Equipment degradation monitoring for sustained reliability

Equipment Health Monitoring through Predictive Maintenance (PDM) data is proposed and the same is demonstrated with case study. Steel Rolling Mill Gearbox is considered for the purpose. Empirical failure rate model using Equipment Health Index (EHI) is proposed and used it for forecasting maintenance requirements of the process equipment. The strength of the proposed approach lies in integrating multiple condition indicators simultaneously to assess the equipment health and estimate empirical average failure rate, which offers a quick look at the maintenance requirements in the near future. Dynamic trending of EHI along with empirical failure rate offers a two pronged approach in ascertaining reliability assurance of the process equipment. The approach mentioned does not replace existing practice of health monitoring, but tries to integrate various predictive technologies to arrive at straight forward one step approach for simultaneous multi-indicator monitoring; a step towards better reliability through apt maintenance decisions. This approach strengthens the expert analysis and advanced vibration signature analysis etc., to contribute to the success of the PDM. Effective PDM in large process industry can save 10-15% of maintenance expenditure. A customized online application with the proposed approach can be developed on a desktop computer with real time data acquisition.