ABSTRACT
In the previous chapters, we focused on filters for removing non-Gaussian noise and outliers from gas turbine signals. The present chapter showcases the Kalman filter for fault detection and isolation techniques in gas turbines. The goal of gas turbine performance diagnostics is to accurately detect, isolate, and assess the changes in engine module performance, engine system malfunctions, and instrumentation problems from knowledge of measured parameters taken along the engine’s gas path. Discernable shifts in engine speed, temperature, pressure, fuel flow, etc., provide the requisite information for determining the underlying shift in engine operation from a presumed nominal state. Historically, this type of analysis was performed through the use of a Kalman filter or one of its derivatives to simultaneously estimate several engine faults. The present chapter outlines the Kalman filter methodology, its relative merits and weaknesses. Some basic background on the Kalman filter and the related weighted least-squares approach have been provided in Chapter 1. The typical use of the Kalman filter in Chapter 1 was for gas path analysis, which is also called multiple-fault analysis. In this approach, the Kalman filter distributes the measurement shifts among a variety of module efficiencies, flow capacities, and areas. In this chapter, the Kalman filter will be used to solve the problem of isolating a single fault to the component level. The single faults under consideration include the engine modules, engine system, and instrumentation faults. The use of the Kalman filter as a single-fault isolator was proposed by Volponi, Depold, Ganguli, and Daguang, and the present chapter is largely based on this paper [12]. Furthermore, the use of the Kalman filter for sensor fault estimation will also be discussed in this chapter.
