A New Feature Selection-Aided Observer for Sensor Fault Diagnosis of an Industrial Gas Turbine

In complex systems, observers play a vital role in fault diagnosis and performance monitoring. Hence, the accuracy of these observers is very important. In this paper, an alternative solution to improve the observer accuracy is investigated. For this purpose, a feature selection-aided observer is proposed. This observer utilizes a feature-selection scheme that augments the sensor data with its time-domain features to observe the system. A decision tree algorithm selects the features needed for the observer, which act as soft sensors. In this scheme, a sliding mode observer is integrated with the decision tree method to select features for attaining more information from sensors (extracting two outputs from each sensor) which causes to increase the number of the observer equations. To implement this method, an augmented model for providing the statistical information is defined. To assess the performance of the introduced algorithm, an industrial twin shaft gas turbine is considered. In this model, the big data of the SGT 600 gas turbine is broken down into numerous data sets with a predefined sample time, then the subspace algorithm (N4SID) is utilized to obtain a state-space model by mean and variance of the inputs and outputs. Moreover, the algorithm is used to detect and diagnose sensors fault with a novel decision logic. Simulation results illustrate that the proposed observer is accurate and reliable for monitoring performance and diagnosing faults and failures.