1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

A review on machinery diagnostics and prognostics implementing condition-based maintenance

Condition monitoring of wind turbines: Techniques and methods

http://staff.www.ltu.se/~lassew/ene/Avd%20Energiteknik/Publikationer/2004/Energy_humid%20GT_041025.pdf

Humidified gas turbines—a review of proposed and implemented cycles

Rotating instabilities (RI) have been observed in axial flow fans, centrifugal compressors as well as in low-speed and high-speed axial compressors. They are responsible for the excitation of high amplitude rotor blade vibrations and noise generation. This flow phenomenon moves relative to the rotor blades and causes periodic vortex separations at the blade tips and an axial reversed flow through the tip clearance of the rotor blades.The paper describes experimental investigations of RI in the Dresden Low-Speed Research Compressor (LSRC). The objective is to show that the fluctuation of the blade tip vortex is responsible for the origination of this flow phenomenon.RI have been found at operating points near the stability limit of the compressor with relatively large tip clearance of the rotor blades. The application of time-resolving sensors in both fixed and rotating frame of reference enables a detailed description of the circumferential structure and the spatial development of this unsteady flow phenomenon, which is limited to the blade tip region.Laser-Doppler-Anemometry (LDA) within the rotor blade passages and within the tip clearance as well as unsteady pressure measurements on the rotor blades show the structure of the blade tip vortex.It will be shown that the periodical interaction of the blade tip vortex of one blade with the flow at the adjacent blade is responsible for the generation of a rotating structure with high mode orders, termed as rotating instability (RI).Copyright © 2000 by ASME

Rotating Instabilities in an Axial Compressor Originating From the Fluctuating Blade Tip Vortex

Greater use is being made of dynamic simulation of energy systems as a design tool for selecting control strategies and establishing operating procedures. This paper discusses the dynamic modeling of a gas-fired combined-cycle power plant with a gas turbine, a steam turbine, and an alternator-all rotating on a common shaft. A waste-heat boiler produces steam at two pressures using heat from the gas turbine flus gas. The transient behavior of the system predicted by the model for various upset situations appears physically reasonable and satisfactory for the operating constraints

Adaptive Simulation of Gas Turbine Performance

A method for solving the gas path analysis problem of jet engine diagnostics based on a probabilistic approach is presented. The method is materialized through the use of a Bayesian Belief Network (BBN). Building a BBN for gas turbine performance fault diagnosis requires information of a stochastic nature expressing the probability of whether a series of events occurred or not. This information can be extracted by a deterministic model and does not depend on hard to find flight data of different faulty operations of the engine. The diagnostic problem and the overall diagnostic procedure are first described. A detailed description of the way the diagnostic procedure is set-up, with focus on building the BBN from an engine performance model, follows. The case of a turbofan engine is used to evaluate the effectiveness of the method. Several simulated and benchmark fault case scenarios have been considered for this reason. The examined cases demonstrate that the proposed BBN-based diagnostic method composes a powerful tool. This work also shows that building a diagnostic tool, based on information provided by an engine performance model, is feasible and can be efficient as well.

Bayesian Network Approach for Gas Path Fault Diagnosis

A common feature of all Differential Gas Path Analysis methods is the necessity of measuring a number of performance variables greater or at least equal to the number of diagnostic parameters which have to be estimated. Discrete Operating Conditions Gas Path Analysis (DOCGPA) is an extended version of the conventional GPA algorithms, providing-among other things-the capability to overcome this problem. In the present paper, we describe how this method can be coupled with an engine computer model, in order to perform component directed fault diagnosis. Application to a commercial turbofan engine demonstrates the effectiveness of the proposed method. Nomenclature [C] = influence coefficient matrix EPR = engine pressure ratio / = scalar defined in Eq. (8) k = number of different operating points [M] = information matrix m = number of unknown component parameters n = number of measured variables [P] = covariance matrix of unknown components parameters m = number of unknown component parameters PEUI = performance estimation uncertainty index (equal to J) [R] = covariance matrix of measures variables tr{ } =' trace of a matrix u = operating condition vector x = component parameter vector y = measurement vector A( ) = percentage change from an initial value 2 = summation Superscripts T = transpose matrix -1 = inverse matrix

Jet Engine Fault Detection with Discrete Operating Points Gas Path Analysis

The diagnostic ability of probabilistic neural networks (PNN) for detecting sensor faults on gas turbines is examined. The structure and the features of a PNN, for sensor fault detection, are presented. It is shown that with the proposed formulation, a powerful tool for sensor fault identification is produced. A particular feature of the PNN produced is the ability to detect sensor faults even in the presence of engine component malfunction, as well as on deteriorated engines. In such situations, the size of bias that can be identified increases. The way to establish the limits of sensor bias that can be detected is presented along with results from application to test cases with realistic noise magnitudes. The diagnostic procedure proposed here is also supported by an engine performance model. The data used for setting up and testing the PNN are generated by such a model.

Setting up of a probabilistic neural network for sensor fault detection including operation with component faults

A method is presented allowing the simulation of Gas Turbine performance with the possibility of adapting to engine particularities. Measurements along the gas path are used, in order to adapt a given performance model by appropriate modification of the component maps. The proposed method can provide accurate simulation for engines of the same type, differing due to manufacturing or assembly tolerances. It doesn’t require accurate component maps, as they are derived during the adaptation process. It can also be used for health monitoring purposes, introducing thus a novel approach for component condition assessment. The effectiveness of the proposed method is demonstrated by application to an industrial Gas Turbine.Copyright © 1989 by ASME

/pdf/adaptive-simulation-of-gas-turbine-performance-8917x5exke.pdf

K. Mathioudakis

Papers

Adaptive Simulation of Gas Turbine Performance

Bayesian Network Approach for Gas Path Fault Diagnosis

Jet Engine Fault Detection with Discrete Operating Points Gas Path Analysis

Setting up of a probabilistic neural network for sensor fault detection including operation with component faults

Adaptive simulation of gas-turbine performance