Sensor Fault/Failure Correction and Missing Sensor Replacement for Enhanced Real-time Gas Turbine Diagnostics

Abstract

Gas turbine sensors are prone to bias and drift. They may also become unavailable due to maintenance activities or failure through time. It is, therefore, important to correct faulty signal or replace missing sensors with estimated values for improved diagnostic solutions. Coping with a small number of sensors is the most difficult to achieve since this often leads to underdetermined and indistinguishable diagnostic problems in multiple fault scenarios. On the other hand, installing additional sensors has been a controversial issue from cost and weight perspectives. Gas path locations with difficult conditions to install sensors is also among other sensor installation related challenges. This paper proposes a sensor fault/failure correction and missing sensor replacement method. Auto-regressive integrated moving average models are employed to correct measurements from faulty and failed sensors. To replace additional sensors needed for further diagnostic accuracy improvements, neural network models are devised. The performance of the developed approach is demonstrated by applying to a three-shaft turbofan engine. Test results verify that the method proposed can well-recover measurements from faulty/failed sensors, no matter with small or major failures. It can also compensate key missing temperature and pressure measurements on the gas path based on the data from other available sensors.