In-process measurement of changes in grinding wheel condition is expected to prevent machining defects. A method for monitoring grinding wheel condition through grinding vibration analysis was examined in this study. To achieve this, a measurement device was developed by housing two accelerometers, a compact microcontroller, a wireless transmitter, and two batteries in an acrylic case mounted on the grinding wheel, enabling vibration acquisition and wireless transmission during operation. Two measurement methods were implemented in the developed system. The first is a raw data transmission method, which measures biaxial acceleration at a sampling frequency of 24 kHz and simultaneously transmits the data to a personal computer (PC). The second is an edge computing method, which calculates anomaly scores using a neural network within the microcontroller and transmits only the results. The advantage of the raw data transmission method is that it allows various analyses using the raw acceleration data received by the PC. However, the disadvantage is the heavy load on the network due to the large volume of data transmitted. In contrast, the edge computing method significantly reduces data volume and power consumption by transmitting only anomaly scores, thereby extending battery life. Experiments were conducted using a surface grinding machine. In the raw data transmission method, it was found that the integrated value of the absolute acceleration measured by the developed device strongly correlates with the magnitude of the grinding force. This result indicates that the developed device can estimate the grinding force. In the edge computing method, the anomaly scores calculated within the microcontroller correlated with the grinding force. This confirms that the developed device can estimate the grinding force using this method as well. Furthermore, it was demonstrated that this method can detect abnormalities in wheel rotational speed, changes in wheel condition, and the occurrence of grinding burn.

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