Design & Implementation of Automatic Machine Condition Monitoring and Maintenance System in Limited Resource Situations

The different properties of the collected data are given shortly:
5. Data Types: Vibration data of three different types of faults occur in Rotating motor/devices
6. Data length: 1 seconds
7. Rotations/Second: 48.33
8. Data acquisition system: ESP32, gyro acceleration sensor, SD card, OLED display.
9. Initial data dimension: 1600 rows, 4 columns.
Initially, we computed the average of columns containing axis values at different frequencies. Subsequently, to form the dataset, we calculated the partial derivative of the averages concerning frequency, representing changes in data magnitude relative to different frequencies. This data, processed with Machine Learning models, demonstrated that different faults produce varying magnitudes at different frequencies, exhibiting distinct patterns. Structural looseness, misalignment, and bearing problems manifested differing magnitudes at different frequencies, contributing to distinct overall patterns .
Structures:
Folders (Structural_Looseness, Bearing, Misalignment) contains the Raw CSV Files containing vibration signals of faulty machines. The MatLab Files Features and Models containing the features, visualization, spectrum and trained models with the features.