###### Abstract
Unlike the traditional Bayesian methods that require a significant level of hand-crafted discriminative and/or non-discriminative feature extraction as the case study demands, these intelligent algorithms--deep learning (DL) models--are designed to accept raw sensor inputs and follow a series of advanced mathematical processes for automatic feature extraction and predictive modeling in a comprehensive framework with less dependence on domain knowledge. Nonetheless, finding a balance between computational costs and accuracy remains a challenge, especially for real-time cases where both factors play major roles in the decision-making, selection, and deployment. Although highly efficient, the accuracy of these intelligent algorithms for discriminative tasks are sometimes threatened by a high similarity in the sensor signals across different tool health conditions [5; 14]. Unfortunately, this is often the case of micro drill bit regrinding machines because the grinding wheel wear conditions are usually mixed with the high-frequency components with low amplitudes, and that makes it hard to identify/isolate. This opens up the need for spectral isolation--extraction of signals within a finite frequency band from a mixed signal. Interestingly, different signal processing tools abound for frequency-domain and/or time-frequency-domain analyses; however, the fast Fourier transform (FFT) retains its traditional advantages--computational cost advantage, user-friendly visualization for inferences, minimal modeling assumptions for spectral isolation, and support for power spectral density (PSD) computation for improved signal analyses/processing [7; 15; 16]. This has motivated us to explore its efficiencies alongside intelligent classifiers for vibration-based wear monitoring of high-frequency automatic regrinding in-line equipment. The following highlights are the major contributions of this study:  
* A multi-sensor vibration monitoring system is proposed for the automatic micro drill bit regrinding of in-line equipment. The proposed framework adopts spectral isolation by integrating the low-frequency vibration responses from the regrinding frame and the high-frequency vibration responses from the gas bearing-powered regrinding spindle in a comprehensive manner. These provide highly discriminate information from the regrinding frame and the regrinding spindle, respectively, for improved condition monitoring. * A multi-option diagnostic framework that exploits the multiple sensor data's vulnerabilities is proposed. The framework offers the options of choosing different data sources--stand-alone and/or integrated sensor data and exploits different 1D-CNN and MLP model architectures. This presents an avenue for evaluating the efficiencies of spectral isolation for improved tool wear monitoring and for assessing the computational cost implications of employing the proposed intelligent monitoring technology. * For the proposed study, we leverage experimental data from an ultra-precision micro drill bit automatic regrinding in-line system (ARIS), which regrinds micro drill bits (\(\phi\)0.15-\(\phi\)0.075 mm) used in the PCB manufacturing process. Empirical and descriptive conclusions are drawn following extensive investigations and evaluations. Our research offers a reliable framework for future research and practice in real-time industrial monitoring/diagnostic applications. The remainder of this paper is structured as follows: Section 2 discusses motivation for the proposed study and related Works, while Section 3 provides the the background of study. Section 4 presents the experimental assessment, while Section 5 concludes the paper.