Improved gas turbine diagnostics towards an integrated prognostic approach wiht vibration and gas path analysis

The degradation of a gas turbine engine in operation is inevitable, leading to losses in performance and eventually reduction in engine availability. Several methods like gas path analysis and vibration analysis have been developed to provide a means of identifying the onset of component degradation. Although both approaches have been applied individually with successes in identifying component faults; localizing complex faults and improving fault prediction confidence are some of the further benefits that can accrue from the integrated application of both techniques. Although, the link between gas path component faults and rotating mechanical component faults have been reported by several investigators, yet, gas path fault diagnostics and mechanical fault diagnostics are still treated as separated toolsets for gas turbine engine health monitoring. This research addresses this gap by laying a foundation for the integration of gas path analysis and vibration to monitor the effect of fouling in a gas turbine compressor. Previous work on the effect of compressor fouling on the gas turbine operation has been on estimating its impact on the gas turbine’s performance in terms of reduction in thermal efficiency and output power. Another methodology often used involves the determination of correlations to characterize the susceptibility and sensitivity of the gas turbine compressor to fouling. Although the above mentioned approaches are useful in determining the impact of compressor fouling on the gas turbine performance, they are limited in the sense that they are not capable of being used to access the interaction between the aerodynamic and rotordynamic domain in a fouled gas turbine compressor. In this work, a Greitzer-type compression system model is applied to predict the flow field dynamics of the fouled compressor. The Moore-Greitzer model is a lumped parameter model of a compressor operating between an inlet and exit ii duct which discharges to a plenum with a throttle to control the flow through the compression system. In a nutshell, the overall methodology applied in this work involves the interaction of four different models, which are: Moore-Greitzer compression system model, Al-Nahwi aerodynamic force model, 2D transfer matrix rotordynamic model and a gas turbine performance engine model. The study carried out in this work shows that as the rate of fouling increases, typified by a decrease in compressor massflow, isentropic efficiency and pressure ratio, there is a corresponding increase in the vibration amplitude at the compressor rotor first fundamental frequency. Also demonstrated in this work, is the application of a Moore-Greitzer type compressor model for the prediction of the inception of unstable operation in a compressor due to fouling. In modelling the interaction between the aerodynamic and rotordynamic domain in a fouled gas turbine compressor, linear simplifications have been adopted in the compression system model. A single term Fourier series has been used to approximate the resulting disturbed flow coefficient. This approximation is reasonable for weakly nonlinear systems such as compressor operating with either an incompressible inlet flow or low Mach number compressible inlet flow. To truly account for nonlinearity in the model, further recommendation for improvement includes using a second order or two-term Fourier series to approximate the disturbed flow coefficient. Further recommendation from this work include an extension of the rotordynamic analysis to include non-synchronous response of the rotor to an aerodynamic excitation and the application of the Greitzer type model for the prediction of the flow and pressure rise coefficient at the inlet of the compressor when fouled

Evaluation of 2D Acoustic Signal Representations for Acoustic-Based Machine Condition Monitoring

Acoustic-based machine condition monitoring (MCM) provides an improved alternative to conventional MCM approaches, including vibration analysis and lubrication monitoring, among others. Several challenges arise in anomalous machine operating sound classification, as it requires effective 2D acoustic signal representation. This paper explores this question. A baseline convolutional neural network (CNN) is implemented and trained with rolling element bearing acoustic fault data. Three representations are considered, such as log-spectrogram, short-time Fourier transform and log-Mel spectrogram. The results establish log-Mel spectrogram and log-spectrogram, as promising candidates for further exploration.Peer reviewe

A Fatigue Life Assessment Methodology for Rolling-Element Bearing Under Irregular Loading

The paper presents a methodology for estimating the fatigue life of rolling-element bearing under irregular loading conditions. This method overcomes the limitations encountered by rolling-element bearing lifing models based on a constant bearing load assumption, when used in applications where bearing load varies over time with also changes in rotational speed. To include these irregular loading effects, a load-slice averaging methodology is applied to the loading history; in which the loading history is assumed to be composed of many thin slices of loading conditions. The operating conditions within each loading slice are averaged, and with the aid of linear damage rule and Lundberg-Palmgren load-life correlation for rolling-element bearings, each loading slice fatigue damage contribution is determined. The cumulative loading slice fatigue damage is used to estimate rolling-element bearing life. This approach can also be used as a tool for real-time life prognosis of rolling-element bearings. The method is demonstrated with simulated loading histories acting on a Cooper split cylindrical roller bearing and life prediction comparison is made between several approximate closed form bearing life expressions for different types of loading.Peer reviewedFinal Published versio

Adaptive Multiscale Weighted Permutation Entropy for Rolling Bearing Fault Diagnosis

© 2020 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.Bearing vibration signals contain non-linear and non-stationary features due to instantaneous variations in the operation of rotating machinery. It is important to characterize and analyze the complexity change of the bearing vibration signals so that bearing health conditions can be accurately identified. Entropy measures are non-linear indicators that are applicable to the time series complexity analysis for machine fault diagnosis. In this paper, an improved entropy measure, termed Adaptive Multiscale Weighted Permutation Entropy (AMWPE), is proposed. Then, a new rolling bearing fault diagnosis method is developed based on the AMWPE and multi-class SVM. For comparison, experimental bearing data are analyzed using the AMWPE, compared with the conventional entropy measures, where a multi-class SVM is adopted for fault type classification. Moreover, the robustness of different entropy measures is further studied for the analysis of noisy signals with various Signal-to-Noise Ratios (SNRs). The experimental results have demonstrated the effectiveness of the proposed method in fault diagnosis of rolling bearing under different fault types, severity degrees, and SNR levels.Peer reviewedFinal Accepted Versio

Diagnostics of gas turbine systems using gas path analysis and rotordynamic response approach

The modern gas turbine is plagued with issues centred on improving engine availability and limiting component degradation. The integrated use of different condition monitoring techniques presents a solution to addressing these challenges. This paper lays a foundation for the integration of gas path analysis and the rotordynamic response of the compressor to monitor the effect of fouling in the compressor. In investigating the resultant interaction between the aerodynamic and rotordynamic domain in a compressor caused by fouling, an approach involving the interaction of four different models is explored. The first model, a gas turbine engine performance model is used to simulate a fouled compressor and quantify the extent of performance deterioration with gas path analysis. The extent of performance deterioration from the engine performance model represented by scaling of the compressor maps becomes an input in the second model, a Moore-Greitzer compression system model, which evaluates the disturbed flow field parameters in the fouled compressor. The third model, a momentum-based aerodynamic force model, predicts the fouling induced aerodynamic force based on the disturbed flow field parameters. The aerodynamic force acting as a forcing function in the fourth model, a compressor rotordynamic model, produces the vibration response. From the investigation carried out in this work, it is observed, as the rate of fouling increases in the compressor, typified by a decrease in compressor massflow, pressure ratio and isentropic efficiency, there is a corresponding increase in the vibration amplitude at the first fundamental frequency of the compressor

Influence of fouling on compressor dynamics: Experimental and modelling approach

The effect of compressor fouling on the performance of a gas turbine has been the subject of several papers; however, the goal of this paper is to address a more fundamental question of the effect of fouling, which is the onset of unstable operation of the compressor. Compressor fouling experiments have been carried out on a test rig refitted with TJ100 small jet engine with centrifugal compressor. Fouling on the compressor blade was simulated with texturized paint with average roughness value of 6 microns. Compressor characteristic was measured for both the clean (baseline) and fouled compressor blades at several rotational speeds by throttling the engine with variable exhaust nozzle. A Greitzer-type compression system model has been applied based on the geometric and performance parameters of the TJ100 small jet engine test rig. Frequency of plenum pressure fluctuation, the mean disturbance flow coefficient and pressure-rise coefficient at the onset of plenum flowfield disturbance predicted by the model was compared with the measurement for both the baseline and fouled engine. Model prediction of the flowfield parameters at inception of unstable operation in the compressor showed good agreement with the experimental data. The results proved that used simple Greitzer model is suitable for prediction of the engine compressor unstable behaviour and prediction of the mild surge inception point for both the clean and the fouled compressor

A Knowledge Transfer Platform for Fault Diagnosis of Industrial Gas Turbines

The aim of this paper is to introduce the bases of an intelligent fault diagnostic platform, which assists in detecting mechanical failures of Industrial Gas Turbines (IGTs). This comprises an integration of an expert system and its complementary signal processing techniques. The essential characteristic here is not to exclude humans (experts) from the diagnostic process, but rather to transfer their knowledge and experience to a computerized platform. The automated process executed by the computerized platform is to ensure the scalability and consistency in fault diagnosis; while the humans are required to corroborate the transparency and liability of the outcomes. In this paper, a Knowledge Transfer Platform (KTP) is proposed for fault diagnosis of industrial systems. It is then designed and tested for combustion fault diagnosis using field data of IGTs. The preliminary results have revealed the feasibility and efficacy of the proposed scheme, which has the potential to be further extended to a large industrial scale and to different engineering diagnostic applications

Integrated gas turbine system diagnostics: components and sensor faults quantification using artificial neural networks

The role of diagnostic systems in gas turbine operations has changed over the past years from a single support troubleshooting maintenance to a more proactive integrated diagnostic system. This has become so, because detecting and fixing fault(s) on one gas turbine sub-system can trigger false fault(s) indication, on other component(s) of the gas turbine system, due to interrelationships between data obtained to monitor not only the GT single component, but also the integrated components and sensors. Hence, there is need for integration of gas turbine system diagnostics. The purpose of this paper is to present artificial neural network diagnostic system (ANNDS) as an integrated gas turbine system diagnostic tool capable of quantifying gas turbine component and sensor fault. A model based approach which consists of an engine model, and an associated parameter estimation algorithm that predicts the difference between the real engine data and the estimated output data is described in this paper. The ANNDS system was trained to detect, isolate and assess component(s) and sensor fault(s) of a single spool industrial gas turbine GT-PG9171ER. The ANN model was construed with multi-layer feed-forward back propagation network for component fault(s) and auto associative network for sensor fault(s). The diagnostic methodology adopted was a nested network structure, trained to handle specific objective function of detecting, isolating or quantifying faults. The data used for training, and testing purposes were obtained from a non-linear aero-thermodynamic model using PYTHIA; a Cranfield University in-house software. The data set analyzed in this paper represent samples of clean and faulty gas turbine components caused by fouling (0.5% - 6% degradation) and sensor fault(s) due to bias (±1% - ±7%). The results show the capability of ANN to detect, isolate (classification) and quantify multiple faults if properly trained

Quantitative Assessment of Damage in Composites by Implementing Acousto-Ultrasonics Technique

This study focused on quantitative damage severity assessment in composite materials using Acousto-Ultrasonics (AU), an in-service and active non-destructive inspection technique in which Lamb waves are communicated through a damaged zone. This was done by activating a signal onto the composite material surface and acquiring the received waves after their interactions with the damage. It relied on early research that presented a series of stress wave factors (SWFs) derived from the frequency-domain of the AU data, as quantitative identifiers of the received signal. Although, the SWFs have previously been proven to determine the understanding of the spatial arrangements of the impact damage, the degree or severity of the damage inside the impact damage area has not been assessed. Therefore, the current research was a step in the right way toward that aim. AU waves were generated via a laminate with increasing concentrations of ply faults, across longitudinal length. The stress wave factors were first examined for an undamaged composite, and the SWFs were then connected with the fault concentration. The significance of the found linkages and the possible futures of quantitative assessment of the degree of damage by such relationships were examined. The stress wave factors showed clear and consistent patterns, as the fault concentration increased. With a rise in fault density, an element measuring the energy content of the waves significantly changed with R- sq(adj) = 91.33% and almost linearly, and provided a robust measurable trend, while other parameter exhibited lesser shifts with R- sq(adj) = 51.86%. The result obtained from the presented work provided a base to cost-effective and in- service measure to early detection of catastrophic failures in composite structures, including the wind turbine blades for renewable and sustainable energy generation.Peer reviewe

Effects of Sensor Spacing and Material Thickness of Al 6082-T6 using Acousto-Ultrasonics Technique

© 2022 The Authors. This is an open access article distributed under the Creative Commons Attribution License, to view a copy of the license, see: https://creativecommons.org/licenses/by/4.0/Peer reviewe