Bearing is an essential mechanical component in rotary machineries. To prevent its unpredicted failures and undesired downtime cost, many researches have been made in the field of Prognostics and Health Management (PHM). Key issues in bearing PHM is to establish a proper health indicator (HI) reflecting its current health state properly at the early stage. However, conventional features have shown some limitations that make them less useful for early diagnostics and prognostics. This paper proposes a feature extraction method using traditional envelope analysis and weighted sum with correlation coefficient. The developed methods are demonstrated using IMS bearing data from NASA Ames Prognostics Data Repository. In the end, proposed feature is compared with traditional time-domain features.
How to Cite
bearing fault diagnosis, Vibration, Feature extraction
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