Experimental vibration data collected for a belt drive system under different operating conditions

Area of Science:

• Mechanical Engineering
• Condition Monitoring
• Data Science

Background:

• Vibration analysis is crucial for monitoring the health of machinery.
• Belt drive systems are common in industrial applications and prone to various faults.
• Understanding vibration signatures is key to diagnosing operational issues.

Purpose of the Study:

• To present a comprehensive dataset of vibration signals from a belt drive system.
• To facilitate research in vibration-based condition monitoring techniques.
• To enable the diagnosis of belt drive system anomalies under different operating conditions.

Main Methods:

• Experimental setup involving a belt drive system with controlled speed and belt pretension.
• Data collection of vibration signals under normal, unbalanced, and faulty belt conditions.
• System operation at low, medium, and high speeds with three distinct pretension levels.

Main Results:

• A dataset comprising vibration signals under varied operational parameters and fault conditions.
• Characterization of vibration patterns associated with different speeds, tensions, and system health states.
• Provides empirical data for developing and validating fault detection algorithms.

Conclusions:

• The collected dataset is valuable for advancing vibration-based condition monitoring of belt drives.
• The data enables a deeper understanding of how operational parameters influence system vibration.
• Facilitates the development of robust anomaly detection and diagnostic tools for industrial machinery.