## 4 Experimental Assessment
### Data Acquisition and Spectral Isolation  
For the proposed study, seven (7) high-sensitivity accelerometers were mounted on the grinding unit at different locations--four (4) under the two high-speed spindles and three (3) on the X-, Y-, and Z-axes of the top frame. Figure 3 shows the actual equipment and the sensor positions. The accelerometers were connected to two NI 9228 modules affixed to a NI cDAQ 9178, and with the help of a LabView software, the digital vibration signals were collected at a sampling rate of 1 KHz. Three different pairs of grinding wheels corresponding to the three major health stages of the wheels are labeled--healthy (new pair of grinding wheels), fairly used (about 40-50% life remaining useful life left for the wheel pair), and faulty (about 0 - 10% remaining useful life left for the wheel pair). Figure 4 shows a picture of the wheels at the different health stages. It can be observed from Figure 4a-c that the grinding surface of the wheels wears down due to prolonged usage. This obviously affects their grinding performance and may damage the micro drill bits as the wheel surface wear intensifies. For each of the wheel conditions, vibration signals were collected and stored in their respective folders and employed on the proposed intelligent wear monitoring framework. Figure 5 presents a view of the raw vibration signals, while Figure 6 shows the spectra comparison of the signals from the different wheel health stages. Figure 4: Pictures of the grinding wheel at (**a**) healthy/normal state, (**b**) fairly used state—about 40–50% life remaining useful, and (**c**) faulty state—about 0–10% life remaining useful. Figure 3: A picture of the micro drill bit ARIS (model TGM-1011) with the sensor locations and data acquisition system. It is observed from Figure 5a-d that the regrinding spindles/wheels' responses are concentrated in the higher frequency bands (between 0 and 1000 Hz) while the frame's responses are concentrated in the lower frequency bands (between 1500 and 2000 Hz). This presents the opportunity for isolating the critical segments to minimize computational costs, improve diagnostic accuracy, and minimize classifier confusion. Our study proposes the FFT-based spectral isolation technique, and was deployed on the signals. Figure 7 shows the critical segments of the respective components (spindle and frame), while Figure 8 shows their corresponding time-domain signal outputs after the spectral isolation process. Figure 5: Raw vibration signals for (**a**) spindle at healthy/normal state, (**b**) spindle at fairly used state, (**c**) spindle at faulty state, (**d**) frame at healthy/normal state, (**e**) frame at fairly used state, and (**f**) frame at faulty state. Figure 6: Spectral comparison of different grinder health states. (**a**) Spindle, (**b**) frame. As shown in Figure 7, it can be observed that the discriminative in the magnitudes for the different grinder health states is obvious. Moreover, it can be observed in Figure 8 that the signals' amplitudes are reduced while preserving the important periodic components of the signals. By harnessing the discriminative information in these critical segments, the diagnostic efficiencies of a classifier can be further improved. More often than not, in cases where the discriminator is poor in the inputs (such as in the case when the raw signals are used), a classifier with basic/simple architecture may produce false predictions, and this often motivates the deepening of the architecture of classifiers in the hopes that it might extract higher-level features. Unfortunately, issues such as over-fitting and increased computational costs may arise. Beyond the advantages of isolating the critical segments for improved diagnostic performance (even for classifiers with basic/simple architecture), the computational costs of the diagnostic process would be significantly reduced.