befitting since they can better provide direct information on the grinding wheels' health states for a more accurate wear monitoring. However, its real-time usability may be affected since the wheels rotate at high speeds and may produce unclear images in real-time. Further, finding an optimal camera localization point is another major challenge. These lapses present opportunities for continued research in the domain and have motivated our continued research.