A novel degree of observability used for measurement selections in gas path diagnostics

A novel method for measurement selections of gas path diagnostics has been developed. This method is based on the singular value decomposition of the observability matrix of linear systems, which are a good approximation of the nonlinear ones for small deviations. It also employs the concept of the degree of observability to formulate the criteria. The states with high degree of observability and the measurement sets with high overall degree of observability result in high estimation accuracy in gas path diagnostics. A heavy-duty gas turbine model is used to validate this method. The influence of the gas turbine nonlinearity, the measurement noise, and the overdetermined measurement on degree of observability is analyzed. The overall degree of observability is calculated for different measurement sets of heavy-duty gas turbine. The gas path diagnostics simulations with different measurement sets using the weighted least-squares estimation method and the extended Kalman filter are conducted. The quality of gas path diagnostics simulation with different measurement sets is assessed and the results demonstrate the capability of the developed method for measurement selections in gas path diagnostics.