Houman Hanachi

A Physics-Based Modeling Approach for Performance Monitoring in Gas Turbine Engines

Performance deterioration monitoring is an essential part of the prognostics and health management (PHM) of gas turbine engines (GTEs). This paper proposes a physics-based modeling approach for performance deterioration monitoring with two model-based performance indicators, heat loss index and power deficit index, for GTE PHM applications. A comprehensive nonlinear thermodynamic model for a single shaft GTE is developed to establish the relation between the operating conditions and the cycle parameters. The model, once properly calibrated, is able to predict the GTE cycle parameters in a healthy condition as the baseline, while in reality, the measured parameters gradually deviate from the baseline, which reflects the performance deterioration of the GTE. To represent the degradation level, the heat loss index is defined as the normalized measure of the thermal power that is being wasted in the GTE compared to the healthy condition. Similarly, the power deficit index is defined as the deficiency ratio of the GTE output power due to the performance deterioration. The effectiveness of the performance indicators in monitoring performance deterioration and their robustness to the variations of the operating conditions are examined by using three years of typical operating data of an industrial GTE. The results clearly reveal the trends of both the short term recoverable deterioration due to fouling effects in the compressor, and the long term non-recoverable deterioration caused by structural degradation. The technique is especially advantageous for prognostic applications where there is no access to internal cycle parameters of a GTE, and only the operating data are available, hence no additional sensors are required.

A framework with nonlinear system model and nonparametric noise for gas turbine degradation state estimation

Modern health management approaches for gas turbine engines (GTEs) aim to precisely estimate the health state of the GTE components to optimize maintenance decisions with respect to both economy and safety. In this research, we propose an advanced framework to identify the most likely degradation state of the turbine section in a GTE for prognostics and health management (PHM) applications. A novel nonlinear thermodynamic model is used to predict the performance parameters of the GTE given the measurements. The ratio between real efficiency of the GTE and simulated efficiency in the newly installed condition is defined as the health indicator and provided at each sequence. The symptom of nonrecoverable degradations in the turbine section, i.e. loss of turbine efficiency, is assumed to be the internal degradation state. A regularized auxiliary particle filter (RAPF) is developed to sequentially estimate the internal degradation state in nonuniform time sequences upon receiving sets of new measurements. The effectiveness of the technique is examined using the operating data over an entire time-between-overhaul cycle of a simple-cycle industrial GTE. The results clearly show the trend of degradation in the turbine section and the occasional fluctuations, which are well supported by the service history of the GTE. The research also suggests the efficacy of the proposed technique to monitor the health state of the turbine section of a GTE by implementing model-based PHM without the need for additional instrumentation.

Bladed disc crack diagnostics using blade passage signals

One of the major potential faults in a turbo fan engine is the crack initiation and propagation in bladed discs under cyclic loads that could result in the breakdown of the engines if not detected at an early stage. Reliable fault detection techniques are therefore in demand to reduce maintenance cost and prevent catastrophic failures. Although a number of approaches have been reported in the literature, it remains very challenging to develop a reliable technique to accurately estimate the health condition of a rotating bladed disc. Correspondingly, this paper presents a novel technique for bladed disc crack detection through two sequential signal processing stages: (1) signal preprocessing that aims to eliminate the noises in the blade passage signals; (2) signal postprocessing that intends to identify the crack location. In the first stage, physics-based modeling and interpretation are established to help characterize the noises. The crack initiation can be determined based on the calculated health monitoring index derived from the sinusoidal effects. In the second stage, the crack is located through advanced detrended fluctuation analysis of the preprocessed data. The proposed technique is validated using a set of spin rig test data (i.e. tip clearance and time of arrival) that was acquired during a test conducted on a bladed military engine fan disc. The test results have demonstrated that the developed technique is an effective approach for identifying and locating the incipient crack that occurs at the root of a bladed disc.

Effects of Humidity Condensation on the Trend of Gas Turbine Performance Deterioration

Performance deterioration in gas turbine engines (GTEs) depends on various factors in the ambient and the operating conditions. For example, humidity condensation at the inlet duct of a GTE creates water mist, which affects the fouling phenomena in the compressor and varies the performance. In this paper, the effective factors on the short-term performance deterioration of a GTE are identified and studied. GTE performance level is quantified with two physics-based performance indicators, calculated from the recorded operating data from the control system of a GTE over a full time between overhaul (TBO) period. A regularized particle filtering (RPF) framework is developed for filtering the indicator signals, and an adaptive neuro-fuzzy inference system (ANFIS) is then trained with the filtered signals and the effective ambient and the operating conditions, i.e., the power, the air mass flow, and the humidity condensation rate. The trained ANFIS model is then run to simulate the GTE performance deterioration in different conditions for system identification. The extracted behavior of the system clearly shows the dependency of the trend of performance deterioration on the operating conditions, especially the humidity condensation rate. The developed technique and the results can be utilized for GTE performance prediction, as well as for suggesting the optimum humidity supply at the GTE intake to control the performance deterioration rate.

Prediction of Compressor Fouling Rate Under Time Varying Operating Conditions

Performance of the compressors deteriorates due to detrimental effects of fouling on the aerodynamic flow characteristic. The compressors need periodic clean up services to re-gain the designed performance. Apart from the operating time, the ambient and the operating conditions affect the fouling phenomenon making accurate scheduling for predictive maintenance very difficult. In this work, the symptoms of compressor fouling are captured through the evolution of the compressor map in terms of loss of isentropic efficiency and mass flow decrease. Compressor mass flow and the rate of humidity condensation at the inlet of the compressor are identified as the effective factors on the fouling rate. Humidity condensation has a competing effect on fouling rate; increment of the condensed humidity up to a certain level accelerates the fouling rate, while additional mist has an inverse effect. The complex effect of the condensed humidity along with the air mass flow is extracted through training an adaptive neuro-fuzzy inference system. The resulting model reveals how the efficiency and the mass flow of the compressor map vary as a result of fouling development, given the mass flow and the humidity condensation history. The methodology is verified using data from a similar compressor commissioned at a different period.Copyright

Effects of the Intake Air Humidity on the Gas Turbine Performance Monitoring

Gas turbine engines (GTEs) are extensively used in locations with high humidity such as offshore platforms. However, in the dry regions, GTEs are often equipped with water spray inlet coolers for warm seasons. In both cases, the moisture affects the thermodynamic properties of the intake air and drifts the performance off the dry condition, especially during the warm days, when the moisture content of the air is high and the inlet air cooler is operational. In this paper, a detailed steady state model is proposed to simulate the GTE performance with the humid air, and it is linked with a thermodynamic model to quantify the total moisture content of the air after the cooler. The developed framework is used to analyze the operating data of a GTE during the three years of service. The results are then utilized for model-based performance monitoring of the GTE, using a recently introduced performance indicator. A comparative analysis is performed between the results received from the primary model overlooking the humidity effects, and the developed enhanced performance model with humidity effects. A better accuracy for the performance indicator was observed where the enhanced model is employed, suggesting the importance of considering the intake air humidity for model-based performance monitoring.Copyright

Enhancement of prognostic models for short-term degradation of gas turbines

Deposition and congestion of foulants in the compressor section of gas turbine engines (GTE) degrades the compressor and leads to performance deterioration of the GTE at the system level. Compressor fouling may develop over a short time, but it is recoverable by washing and cleaning. Reliable prediction of the fouling as a function of time is helpful for planning compressor wash services. In this work, the fouling state is parametrized as the relative change of the ratio of the compressor mass flow and efficiency against ideal conditions. A regression-based prognostic model is developed to predict the fouling state as a function of time. In the next step, an adaptive neuro-fuzzy inference system (ANFIS) is developed that considers the rate of humidity condensation at the inlet of the compressor for the prognostic model. The performance of the developed models is evaluated with recorded operating data from a GTE in a power plant. The study shows that enhancement of the prognostic model may be accomplished by taking into account the effects of humidity on the rate of fouling and results in an improvement in the prognostic accuracy.

Simulation of ultrasonic testing for resolution of corrosion detection in pipes

Ultrasonic testing is a conventional non-destructive test technique widely used in the industry. In this paper, we study ultrasonic testing for detection of internal corrosion of the thin-walled pipes. When the reflective surface is too close to the probe, ultrasonic excitation over a large area leads to interference in the reflection wave. This puts a limitation on the probe size, given the expected accuracy of the measurement. This paper investigates the relation between the accuracy and the probe size, using Finite Element Method (FEM) for simulation of ultrasound wave propagation.

Effects of sampling decimation on a gas turbine performance monitoring

Monitoring the performance of gas turbine engines (GTEs) by sampling the operating parameters of the GTEs is the central part of the GTEs health management program. The rate of data sampling and the consequent analyses of the sampled data are restricted to the available resources. It especially appears as a principal constraint where the data is manually logged by the operators. In a recent research work, a physics-based approach and resulting performance indicators, i.e., “Heat Loss index” and “Power Deficit index” were introduced by the authors to monitor the health state of the gas turbines using only the readings from the GTE operating system. Statistical estimation approach was taken to establish prediction models for performance indicators. This study provides a quantitative analysis for the effect of sampling decimation on the accuracy of the developed predictor within a time window. Consequently, it provides an insight into the performance prediction uncertainty, in connection with the sampling frequency and the length of the observation window on which the model is established.

Bladed disk crack detection through advanced analysis of blade time of arrival signal

Health condition monitoring and fault diagnostics of turbo fan engines play significant roles in overall cost reduction and reliability enhancement of the aircraft system. Among various types of potential faults in a turbo fan engine, crack initiation and propagation in the bladed disks of engines caused by high-cycle fatigue under cyclic loads are typical ones that could result in the breakdown of the engines if not detected at an early stage. Reliable fault detection techniques are therefore required to detect impending engine malfunctions as well as unexpected failures that could otherwise lead to costly and/or catastrophic consequences. Although a number of approaches have been reported in literature, it still remains very challenging to develop a reliable technique to accurately estimate the health condition of bladed disks of engines. As such, this paper presents a new technique for engine bladed disk crack detection through advanced analysis of blade time-of-arrival signal. Two stages of signal processing are involved in this technique: 1) signal preprocessing for removing the noise caused by rotor imbalance; and 2) signal post-processing for identifying the location of the crack. The effectiveness of the developed technique is validated experimentally in a spin rig test.