Early Intervention: How VIB360 Prevented a Critical Bearing Failure in a Biomass Power Turbine
- Customer: Energy Biomass Power Generation
- Asset: 7.5 MW MAN DIESEL and TURBO Steam Turbine and gearbox assembly (GTA)
- Location: Orleans, France
The Situation
Initial Observation:
Fluctuations of up to 7 RPM were observed in the turbine’s rotational speed during operation. This deviation was significantly beyond normal control limits, indicating abnormal dynamic behavior rather than routine control variation.
Detection Method:
The VIB360 torsional vibration monitoring system was installed on the turbine using a speed sensor (MPU). Upon commissioning, the system immediately captured the anomaly in real time, enabling early identification of the issue.
Detection or Event:
Change Detected: The system recorded speed fluctuations (Delta RPM) of 7.00.
Quantified Detection: Shock Index level reached 2.90 and 2.89, and dynamic overloads in the bearings were identified.
Trigger for Action: The system triggered a “50% Attention” alert due to these instabilities and the presence of a specific harmonic distortion (Order 3).
Insection Findings & Action by Customer
Physical Findings: Analysis confirmed speed instability likely linked to the regulation valves.
Root Cause: Indicators suggested a localized weakness, such as potential erosion in the turbine blades, identified through the Vib360 data and High harmonic distortion.
Customer has acknowledged the Vib360 readings in correlation with the physical findings and root cause analysis and taken further actions.
Outcome/Value:
Avoided Events: Early detection of valve instability and blade weaknesses prevented potential critical bearing failure or unplanned shutdown.
Operational Benefit: The move from manual one-time checks to 24/7 online monitoring allowed for real-time health tracking and proactive maintenance planning.
Key Takeaway:
Torsional vibration monitoring with Vib360 goes beyond traditional condition monitoring.
It acts as a decision-making platform, enabling operators to:
- Detect subtle dynamic instabilities early
- Understand the mechanism behind faults
- Take targeted corrective actions before damage occurs
Instead of reacting to failures, teams can actively prevent them through informed, real-time decisions.