Rahul Srinivas Prabhu

System to monitor blade health in axial flow compressors

Detection of incipient cracks and crack propagation in rotor blades during operation of gas compressors is an important problem from the perspective of safety, reliability, availability, and maintenance of gas turbines. In this paper we present the architecture of a blade health monitoring system that has been deployed to monitor rotor blades of the gas compressors. The system operates continuously, 24×7, with minimal human intervention to monitor a fleet of gas compressors all around the world. The compressors are retrofitted with non-contact magnetic clearance sensors to generate Blade Passing Signals (BPS) during the rotation of the blades. These signals are acquired by a Data Acquisition (DAQ) box. The DAQ extracts the Time of Arrival (TOA) of the blades and stores them. The TOA data is transferred to a central system for feature extraction at regular intervals. The extracted features - natural frequencies of predetermined modes of vibration and static deflection are used to determine the health of the rotating blades and generate alarms for the Monitoring and Diagnostics (M&D) team. The M&D team monitors the health of blades using a web portal that provides visualization of data, features, and alarms.

Estimation of static deflection under operational conditions for blade health monitoring

Reliability and availability of gas turbines are critical to industry as they are used in a variety of applications. It is important to determine the health of these complex systems in near-real time and detect failures as early as possible. Cracks in compressor blades, used in gas turbines, if not detected early, could lead to catastrophic failures that would result in increased costs and extended outages. These cracks tend to induce changes in the static deflection of the blade. By tracking the changes in static deflection, it is possible to determine whether a blade is cracked or not. However, the static deflection of the blade, under field conditions, is also affected by operational parameters of the gas turbine. Therefore, estimating static deflection under field conditions and isolating the effects of crack from that of operational parameters is a significant task. This paper describes an algorithm for the estimation of static deflection under field conditions. It discusses some of the challenges involved and possible solutions for mitigating them. This algorithm can be used in conjunction with a crack propagation model to determine crack size in real-time.