Diagnostics of Anomaly Steam Turbine Behavior in terms of Remote SHM and Cloud Computing

Abstract

Monitoring of structural health is the important task in terms of ensuring thereliability of the operated technology. It is important especially in case of the steamturbine operation where the unplanned outage is associated with the high financiallosses.The conventional approach for the evaluation of the normal turbine operation isbased on precise specification of the trigger limits for each of the measured signals madeby the technical expert. The abnormal operation is then determined when one of thesignals is outside of the limits. However, it is a non-trivial task to specify trigger limitsfor all measured signals and sometimes it is even not practicable. The method describedin this paper is based on the automatic detection of anomalies in the turbine behaviorwithout the need to precisely specify the trigger limits manually.The approach is based on two steps. The first is to find the behavior of the turbinethat is related to the normal turbine operation. The signal trigger limits are evaluatedautomatically with probabilistic assessment. The second step is to investigate the actualoperation state using the signal measurements. Then if the actual behavior of the turbineis out of the boundary of the normal turbine operation, the anomaly is detected.The described method was validated using the measurement data acquired inoperation of a steam turbine with a nominal power of hundreds MW. As an example ofthe method application, the paper shows the detection of an anomaly, which wassubsequently identified as a contact between the stator and the rotor turbine part. Thiscontact is potentially dangerous because it can change the structure of the machine.The described method is being integrated into the remote monitoring system that isbased on cloud computing and being developed by the authors of this paper. The remotesensing of the turbine operation is nowadays the key to reduce costs in terms ofmaintaining the installed system and frequent visits of technical personal support tocollect the data. The architecture of the monitoring system itself is described in thepaper. The system is important in terms of providing an early warning in case of anunexpected behavior of the turbine. This provides the maintainability and reliability ofthe operated technology. Nowadays more than 30 operated turbines are part of thismonitoring system worldwide.