As one of the most asset-intensive industry, power generation plant's business competitiveness strongly rely on asset availability, reliability and performance. At B.Grimm Power plants in Thailand, there has been the need for shortening plant outage duration from extending time-based major overhaul schedule of critical asset (such as steam turbine) without jeopardizing its reliability and performance. Therefore, the robust justification approach is required for making critical decision.

The major challenge was found that traditional asset condition monitoring capability in-place seems insufficient to detect early sign of failures at machines running in dynamic conditions. Thanks to advanced predictive analytics technology, AI have been developed from machine historian data, so that it can early detect the incident occurred before. Turbomachinery diagnosis from raw vibration data have been additionally conducted to investigate the potential defect area in depth.

As a result, it was justified that the strategy can be optimized leading to huge benefits major overhaul schedule extended with safe and reliable operations until next planned plant outage schedule. Long term partnership program has been also implemented to enable B.Grimm Power staffs sustain the platform by themselves.