A comparative study of data-driven and physics-based gas turbine fault recognition approaches

The present paper compares the fault recognition capabilities of two gas turbine diagnostic approaches: data-driven and physics-based (a.k.a. gas path analysis, GPA). The comparison takes into consideration two differences between the approaches, the type of diagnostic space and diagnostic decision rule. To that end, two stages are proposed. In the first one, a data-driven approach with an artificial neural network (ANN) that recognizes faults in the space of measurement deviations is compared with a hybrid GPA approach that employs the same type of ANN to recognize faults in the space of estimated fault parameter. Different case studies for both anomaly detection and fault identification are proposed to evaluate the diagnostic spaces. They are formed by varying the classification, type of diagnostic analysis, and deviation noise scheme. In the second stage, the original GPA is reconstructed replacing the ANN with a tolerance-based rule to make diagnostic decisions. Here, two aspects are under analysis: the comparison of GPA classification rules and whole approaches. The results reveal that for simple classifications both spaces are equally accurate for anomaly detection and fault identification. However, for complex scenarios, the data-driven approach provides on average slightly better results for fault identification. The use of a hybrid GPA with ANN for a full classification instead of an original GPA with tolerance-based rule causes an increase of 12.49% in recognition accuracy for fault identification and up to 54.39% for anomaly detection. As for the whole approach comparison, the application of a data-driven approach instead of the original GPA can lead to an improvement of 12.14% and 53.26% in recognition accuracy for fault identification and anomaly detection, respectively.

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Data-driven anomaly detection in cyber-physical production systems

at - Automatisierungstechnik ◽

10.1515/auto-2015-0060 ◽

2015 ◽

Vol 63 (10) ◽

Cited By ~ 9

Author(s):

Oliver Niggemann ◽

Christian Frey

Keyword(s):

Anomaly Detection ◽

Production Systems ◽

Data Driven ◽

Plant Analysis ◽

Product Complexity ◽

Automation Systems ◽

Data Driven Approach ◽

Assistance Systems ◽

Cyber Physical Production Systems ◽

Intelligent Assistance

AbstractDue to global competition and increasing product complexity, the complexity of production systems has grown significantly in recent years. This places an increasing burden on automation developers, systems engineers and plant constructors. Intelligent assistance systems and smart automation systems are a possible solution to face this complexity: The machines, i.e. the software and assistance systems, take over tasks that were previously carried out manually by experts. At the heart of this concept are intelligent anomaly detection approaches based on models of the system behaviors. Intelligent assistance systems learn these models automatically: Based on data, these systems extract most necessary knowledge about the diagnosis task. This paper outlines this data-driven approach to plant analysis using several use cases from industry.

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The Comparison between Empirical and Data-driven Computation in Predicting CO and NOx Emissions from Gas-Turbine-Based Power Plant

10.29118/ipa21-o-232 ◽

2021 ◽

Author(s):

T. Y. Wicaksono

Keyword(s):

Gas Turbine ◽

Monitoring System ◽

Power Plants ◽

Combustion Process ◽

Open Data ◽

Living Organism ◽

Empirical Method ◽

Data Driven ◽

Gas Emissions ◽

Data Driven Approach

The demand for the energy has been significantly increased over years led by the growth of global population. By the signing of the Paris Agreement in 2015, countries pledged to reduce the greenhouse gas effect including gas emissions to prevent and mitigate the global warming. The emissions control from power generation has then become a serious concern for countries to achieve their target in reducing gas emissions. Besides, the emitted gas such as Nitrogen oxides (NOx) or Carbon Monoxide (CO) that are resulted from the combustion process of fossil fuels in power plants is harmful pollutants to the living organism. The presence of those gas emissions can be predicted using Predictive Emissions Monitoring System (PEMS) or Continues Emissions Monitoring System (CEMS) methods. Continuous Emissions Monitoring System is a system that was designed to monitor the effluent gas streams resulted from the combustion processes. However, this empirical method still has several constraints in predicting the gas emissions where in some cases, it produces significant errors that caused by some uncontrollable aspects such as ambient temperature, pressure and humidity that can lead to miscalculation of operational risks and costs. Solving this problem, we conduct a PEMS with data-driven approach. In this study, we used the 2011-2015 open data from gas-turbine-based power plants in Turkey to train and test several supervised methods as a practical application to predict gas concentration. Predictive Emissions Monitoring System (PEMS) offers more advantages than Continuous Emissions Monitoring System (CEMS) especially in economic aspects. The system will monitor and predict the actual emissions from gas-turbine-based power plants operation. The results of this study indicate that the data-driven approach produces a good RMSE value. By having the gas emissions predicted, a mitigation plan can be set and the operational costs in the following years can be optimized by the company

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A Review on Gas Turbine Anomaly Detection for Implementing Health Management

Volume 1: Aircraft Engine; Fans and Blowers; Marine ◽

10.1115/gt2016-58135 ◽

2016 ◽

Cited By ~ 6

Author(s):

Ningbo Zhao ◽

Xueyou Wen ◽

Shuying Li

Keyword(s):

Anomaly Detection ◽

Gas Turbine ◽

Gas Turbines ◽

Health Management ◽

Vibration Signal ◽

Source Information ◽

Rapid Improvement ◽

Recent Developments ◽

Data Driven Approach ◽

On Line

With the rapid improvement of equipment manufacturing technology and the ever increasing cost of fuel, engine health management has become one of the most important parts of aeroengine, industrial and marine gas turbine. As an effective technology for improving the engine availability and reducing the maintenance costs, anomaly detection has attracted great attention. In the past decades, different methods including gas path analysis, on-line monitoring or off-line analysis of vibration signal, oil and electrostatic monitoring have been developed. However, considering the complexity of structure and the variability of working environments for engine, many important problems such as the accurate modeling of gas turbine with different environment, the selection of sensors, the optimization of various data-driven approach and the fusion strategy of multi-source information still need to be solved urgently. Besides, although a large number of investigations in this area are reported every year in various journals and conference proceedings, most of them are about aeroengine or industrial gas turbine and limited literature is published about marine gas turbine. Based on this background, this paper attempts to summarize the recent developments in health management of gas turbines. For the increasing requirement of predict-and-prevent maintenance, the typical anomaly detection technologies are analyzed in detail. In addition, according to the application characteristics of marine gas turbine, this paper introduces a brief prospect on the possible challenges of anomaly detection, which may provide beneficial references for the implementing and development of marine gas turbine health management.

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Estimation and monitoring of unmeasured gas turbine variables

Transactions of the Canadian Society for Mechanical Engineering ◽

10.1139/tcsme-2017-0009 ◽

2019 ◽

Vol 43 (1) ◽

pp. 26-37

Author(s):

Luis Angel Miró-Zárate ◽

Igor Loboda ◽

Juan Luis Pérez-Ruiz ◽

Miguel Toledo-Velázquez

Keyword(s):

Gas Turbine ◽

Accurate Method ◽

Measured Data ◽

Data Driven ◽

Monitoring Systems ◽

Accuracy Analysis ◽

Practical Applications ◽

Drastic Increase ◽

Data Driven Approach ◽

Diagnostic Capabilities

This work proposes a universal data-driven approach to compute and monitor gas turbine unmeasured variables. To this end, a large amount of unmeasured and measured data is first computed at steady state for both baseline and faulty engine conditions using a nonlinear thermodynamic model. On the data generated, polynomial models that relate the unmeasured quantities with the measured variables are then determined. These data-driven models allow the computation of unmeasured variables and their deviations. Accuracy analysis is conducted separately for baseline and current estimates of unmeasured variables and for deviation estimates. All the results prove that the estimates are exact enough. Thus it is possible to obtain a universal fast and accurate method for computing important unmeasured gas turbine quantities that is suitable for practical applications. The method promises a drastic increase in the diagnostic capabilities of online monitoring systems.

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Application of Machine Learning in Turbulent Combustion for Aviation Gas Turbine Combustor Design

10.1115/gtindia2021-76442 ◽

2021 ◽

Author(s):

Vishwas Verma ◽

Kiran Manoharan ◽

Jaydeep Basani

Keyword(s):

Neural Network ◽

Machine Learning ◽

Flow Field ◽

Gas Turbine ◽

Computational Cost ◽

Data Driven ◽

Navier Stokes ◽

Computational Time ◽

Reacting Flow ◽

Data Driven Approach

Abstract Numerical simulation of gas turbine combustors requires resolving a broad spectrum of length and time scales for accurate flow field and emission predictions. Reynold’s Averaged Navier Stokes (RANS) approach can generate solutions in few hours; however, it fails to produce accurate predictions for turbulent reacting flow field seen in general combustors. On the other hand, the Large Eddy Simulation (LES) approach can overcome this challenge, but it requires orders of magnitude higher computational cost. This limits designers to use the LES approach in combustor development cycles and prohibits them from using the same in numerical optimization. The current work tries to build an alternate approach using a data-driven method to generate fast and consistent results. In this work, deep learning (DL) dense neural network framework is used to improve the RANS solution accuracy using LES data as truth data. A supervised regression learning multilayer perceptron (MLP) neural network engine is developed. The machine learning (ML) engine developed in the present study can compute data with LES accuracy in 95% lesser computational time than performing LES simulations. The output of the ML engine shows good agreement with the trend of LES, which is entirely different from RANS, and to a reasonable extent, captures magnitudes of actual flow variables. However, it is recommended that the ML engine be trained using broad design space and physical laws along with a purely data-driven approach for better generalization.

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Predicting personal thermal preferences based on data-driven methods

E3S Web of Conferences ◽

10.1051/e3sconf/201911105015 ◽

2019 ◽

Vol 111 ◽

pp. 05015

Author(s):

José Joaquín Aguilera ◽

Jørn Toftum ◽

Ongun Berk Kazanci

Keyword(s):

Machine Learning ◽

Thermal Comfort ◽

Thermal Environment ◽

Data Driven ◽

Individual Level ◽

Humidity Measurements ◽

Data Driven Approach ◽

Artificial Neural Network Ann ◽

Thermal Preferences ◽

Large Groups

One of the prevalent models to account for thermal comfort in HVAC design is the Predicted Mean Vote (PMV). However, the model is based on parameters difficult to estimate in real applications and it focuses on mean votes of large groups of people. Personal Comfort Models (PCM) is a data-driven approach to model thermal comfort at an individual level. It takes advantage of concepts such as machine learning and Internet of Things (IoT), combining feedback from occupants and local thermal environment measurements. The framework presented in this paper evaluates the performance of PCM and PMV regarding the prediction of personal thermal preferences. Air temperature and relative humidity measurements were combined with thermal preference votes obtained from a field study. This data was used to train three machine learning methods focused on PCM: Artificial Neural Network (ANN), Naive-Bayes (NB) and Fuzzy Logic (FL); comparing them with a PMV-based algorithm. The results showed that all methods had a better overall performance than guessing randomly the thermal preferences votes. In addition, there was not a difference between the performance of the PCM and PMV-based algorithms. Finally, the PMV-based method predicted well thermal preferences of individuals, having a 70% probability of correct guessing.

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A Benchmarking Analysis of a Data-Driven Gas Turbine Diagnostic Approach

Volume 6: Ceramics; Controls, Diagnostics, and Instrumentation; Education; Manufacturing Materials and Metallurgy ◽

10.1115/gt2018-76887 ◽

2018 ◽

Author(s):

Igor Loboda ◽

Juan Luis Pérez-Ruiz ◽

Sergiy Yepifanov

Keyword(s):

Feature Extraction ◽

Fault Detection ◽

Gas Turbine ◽

Software Tool ◽

Aircraft Engine ◽

Data Driven ◽

Fault Classification ◽

Fault Identification ◽

Diagnostic Process ◽

Promising Tool

In an effort to better compare particular gas turbine diagnostic solutions and recommend the best solution, the software tool called Propulsion Diagnostic Method Evaluation Strategy (ProDiMES) has been developed. This benchmarking platform includes a simulator of the aircraft engine fleet with healthy and faulty engines. The platform presents a public approach, at which different investigators can verify and compare their algorithms for the diagnostic stages of feature extraction, fault detection, and fault identification. Using ProDiMES, some different diagnostic solutions have been compared so far. This study presents a new attempt to enhance a gas turbine diagnostic process. A data-driven algorithm that embraces the mentioned three diagnostic stages is verified on the basis of ProDiMES. At the feature extraction stage, this algorithm uses a polynomial model of an engine baseline to compute deviations of actual gas path measurements from the corresponding values of a healthy engine. At the fault detection and fault identification stages, a common classification for fault detection and fault identification is firstly constructed using deviation vectors (patterns). One of the three chosen pattern recognition techniques then performs both fault detection and fault identification as a common process. Numerous numerical experiments have been conducted to select the best configurations of the baseline model, a pertinent structure of the fault classification, and the best recognition technique. The experiments were accompanied by a computational precision analysis for each component of the proposed algorithm. The comparison of the final diagnostic ProDiMES metrics obtained under the selected optimal conditions with the metrics of other diagnostic solutions shows that the proposed algorithm is a promising tool for gas turbine monitoring systems.

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