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Reliability Centred Maintenance (RCM) leverages global best practices of Operations and Maintenance combined with advanced data analytics models using AI, ML and DL. RCM helps in decision-making related to maintenance of large assets, its components, maintenance methods and schedules in order to achieve efficient O&M operations.
In comparison with Condition Based Maintenance (CBM) which bases maintenance decisions on condition of equipment, RCM provides insights based on consequences of failure mode. RCM provides an understanding of which failure modes deserve what kind of maintenance and which task needs to be redesigned. With CBM, there can be no upper limit placed on maintenance frequency. But with RCM, it is possible to reduce or limit the number of maintenance needs to be done. However, CBM can be used effectively, with the availability of smart operational and maintenance related data analytics models.
To achieve smart operations, it is critical to track the component-wise efficiency and thereby their impact on overall asset efficiency. The overall efficiency of the asset therefore depends on the efficiency of mechanical and electrical components. And to achieve smart maintenance, it is critical to predict the failure of a component well in advance by which maintenance, replacement, cranes and spares can be planned smartly. IPPs will be able to reduce revenue loss caused due to sudden failures and add credibility to their company during investor evaluation. Through preventive maintenance or well-planned replacement activities, huge savings in maintenance costs can be achieved.
As the characteristics of each asset and each component of the asset are different, we need to have a highly scalable and intelligent model, with several layers of automation using ML & DL, to address any type of component. We can get a complete understanding of the key reasons behind the efficiency loss of an asset, anomaly detection and predict failures well in advance. This helps in taking quick real-time operational and maintenance decisions so that the asset will run at its best possible efficiency with reduced downtime. In conclusion, the best O&M practices when combined with data analytics creates the right balance between increased revenue from generation and reduced cost of generation.