Gas Turbine Diagnostics Research Articles

In situ multi-perspective scanning of 3D particle deposition on flat plates with film cooling and determination of practical model parameters

A practical strategy for three-dimensional morphology measurement in the particle deposition process is of great importance to deepen the understanding of deposition physical processes. Due to the methodology limitations, it is difficult to obtain the formation process of deposition morphology. In this work, an in situ multi-perspective scanning (MPS) method using structured light was developed to capture the formation process of deposits. The point cloud registration algorithm was adopted to reconstruct the deposition morphology. Based on the validation results of Vernier caliper, the MPS method provides an uncertainty of ±0.056 mm at 4 mm. To verify the potential application of in situ measurement in gas turbine diagnostics, MPS method was preliminarily applied to the flat plate test with film cooling in particle-laden flow. The application demonstrated that this method successfully reconstructed the deposition morphology on a flat plate in the presence of film cooling. Furthermore, the obtained morphology results revealed that deposition roughness lied in the 0.35–0.56 mm and increased with an increase of blowing ratio. Additional comparisons with film cooling effectiveness showed the deposition distribution was related to the film cooling process. The detailed three-dimensional (3D) deposition morphology determined the mean curvature and mass of deposits, which may improve the prediction accuracy of deposition models in numerical simulations.

Performance diagnostics of gas turbines operating under transient conditions based on dynamic engine model and artificial neural networks

Gas turbine engines are machines of high complexity and non-linearity. Interpreting the vast amount of data from a gas turbine and converting them into customer value requires the combination of domain knowledge and modern computational intelligence tools. Reaching to an accurate and reliable diagnosis of gas turbines is a process that is becoming increasingly complex. The engines are now expected to operate in more dynamic conditions to compensate for the intermittent nature of renewables. Transient operating conditions will accelerate the deterioration of gas turbine components which motivates the development of new methods and tools to cope with such type of information. In this paper, we propose a novel performance diagnostic method for gas turbines that combines a dynamic engine model with artificial neural networks (ANN). An engine model of a two shaft gas turbine has been developed in MATLAB/Simulink and used by a family of ANNs to detect the degradation of the engine operating under transient conditions, when all of its components are experiencing degradation. The conducted case studies consider various degradation scenarios. The advantage of the proposed method is that it deals effectively with both fixed and time-evolving degradation. Furthermore, in cases where there is limited amount of data for training ANNs the model can fill this gap by simulating plethora of scenarios that can potentially extend the applicability of ANNs to gas turbine diagnostics. The proposed method could be used as a tool for supporting the operation and maintenance activities of gas turbines.

Special issue on all‐electric and hybrid‐electric flight

Special issue on all‐electric and hybrid‐electric flight

AI-Based Exhaust Gas Temperature Prediction for Trustworthy Safety-Critical Applications

Data-driven condition-based maintenance (CBM) and predictive maintenance (PdM) strategies have emerged over recent years and aim at minimizing the aviation maintenance costs and environmental impact by the diagnosis and prognosis of aircraft systems. As the use of data and relevant algorithms is essential to AI-based gas turbine diagnostics, there are different technical, operational, and regulatory challenges that need to be tackled in order for the aeronautical industry to be able to exploit their full potential. In this work, the machine learning (ML) method of the generalised additive model (GAM) is used in order to predict the evolution of an aero engine’s exhaust gas temperature (EGT). Three different continuous synthetic data sets developed by NASA are employed, known as New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS), with increasing complexity in engine deterioration. The results show that the GAM can be predict the evolution of the EGT with high accuracy when using several input features that resemble the types of physical sensors installed in aero gas turbines currently in operation. As the GAM offers good interpretability, this case study is used to discuss the different data attributes a data set needs to have in order to build trust and move towards certifiable models in the future.

Sensor Fault/Failure Correction and Missing Sensor Replacement for Enhanced Real-time Gas Turbine Diagnostics

Gas turbine sensors are prone to bias and drift. They may also become unavailable due to maintenance activities or failure through time. It is, therefore, important to correct faulty signal or replace missing sensors with estimated values for improved diagnostic solutions. Coping with a small number of sensors is the most difficult to achieve since this often leads to underdetermined and indistinguishable diagnostic problems in multiple fault scenarios. On the other hand, installing additional sensors has been a controversial issue from cost and weight perspectives. Gas path locations with difficult conditions to install sensors is also among other sensor installation related challenges. This paper proposes a sensor fault/failure correction and missing sensor replacement method. Auto-regressive integrated moving average models are employed to correct measurements from faulty and failed sensors. To replace additional sensors needed for further diagnostic accuracy improvements, neural network models are devised. The performance of the developed approach is demonstrated by applying to a three-shaft turbofan engine. Test results verify that the method proposed can well-recover measurements from faulty/failed sensors, no matter with small or major failures. It can also compensate key missing temperature and pressure measurements on the gas path based on the data from other available sensors.

Aircraft Engine Performance Monitoring and Diagnostics Based on Deep Convolutional Neural Networks

The rapid advancement of machine-learning techniques has played a significant role in the evolution of engine health management technology. In the last decade, deep-learning methods have received a great deal of attention in many application domains, including object recognition and computer vision. Recently, there has been a rapid rise in the use of convolutional neural networks for rotating machinery diagnostics inspired by their powerful feature learning and classification capability. However, the application in the field of gas turbine diagnostics is still limited. This paper presents a gas turbine fault detection and isolation method using modular convolutional neural networks preceded by a physics-driven performance-trend-monitoring system. The trend-monitoring system was employed to capture performance changes due to degradation, establish a new baseline when it is needed, and generatefault signatures. The fault detection and isolation system was trained to step-by-step detect and classify gas path faults to the component level using fault signatures obtained from the physics part. The performance of the method proposed was evaluated based on different fault scenarios for a three-shaft turbofan engine, under significant measurement noise to ensure model robustness. Two comparative assessments were also carried out: with a single convolutional-neural-network-architecture-based fault classification method and with a deep long short-term memory-assisted fault detection and isolation method. The results obtained revealed the performance of the proposed method to detect and isolate multiple gas path faults with over 96% accuracy. Moreover, sharing diagnostic tasks with modular architectures is seen as relevant to significantly enhance diagnostic accuracy.

Assessment of Dynamic Bayesian Models for Gas Turbine Diagnostics, Part 2: Discrimination of Gradual Degradation and Rapid Faults

There are many challenges that an effective diagnostic system must overcome for successful fault diagnosis in gas turbines. Among others, it has to be robust to engine-to-engine variations in the fleet, it has to discriminate between gradual deterioration and abrupt faults, and it has to identify sensor faults correctly and be robust in case of such faults. To combine their benefits and overcome their limitations, two diagnostic methods were integrated in this work to form a multi-layer system. An adaptive performance model was used to track gradual deterioration and detect rapid or abrupt anomalies, while a series of static and dynamic Bayesian networks were integrated to identify component degradation, component abrupt faults, and sensor faults. The proposed approach was tested on synthetic data and field data from a single-shaft gas turbine of 50 MW class. The results showed that the approach could give acceptable accuracy in the isolation and identification of multiple faults, with 99% detection and isolation accuracy and 1% maximum error in the identified fault magnitude. The approach was also proven robust to sensor faults, by replacing the faulty signal with an estimated value that had only 3% error compared to the real measurement.

Assessment of Dynamic Bayesian Models for Gas Turbine Diagnostics, Part 1: Prior Probability Analysis

The reliability and cost-effectiveness of energy conversion in gas turbine systems are strongly dependent on an accurate diagnosis of possible process and sensor anomalies. Because data collected from a gas turbine system for diagnosis are inherently uncertain due to measurement noise and errors, probabilistic methods offer a promising tool for this problem. In particular, dynamic Bayesian networks present numerous advantages. In this work, two Bayesian networks were developed for compressor fouling and turbine erosion diagnostics. Different prior probability distributions were compared to determine the benefits of a dynamic, first-order hierarchical Markov model over a static prior probability and one dependent only on time. The influence of data uncertainty and scatter was analyzed by testing the diagnostics models on simulated fleet data. It was shown that the condition-based hierarchical model resulted in the best accuracy, and the benefit was more significant for data with higher overlap between states (i.e., for compressor fouling). The improvement with the proposed dynamic Bayesian network was 8 percentage points (in classification accuracy) for compressor fouling and 5 points for turbine erosion compared with the static network.

A methodology for identifying the most suitable measurements for engine level and component level gas path diagnostics of a micro gas turbine

Recently, the utilization of micro gas turbines in smart grids are rising that makes the part-load operation principal situation of the engine service. This leads to faster life consumption that increases the importance of the diagnostics process. Gas path analysis is an effective method for gas turbine diagnostics. Complex dynamics of gas turbine induces challenging conditions to perform applicable gas path analysis. This study aims to facilitate MGT gas path diagnostics through reducing the number of monitoring parameters and preparation a pattern for engine level and component level health assessment in both full and part load operation of a recuperated micro gas turbine. To attain this goal a model is proposed to simulate MGT off-design performance which is validated against experimental data in healthy and degraded operation modes. Fouling in compressor, turbine and recuperator and erosion in compressor and turbine as the most common degradations in the gas turbine are considered. The fault simulation is performed by changing the health parameters of gas path components. According to the result investigation, a matrix comprises deviation contours of four parameters, Power, fuel flow, compressor discharge pressure, and exhaust gas temperature is presented and analyzed. The analysis shows that monitoring these parameters makes it possible to perform engine level and component level diagnostics through evaluating a binary code (generated by mentioned parameter variations) against the fault effects pattern in different load fractions and fault severities. The simulation also showed that the most power drop occurred under the compressor fouling by about 8.7% while the most reduction in thermal efficiency is observed under recuperator fouling by about 7.84%. Furthermore, the investigation showed the maximum decrease in the surge margin induced by the compressor fouling during the lower part-load operation by about 45.7% while in the higher loads created by the turbine fouling by about 14%.

Detection of Unit of Measure Inconsistency in gas turbine sensors by means of Support Vector Machine classifier

The reliability of gas turbine diagnostics clearly relies on reliable measurements. However, raw data reliability can be corrupted by label noise issues, as for instance an erroneous association between data and the respective unit of measure. Such issue, rarely investigated in the literature, is named Unit of Measure Inconsistency (UMI).Machine Learning classifiers are suitable tools to tackle the challenge of UMI detection. Thus, this paper investigates the capability of four Support Vector Machine approaches to detect UMIs. All approaches are tested on a dataset composed of field data taken on a fleet of Siemens gas turbines.The results of this study demonstrate that the Radial Basis Function with One-vs-One decomposition allows higher diagnostic accuracy.

Improved performance of gas turbine diagnostics using new noise‐removal techniques

Improved performance of gas turbine diagnostics using new noise‐removal techniques

Fault detection and isolation of gas turbine engine using inversion-based and optimal state observers

This paper aims to design, develop and compare fault detection and isolation (FDI) schemes including inversion-based fault reconstruction and optimal state observers for a class of nonlinear system subject to concurrent faults and unknown disturbances that represents the nonlinear dynamic model of a gas turbine engine. Towards this end, for each fault, utilizing an existing coordinate transformation, the original system is transformed into a new form in which both observers are applied for fault diagnosis. The coordinate transformation comes from observability concept in differential geometry. The inversion-based observer is highly beneficial for straightforward detection and directly isolating the faults, and the state observer is optimal with respect to the magnitude of observer gain and convergence time. The mentioned approaches are implemented for FDI of a gas turbine model subject to compressor efficiency, compressor mass flow capacity, and actuator faults in addition to an unknown disturbance. The simulation results illustrate that the proposed FDI schemes are a promising tool for the gas turbine diagnostics.

A Review of Information Fusion Methods for Gas Turbine Diagnostics

The correct and early detection of incipient faults or severe degradation phenomena in gas turbine systems is essential for safe and cost-effective operations. A multitude of monitoring and diagnostic systems were developed and tested in the last few decades. The current computational capability of modern digital systems was exploited for both accurate physics-based methods and artificial intelligence or machine learning methods. However, progress is rather limited and none of the methods explored so far seem to be superior to others. One solution to enhance diagnostic systems exploiting the advantages of various techniques is to fuse the information coming from different tools, for example, through statistical methods. Information fusion techniques such as Bayesian networks, fuzzy logic, or probabilistic neural networks can be used to implement a decision support system. This paper presents a comprehensive review of information and decision fusion methods applied to gas turbine diagnostics and the use of probabilistic reasoning to enhance diagnostic accuracy. The different solutions presented in the literature are compared, and major challenges for practical implementation on an industrial gas turbine are discussed. Detecting and isolating faults in a system is a complex problem with many uncertainties, including the integrity of available information. The capability of different information fusion techniques to deal with uncertainty are also compared and discussed. Based on the lessons learned, new perspectives for diagnostics and a decision support system are proposed.

Effects of Compressor Fouling and Compressor Turbine Degradation on Engine Creep Life Consumption

The effect of engine degradation in the form of compressor fouling and compressor turbine degradation on the creep life consumption of the high-pressure (HP) turbine blades of an LM2500+ industrial gas turbine engine is investigated in this work. The degradations are flow capacity degradation and isentropic efficiency degradation. An engine model was created in Cranfield gas turbine performance and diagnostics software, pythia. Blade thermal and stress models were developed together with the Larson–Miller parameter (LMP) method for creep life analysis. The percentage decreases in creep life due to each effect were examined. For the engine considered, compressor degradation has more impact on engine creep life toward peak power operation, while HP turbine degradation has more impact on creep life at lower power levels. The results of this work will give engine operators an idea of how engine components creep life is consumed and make reasonable decisions concerning operating at part loads.

Development of Reliable NARX Models of Gas Turbine Cold, Warm, and Hot Start-Up

This paper documents the setup and validation of nonlinear autoregressive network with exogenous inputs (NARX) models of a heavy-duty single-shaft gas turbine (GT). The data used for model training are time series datasets of several different maneuvers taken experimentally on a GT General Electric PG 9351FA during the start-up procedure and refer to cold, warm, and hot start-up. The trained NARX models are used to predict other experimental datasets, and comparisons are made among the outputs of the models and the corresponding measured data. Therefore, this paper addresses the challenge of setting up robust and reliable NARX models, by means of a sound selection of training datasets and a sensitivity analysis on the number of neurons. Moreover, a new performance function for the training process is defined to weigh more the most rapid transients. The final aim of this paper is the setup of a powerful, easy-to-build and very accurate simulation tool, which can be used for both control logic tuning and GT diagnostics, characterized by good generalization capability.

Denoising Signals Used in Gas Turbine Diagnostics with Ant Colony Optimized Weighted Recursive Median Filters

Accurate fault detection and isolation requires signal processing of measurement signals which are contaminated with noise. Typically, gas turbine faults are revealed by sharp trend shifts in the signals and these trend shifts should be preserved during signal processing. Linear filters can smooth out the sharp trend shifts while removing noise. However, nonlinear filters such as the weighted recursive median (WRM) filters show good noise reduction while preserving key signal features if their integer weights are determined optimally. We propose the ant colony optimization (ACO) method coupled with local search to calculate the integer weights of WRM filters. It is found that the filter weight optimization problem is mathematically equivalent to the quadratic assignment problem which can be solved by ACO. Optimal parameters for the ACO are found using numerical experiments. The WRM filter is demonstrated for abrupt and gradual faults in gas turbines and is found to yield noise reduction of 52–64% for simulated noisy signals considered in this paper.

Gas turbine gas path diagnostics: A review

In this competitive business world one way to increase profitability of a power production unit is to reduce the operation and maintenance expenses. This is possible if the gas turbine availability and reliability is improved using the appropriate maintenance action at the right time. In that case, fault diagnostics is very critical and effective and advanced methods are essential. Gas turbine diagnostics has been studied for the past six decades and several methods are introduced. This paper aims to review and summarise the published literature on gas path diagnostics, giving more emphasis to the recent developments, and identify advantages and limitations of the methods so that beginners in diagnostics can easily be introduced. Towards this end, this paper, identifies various diagnostic methods and point out their pros and cons. Finally, the paper concludes the review along with some recommended future works.

Method of gas turbine malfunction diagnostics using hybrid intellectual systems

Methods of improving the safety and efficiency of gas turbines are analyzed. The existing diagnosticmethodologies and software for compression stations are reviewed. Lists of significant diagnostics parameters,typical faults and their causes are compiled. A model of a hybrid intellectual system of gas turbine fault diagnosisbased on artificial neural network and fuzzy inference is introduced. The fuzzy inference method which allowsprocessing an arbitrary number of intermediate variables and transitive relations is described. Moreover, themethod makes it possible to take into account the inaccuracies in expert knowledge not only in describing thefacts of an application domain but also in cause-and-effect relations between them. The implementation of theproposed model and the method in the system of taking decisions that makes it possible to reveal both the presenceand character of the faults and their possible causes is described. The proposed system makes it possible toimprove the accuracy and completeness of gas turbine diagnostics, which, in its turn, will increase the labor safetyfor the compression station staff as well as the promptness of repair and maintenance. Thus, the application ofthe system may have a positive effect on the service life and economic feasibility of gas turbines.

A component map tuning method for performance prediction and diagnostics of gas turbine compressors

In this paper, a novel compressor map tuning method is developed with the primary objective of improving the accuracy and fidelity of gas turbine engine models for performance prediction and diagnostics. A new compressor map fitting and modeling method is introduced to simultaneously determine the best elliptical curves to a set of compressor map data. The coefficients that determine the shape of the compressor map curves are analyzed and tuned through a multi-objective optimization scheme in order to simultaneously match multiple sets of engine performance measurements. The component map tuning method, that is developed in the object oriented Matlab Simulink environment, is implemented in a dynamic gas turbine engine model and tested in off-design steady state and transient as well as degraded operating conditions. The results provided demonstrate and illustrate the capabilities of our proposed method in refining existing engine performance models to different modes of the gas turbine operation. In addition, the excellent agreement between the injected and the predicted degradation of the engine model demonstrates the potential of the proposed methodology for gas turbine diagnostics. The proposed method can be integrated with the performance-based tools for improved condition monitoring and diagnostics of gas turbine power plants.

A More Realistic Scheme of Deviation Error Representation for Gas Turbine Diagnostics

Gas turbine diagnostic algorithms widely use fault simulation schemes, in which measurement errors are usually given by theoretical random number distributions, like the Gaussian probability density function. The scatter of simulated noise is determined on the basis of known information on maximum errors for every sensor type. Such simulation differs from real diagnosis because instead of measurements themselves the diagnostic algorithms work with their deviations from an engine baseline. In addition to simulated measurement inaccuracy, the deviations computed for real data have other error components. In this way, simulated and real deviation errors differ by amplitude and distribution. As a result, simulation-based investigations might result in too optimistic conclusions on gas turbine diagnosis reliability. To understand error features, deviations of real measurements are analyzed in the present paper. To make error presentation more realistic, it is proposed to extract an error component from real deviations and to integrate it in fault description. Finally, the effect of the new noise representation mode on diagnostic reliability is estimated. It is shown that the reliability change due to inexact error simulation can be significant.