부산대학교 도서관

Fault detection and diagnostics are important steps in the predictive maintenance of industrial systems, especially faults in the mechanical parts most susceptible to fail, such as bearings and gears in rotating machines. These two components represent more than 50% of causes of the operational downtime. Therefore, the detection of their appearance allows anticipating the total failure of the machine and schedule in advance maintenance actions. However, in the presence of a combined gear and bearing faults, it is difficult to isolate their states. To remedy this situation, this paper proposes a data processing methodology that exploits the three-phase current signals of the rotating machine and build a health indicator (HI) from each current phase that reveals the different health states. This indicator is constructed by extracting features from the collected raw data in frequency and time domains, and then they properly combined with a physical significance. After that, all health indicators (HIs) of the three phase current data are fed to a machine learning model for an online pattern recognition of the bearing and gear states, including the combined faults. The proposed approach is demonstrated through a test bench that studies bearing and gear defects of a gearbox under different operating conditions.