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Machines
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Diagnostics and Optimization of Gas Turbine
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Diagnostics and Optimization of Gas Turbine

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 25611

Special Issue Editors

Prof. Dr. Konstantinos Kyprianidis

E-Mail Website
Guest Editor
Energy Engineering, Mälardalen University, Future Energy Center, 1, 722 20 Vasteras, Sweden
Interests: energy & environment; process control; system analysis; design optimization; mechanical & gas turbine engineering; aerospace; defence
Special Issues, Collections and Topics in MDPI journals
Dr. Valentina Zaccaria

E-Mail Website
Guest Editor
School of Business Society and Engineering, Division of Automation in Energy and Environmental Enigineering, Mälardalen University, 1, 722 20 Västerås, Sweden
Interests: automatic control; model predictive control; diagnostics

Special Issue Information

Dear Colleagues,

The future trends in the energy market, the increasing penetration of renewable sources, and the need for alternative fuels mean that gas turbines need to operate more flexibly and in advanced/hybrid configurations. At the same time, the variability of energy prices in the current diverse scenario increases the need to reduce operation and ownership costs. This poses new challenges in all phases of engine life: design, manufacturing, operations, monitoring, and maintenance.

Modern computing capabilities and the progress in artificial intelligence and machine learning allow for more and more complex tools. New methods are required for operations optimization, and for engine monitoring, diagnostics, and prognostics. Furthermore, methods for incorporating considerations in diagnostics during the design phase and for the integration of diagnostics, control, and optimization are of interest.

This Special Issue is meant to cover topics related to mathematical modeling, machine learning, optimization, and numerical methods aiming at improving reliability and flexibility, extending the lifetime, and reducing the operating costs of gas turbine systems. Envisaged applications include industrial gas turbines, micro gas turbines, aero-engines, and gas turbines in hybrid configurations.

Prof. Konstantinos Kyprianidis
Dr. Valentina Zaccaria
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • diagnostics 
  • machine learning 
  • prognostics and lifetime estimation models 
  • maintenance optimization 
  • operations optimization and optimal control 
  • optimization under uncertainty 
  • sensors and data fusion

Published Papers (10 papers)

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Research

17 pages, 8924 KiB  
Article
Optimization of Hammer Peening Process for Gas Turbine Rotor Straightening
by Taewung Kim and Taehyung Kim
Machines 2022, 10(10), 950; https://doi.org/10.3390/machines10100950 - 19 Oct 2022
Cited by 1 | Viewed by 1469
Abstract
Some rotors are bent permanently due to high operating temperatures, repeated transition periods, and so on. Rotors with large deformations often require straightening processes. The goal of this study is to develop a method to determine the optimal locations and strengths of hammer [...] Read more.
Some rotors are bent permanently due to high operating temperatures, repeated transition periods, and so on. Rotors with large deformations often require straightening processes. The goal of this study is to develop a method to determine the optimal locations and strengths of hammer peening for straightening gas turbine rotors. A set of parametric hammer peening simulations were performed for various dimensions of straight rotors and peening locations. The deformed geometries of the rotor from the parametric simulations were presented as curvature vectors. These curvature vectors were fitted using an empirical function. For a given initial geometry of the rotor and hammer peening plans, the post-peening geometry of the rotor was predicted by superimposing the initial curvature and newly induced curvature. An optimization statement was defined to determine a set of hammer peening locations and strengths. Constraints were imposed to exclude areas where hammer peening could not be performed such as locations for bearings. The proposed method provides an optimal hammer peening plan for the given runout data. The proposed method was validated against a series of hammer peening test results for a simple shaft. The developed method can be applied to other types of rotor straightening methods such as hot spotting. Full article
(This article belongs to the Special Issue Diagnostics and Optimization of Gas Turbine)
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18 pages, 5516 KiB  
Article
Design of Gas Turbine Cooling System Based on Improved Jumping Spider Optimization Algorithm
by Tianyi Li, Yanmei Liu and Zhen Chen
Machines 2022, 10(10), 909; https://doi.org/10.3390/machines10100909 - 08 Oct 2022
Cited by 1 | Viewed by 1247
Abstract
The gas turbine cooling system is a complex MIMO system with a strong coupling, nonlinear, time-varying and large disturbance amplitude. In order to automatically control the target flow, target temperature and pipeline pressure, in this paper, the decoupler and regulator of a gas [...] Read more.
The gas turbine cooling system is a complex MIMO system with a strong coupling, nonlinear, time-varying and large disturbance amplitude. In order to automatically control the target flow, target temperature and pipeline pressure, in this paper, the decoupler and regulator of a gas turbine cooling system are designed. Firstly, the working principle of a gas turbine cooling system and the coupling between the controlled variables of the system are analyzed. The decoupler of the system is designed by using the diagonal matrix decoupling method. The transfer function models of the coupling system are built through system identification, and the decoupling matrix of the system is calculated according to the diagonal matrix decoupling method and transfer function models. Then, the engine cooling control system simulation model is constructed and an improved jumping spider optimization algorithm is proposed. The parameters of the controller are optimized by the improved jumping spider optimization algorithm. Finally, the control system simulation is done and compared with the jumping spider optimization algorithm and the particle swarm optimization algorithm. The simulation results show that the improved jumping spider optimization algorithm is more suitable for the multivariable strong coupling nonlinear engine cooling system. For the flow and pressure control, the transient time and overshoot are reduced, and the steady-state error is less than 1%. For the temperature control, the result of the improved jumping spider optimization algorithm is more smooth, without overshoot, and almost does not exceed the set inlet water temperature. The overshoot, steady-state errors and transient time of the system have been improved, which proves the feasibility and significance of the improved jumping spider optimization algorithm by comparing the control performance and optimization time. Full article
(This article belongs to the Special Issue Diagnostics and Optimization of Gas Turbine)
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17 pages, 3439 KiB  
Article
Uncertainty Quantification for Full-Flight Data Based Engine Fault Detection with Neural Networks
by Matthias Weiss, Stephan Staudacher, Jürgen Mathes, Duilio Becchio and Christian Keller
Machines 2022, 10(10), 846; https://doi.org/10.3390/machines10100846 - 23 Sep 2022
Cited by 2 | Viewed by 1914
Abstract
Current state-of-the-art engine condition monitoring is based on a minimum of one steady-state data point per flight. Due to the scarcity of available data points, there are difficulties distinguishing between random scatter and an underlying fault introducing a detection latency of several flights. [...] Read more.
Current state-of-the-art engine condition monitoring is based on a minimum of one steady-state data point per flight. Due to the scarcity of available data points, there are difficulties distinguishing between random scatter and an underlying fault introducing a detection latency of several flights. Today’s increased availability of data acquisition hardware in modern aircraft provides continuously sampled in-flight measurements, so-called full-flight data. These full-flight data give access to sufficient data points to detect faults within a single flight, significantly improving the availability and safety of aircraft. Artificial neural networks are considered well suited for the timely analysis of an extensive amount of incoming data. This article proposes uncertainty quantification for artificial neural networks, leading to more reliable and robust fault detection. An existing approach for approximating the aleatoric uncertainty was extended by an Out-of-Distribution Detection in order to take the epistemic uncertainty into account. The method was statistically evaluated, and a grid search was performed to evaluate optimal parameter combinations maximizing the true positive detection rates. All test cases were derived based on in-flight measurements of a commercially operated regional jet. Especially when requiring low false positive detection rates, the true positive detections could be improved 2.8 times while improving response times by approximately 6.9 compared to methods only accounting for the aleatoric uncertainty. Full article
(This article belongs to the Special Issue Diagnostics and Optimization of Gas Turbine)
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19 pages, 2029 KiB  
Article
Optimal Classifier to Detect Unit of Measure Inconsistency in Gas Turbine Sensors
by Lucrezia Manservigi, Mauro Venturini, Enzo Losi, Giovanni Bechini and Javier Artal de la Iglesia
Machines 2022, 10(4), 228; https://doi.org/10.3390/machines10040228 - 24 Mar 2022
Cited by 6 | Viewed by 1973
Abstract
Label noise is a harmful issue that arises when data are erroneously labeled. Several label noise issues can occur but, among them, unit of measure inconsistencies (UMIs) are inexplicably neglected in the literature. Despite its relevance, a general and automated approach for UMI [...] Read more.
Label noise is a harmful issue that arises when data are erroneously labeled. Several label noise issues can occur but, among them, unit of measure inconsistencies (UMIs) are inexplicably neglected in the literature. Despite its relevance, a general and automated approach for UMI detection suitable to gas turbines (GTs) has not been developed yet; as a result, GT diagnosis, prognosis, and control may be challenged since collected data may not reflect the actual operation. To fill this gap, this paper investigates the capability of three supervised machine learning classifiers, i.e., Support Vector Machine, Naïve Bayes, and K-Nearest Neighbors, that are tested by means of challenging analyses to infer general guidelines for UMI detection. Classification accuracy and posterior probability of each classifier is evaluated by means of an experimental dataset derived from a large fleet of Siemens gas turbines in operation. Results reveal that Naïve Bayes is the optimal classifier for UMI detection, since 88.5% of data are correctly labeled with 84% of posterior probability when experimental UMIs affect the dataset. In addition, Naïve Bayes proved to be the most robust classifier also if the rate of UMIs increases. Full article
(This article belongs to the Special Issue Diagnostics and Optimization of Gas Turbine)
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24 pages, 4213 KiB  
Article
Steady-State Fault Detection with Full-Flight Data
by Matthias Weiss, Stephan Staudacher, Duilio Becchio, Christian Keller and Jürgen Mathes
Machines 2022, 10(2), 140; https://doi.org/10.3390/machines10020140 - 16 Feb 2022
Cited by 6 | Viewed by 3454
Abstract
Aircraft engine condition monitoring is a key technology for increasing safety and reducing maintenance expenses. Current engine condition monitoring approaches use a minimum of one steady-state snapshot per flight. Whilst being appropriate for trending gradual engine deterioration, snapshots result in a detrimental latency [...] Read more.
Aircraft engine condition monitoring is a key technology for increasing safety and reducing maintenance expenses. Current engine condition monitoring approaches use a minimum of one steady-state snapshot per flight. Whilst being appropriate for trending gradual engine deterioration, snapshots result in a detrimental latency in fault detection. The increased availability of non-mandatory data acquisition hardware in modern airplanes provides so-called full-flight data sampled continuously during flight. These datasets enable the detection of engine faults within one flight by deriving a statistically relevant set of steady-state data points, thus, allowing the application of machine-learning approaches. It is shown that low-pass filtering before steady-state detection significantly increases the success rate in detecting steady-state data points. The application of Principal Component Analysis halves the number of relevant dimensions and provides a coordinate system of principal components retaining most of the variance. Consequently, clusters of data points with and without engine fault can be separated visually and numerically using a One-Class Support Vector Machine. High detection rates are demonstrated for various component faults and even for a minimum instrumentation suite using synthesized datasets derived from full-flight data of commercially operated flights. In addition to the tests conducted with synthesized data, the algorithm is verified based on operational in-flight measurements providing a proof-of-concept. Consequently, the availability of continuously sampled in-flight measurements combined with machine-learning methods allows fault detection within a single flight. Full article
(This article belongs to the Special Issue Diagnostics and Optimization of Gas Turbine)
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25 pages, 5956 KiB  
Article
Estimation and Mitigation of Unknown Airplane Installation Effects on GPA Diagnostics
by Mikael Stenfelt and Konstantinos Kyprianidis
Machines 2022, 10(1), 36; https://doi.org/10.3390/machines10010036 - 04 Jan 2022
Viewed by 1417
Abstract
In gas turbines used for airplane propulsion, the number of sensors are kept at a minimum for accurate control and safe operation. Additionally, when data are communicated between the airplane main computer and the various subsystems, different systems may have different constraints and [...] Read more.
In gas turbines used for airplane propulsion, the number of sensors are kept at a minimum for accurate control and safe operation. Additionally, when data are communicated between the airplane main computer and the various subsystems, different systems may have different constraints and requirements regarding what data transmit. Early in the design process, these parameters are relatively easy to change, compared to a mature product. If the gas turbine diagnostic system is not considered early in the design process, it may lead to diagnostic functions having to operate with reduced amount of data. In this paper, a scenario where the diagnostic function cannot obtain airplane installation effects is considered. The installation effects in question is air intake pressure loss (pressure recovery), bleed flow and shaft power extraction. A framework is presented where the unknown installation effects are estimated based on available data through surrogate models, which is incorporated into the diagnostic framework. The method has been evaluated for a low-bypass turbofan with two different sensor suites. It has also been evaluated for two different diagnostic schemes, both determined and underdetermined. Results show that, compared to assuming a best-guess constant-bleed and shaft power, the proposed method reduce the RMS in health parameter estimation from 26% up to 80% for the selected health parameters. At the same time, the proposed method show the same degradation pattern as if the installation effects were known. Full article
(This article belongs to the Special Issue Diagnostics and Optimization of Gas Turbine)
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17 pages, 6473 KiB  
Article
Data-Driven Models for Gas Turbine Online Diagnosis
by Iván González Castillo, Igor Loboda and Juan Luis Pérez Ruiz
Machines 2021, 9(12), 372; https://doi.org/10.3390/machines9120372 - 20 Dec 2021
Cited by 5 | Viewed by 3034
Abstract
The lack of gas turbine field data, especially faulty engine data, and the complexity of fault embedding into gas turbines on test benches cause difficulties in representing healthy and faulty engines in diagnostic algorithms. Instead, different gas turbine models are often used. The [...] Read more.
The lack of gas turbine field data, especially faulty engine data, and the complexity of fault embedding into gas turbines on test benches cause difficulties in representing healthy and faulty engines in diagnostic algorithms. Instead, different gas turbine models are often used. The available models fall into two main categories: physics-based and data-driven. Given the models’ importance and necessity, a variety of simulation tools were developed with different levels of complexity, fidelity, accuracy, and computer performance requirements. Physics-based models constitute a diagnostic approach known as Gas Path Analysis (GPA). To compute fault parameters within GPA, this paper proposes to employ a nonlinear data-driven model and the theory of inverse problems. This will drastically simplify gas turbine diagnosis. To choose the best approximation technique of such a novel model, the paper employs polynomials and neural networks. The necessary data were generated in the GasTurb software for turboshaft and turbofan engines. These input data for creating a nonlinear data-driven model of fault parameters cover a total range of operating conditions and of possible performance losses of engine components. Multiple configurations of a multilayer perceptron network and polynomials are evaluated to find the best data-driven model configurations. The best perceptron-based and polynomial models are then compared. The accuracy achieved by the most adequate model variation confirms the viability of simple and accurate models for estimating gas turbine health conditions. Full article
(This article belongs to the Special Issue Diagnostics and Optimization of Gas Turbine)
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27 pages, 3501 KiB  
Article
Aircraft Engine Performance Monitoring and Diagnostics Based on Deep Convolutional Neural Networks
by Amare Desalegn Fentaye, Valentina Zaccaria and Konstantinos Kyprianidis
Machines 2021, 9(12), 337; https://doi.org/10.3390/machines9120337 - 07 Dec 2021
Cited by 18 | Viewed by 5081
Abstract
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, [...] Read more.
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. Full article
(This article belongs to the Special Issue Diagnostics and Optimization of Gas Turbine)
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18 pages, 3856 KiB  
Article
Assessment of Dynamic Bayesian Models for Gas Turbine Diagnostics, Part 2: Discrimination of Gradual Degradation and Rapid Faults
by Valentina Zaccaria, Amare Desalegn Fentaye and Konstantinos Kyprianidis
Machines 2021, 9(12), 308; https://doi.org/10.3390/machines9120308 - 24 Nov 2021
Cited by 4 | Viewed by 1611
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Diagnostics and Optimization of Gas Turbine)
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17 pages, 2095 KiB  
Article
Assessment of Dynamic Bayesian Models for Gas Turbine Diagnostics, Part 1: Prior Probability Analysis
by Valentina Zaccaria, Amare Desalegn Fentaye and Konstantinos Kyprianidis
Machines 2021, 9(11), 298; https://doi.org/10.3390/machines9110298 - 21 Nov 2021
Cited by 5 | Viewed by 1528
Abstract
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, [...] Read more.
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. Full article
(This article belongs to the Special Issue Diagnostics and Optimization of Gas Turbine)
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