Sep 2026· International Journal of Automation Technology· 0 citations· 12 references
Abstract
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.
In this paper, vibration monitoring is considered as a tool for detecting hidden defects in electric motors. The results of the development help reduce the risk of emergency shutdowns, thereby increasing the overall service life of the equipment. The product is presented as an experimental vibration monitoring system,...
A. Prosselkov, V. P. Chinishlov, P. Petrov et al.· Bulletin of Manash Kozybayev...· 0 citations
In this study the vibration behavior of synchronous belts with representative damages is investigated using a test bench and a design of experiments approach. A laser triangulation sensor is used to record the vibrations of undamaged belts and belts with artificially introduced tooth crack, tooth separation and cord da...
P. Häderle, M. Dazer· Engineering Research· 0 citations
Traditional contact-based vibration measurements can be affected by sensor installation and mechanical coupling, which complicates feature extraction for dry-type transformer core diagnosis. This study evaluates a fault-diagnosis workflow combining laser Doppler vibrometry (LDV), Gramian angular field (GAF) encoding...
Chun-Hua Fang, Yu-Kai Li, Lan Jiang et al.· Engineering Research Express· 0 citations
In response to the problems of insufficient measurement accuracy and poor reliability in the traditional manual detection method for the contact pressure of high-speed railway relays, this paper proposes a relay contact pressure state detection system based on STM32. The system uses a thin-film pressure sensor to adher...
Zhuo-Hang Li, Xiao-Lin Wang· Twelfth International Sympos...· 0 citations
The designed system successfully improved installation safety and reduced the risk of equipment damage and fire and was developed using an ESP32 microcontroller integrated with a PZEM-004T sensor, a DHT22 sensor, and an MQ-2 sensor.
I. M. D. P. Putra, Nardi Nardi, Dibyo Susanto et al.· Internet of Things and Artif...· 0 citations
Vibration analysis is a widely used technique for machine condition monitoring, since mechanical faults often introduce characteristic spectral components in inertial signals. This paper presents a low-cost embedded spectrum analyzer for vibration monitoring based on an ADXL345 digital accelerometer and the BitDogLab o...
Edson Costa Oliveira, B. Masiero, Fabiano Fruett· 2026 10th International Symp...· 0 citations
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.