###### Abstract  
Despite the increasing digitalization of equipment diagnostic/condition monitoring systems, it remains a challenge to accurately harness discriminant information from multiple sensors with unique spectral (and transient) behaviors. High-precision systems such as the automatic regrinding in-line equipment provide intelligent regrinding of micro drill bits; however, immediate monitoring of the grinder during the grinding process has become necessary because ignoring it directly affects the drill bit's life and the equipment's overall utility. Vibration signals from the frame and the high-speed grinding wheels reflect the different health stages of the grinding wheel and can be exploited for intelligent condition monitoring. The spectral isolation technique as a preprocessing tool ensures that only the critical spectral segments of the inputs are retained for improved diagnostic accuracy at reduced computational costs. This study explores artificial intelligence-based models for learning the discriminant spectral information stored in the vibration signals and considers the accuracy and cost implications of spectral isolation of the critical spectral segments of the signals for accurate equipment monitoring. Results from one-dimensional convolutional neural networks (1D-CNN) and multi-layer perceptron (MLP) neural networks, respectively, reveal that spectral isolation offers a higher condition monitoring accuracy at reduced computational costs. Experimental results using different 1D-CNN and MLP architectures reveal 4.6% and 7.5% improved diagnostic accuracy by the 1D-CNNs and MLPs, respectively, at about 1.3% and 5.71% reduced computational costs, respectively. intelligent monitoring; spectral isolation; deep learning; grinding wheel wear; vibration signals +
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the drilling process. More often than not, these drill bit failures can be traced to the high-frequency vibrations generated during the drilling process; therefore, the demand regular tool monitoring and maintenance--regularly sharpening [7]. Most investigations on the vibration of micro drilling are focused on only drill self-structure. However, it is also important to monitor the maintenance efficiency of the drill bit regrinding machine to ensure that at all times, the drill bits produce the desired surface finish and geometrical accuracy of the finished workpiece--PCB. Several direct, indirect, and/or hybrid tool monitoring techniques/technologies have been studied [8; 9]. Direct methods such as optical devices and cameras, although more reliable for accurate monitoring, are faced with certain limitations. Their real-time usability may be affected since ARIS wheels rotate at high speeds and may produce unclear images in real-time, which often require interrupting the machine. Further, finding an optimal camera localization point is another major challenge. Against these limitations, indirect methods--cutting force, acoustic emission, spindle motor, temperature, vibration, machining sound, etc.--offer real-time efficiencies and the integration of multiple sensing opportunities for fully extracting deep and shallow machine behavior for optimal condition monitoring [7]. In addition, recent technological advancements have ushered in intelligent algorithms and the super computational resources for harnessing them--for intelligent tool monitoring [4]. These intelligent algorithms, such as convolutional neural networks (CNN) [5], recurrent neural networks (RNN), auto encoders (AE) [10], and MLPs [11], are quite robust for diverse predictive maintenance and equipment monitoring purposes and have been reported in many studies, including but not limited to hydraulic equipment monitoring [5], remaining useful life estimation, wear detection [7], and machinery fault diagnostics and prognostics [12; 13].