Condition monitoring systems can detect abnormal states of mechanical equipment in the early stages of failure, preventing further deterioration of faults and avoiding unnecessary losses. Rolling bearings are vulnerable components in mechanical equipment. This paper focuses on the research and development of a condition monitoring system for rolling bearings. Existing monitoring techniques often involve periodically uploading monitoring data. Most of the data uploaded throughout the entire lifespan of a rolling bearing represents normal operation, leading to a waste of resources. This paper analyzes the lifecycle of rolling bearings. Considering that the degradation phase of rolling bearings accounts for a small proportion of their overall lifespan, a novel rolling bearing condition monitoring system is proposed. This system provides real-time monitoring of rolling bearings, uploading collected data only when abnormalities are detected in the rolling bearing. A monitoring device is designed based on the requirements of the condition monitoring system.

2.2 Rolling Bearing Fault Diagnosis Methods

Currently, most research on rolling bearing condition monitoring and fault diagnosis utilizes vibration signal data, and vibration analysis methods are widely applied. Correspondingly, acoustic monitoring technology uses sound signals generated by rolling bearings during operation to determine their operational status. However, extracting bearing fault features becomes challenging when environmental noise and other machine noise interfere with the equipment. In contrast, analyzing vibration signals is suitable for various operating conditions.

Fault diagnosis can determine if equipment has a fault through simple diagnostics. Simple diagnostics typically involve calculating time-domain feature values from rolling bearing vibration signals and then determining if the rolling bearing is operating normally based on these values. Time-