Micro Gas Turbines in the Future Smart Energy System: Fleet Monitoring, Diagnostics, and System Level Requirements

Aslanidou, Ioanna; Rahman, Moksadur; Zaccaria, Valentina; Kyprianidis, Konstantinos

doi:10.3389/fmech.2021.676853

Cited by 16 publications

(13 citation statements)

References 57 publications

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“…Therefore, another fleet learning concept is to use a general model that basically emulates the behavior of the entire fleet but then adapts it to the specific system under consideration (D2). In [314], a framework for the condition diagnosis of a fleet of micro gas turbines with production variances was presented. The idea is to expand a model that depicts the behavior of an averaged turbine by tuning parameters that can adapt to each individual turbine.…”

Section: A Approaches From the Manufacturer's Perspectivementioning

confidence: 99%

Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

Braig

Zeiler

2023

IEEE Access

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Prognostics and health management (PHM) is an engineering approach dealing with the diagnosis, prognosis, and management of the health state of engineering systems. Methods that can analyze system behavior, fault conditions, and degradation are crucial for PHM applications, as they create the basis for determining, predicting, and monitoring the health of engineering systems. Data-driven methods have been proven to be suitable for automated diagnosis or prognosis due to their pattern recognition and anomaly detection abilities. Moreover, they do not require knowledge of the underlying degradation process. However, training data-driven methods usually requires a large amount of data, whose collection, cleansing, organization, and preparation are generally very time-consuming and costly. There are usually little or no run-to-failure data available at market launch, especially for new systems such as new machine generations. Nevertheless, related systems, hereinafter referred to as similar systems, often already exist, differing only in some technical characteristics. In this paper, the similar system problem is defined, and explanations of the difficulties that arise when using data from similar systems are presented. Furthermore, it is discussed why the usage of these data offers great potential for condition diagnosis and prognosis of engineering systems. An overview of data-driven methods that can be used to utilize data from similar systems is provided, and the methods that such systems already consider are highlighted. Two related research areas are identified, namely, fleet learning and transfer learning. In the paper, it is shown that similar system approaches will become an important branch of research in PHM. However, some difficulties must be overcome.INDEX TERMS Condition diagnosis, condition prognosis, data-driven methods, fleet learning, prognostics and health management (PHM), similar system approach, similar system problem, transfer learning.

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Section: A Approaches From the Manufacturer's Perspectivementioning

confidence: 99%

Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

Braig

Zeiler

2023

IEEE Access

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“…In the manufacturing industry, many sensor devices are interconnected to collect operational data from machines on a continuous basis and feed it to backend computers for control and predictive analysis (Gopalakrishnan and Kumaran, 2022;Masero et al, 2018). These techniques can be used to monitor different parts of a manufacturing process (He et al, 2017), including belt drives (Pollak et al, 2021), bearings (Pichler et al, 2020;Lalik and Wa ˛torek, 2021), fleet (Ioanna et al, 2021), boiler feed pumps (Moleda et al, 2020), bi-directional JQME 29,2 control valve (Khadim et al, 2021), rotating machinery (Torres-Contreras et al, 2021;Lis et al, 2021), conveyor motors (Kiangala and Wang, 2020), injection moulding machines (Rousopoulou et al, 2020) and steel plate systems (Chong et al, 2021). In addition to all these, failures can happen in key components such as barrier machines in railroad crossings (Grzechca et al, 2021a) and heavy Earth moving machinery.…”

Section: Fault Detection Techniquesmentioning

confidence: 99%

“…, 2021), bearings (Pichler et al. , 2020; Lalik and Wątorek, 2021), fleet (Ioanna et al. , 2021), boiler feed pumps (Moleda et al.…”

Section: Rq1: What Are the Domains That Use Anomaly Detection For Pre...mentioning

confidence: 99%

Review of fault detection techniques for predictive maintenance

Divya

Bhasi

Kumar

2022

JQME

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PurposeThis study aims to bring awareness to the developing of fault detection systems using the data collected from sensor devices/physical devices of various systems for predictive maintenance. Opportunities and challenges in developing anomaly detection algorithms for predictive maintenance and unexplored areas in this context are also discussed.Design/methodology/approachFor conducting a systematic review on the state-of-the-art algorithms in fault detection for predictive maintenance, review papers from the years 2017–2021 available in the Scopus database were selected. A total of 93 papers were chosen. They are classified under electrical and electronics, civil and constructions, automobile, production and mechanical. In addition to this, the paper provides a detailed discussion of various fault-detection algorithms that can be categorised under supervised, semi-supervised, unsupervised learning and traditional statistical method along with an analysis of various forms of anomalies prevalent across different sectors of industry.FindingsBased on the literature reviewed, seven propositions with a focus on the following areas are presented: need for a uniform framework while scaling the number of sensors; the need for identification of erroneous parameters; why there is a need for new algorithms based on unsupervised and semi-supervised learning; the importance of ensemble learning and data fusion algorithms; the necessity of automatic fault diagnostic systems; concerns about multiple fault detection; and cost-effective fault detection. These propositions shed light on the unsolved issues of predictive maintenance using fault detection algorithms. A novel architecture based on the methodologies and propositions gives more clarity for the reader to further explore in this area.Originality/valuePapers for this study were selected from the Scopus database for predictive maintenance in the field of fault detection. Review papers published in this area deal only with methods used to detect anomalies, whereas this paper attempts to establish a link between different industrial domains and the methods used in each industry that uses fault detection for predictive maintenance.

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“…The production of energy from those that are renewable varies according to natural conditions, such as wind speed and solar radiation. For this reason, it has been studied how to overcome the problem of the intermittency of renewable energies and their storage [ 2 ]. Faced with this problem, attention is being paid to the Gas Turbine (GT), which has the fastest response among conventional power generators, which is convenient so as not to affect the stability of electrical networks [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

System Identification Methodology of a Gas Turbine Based on Artificial Recurrent Neural Networks

Aquize

Cajahuaringa

Machuca

et al. 2023

Sensors

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The application of identification techniques using artificial intelligence to the gas turbine (GT), whose nonlinear dynamic behavior is difficult to describe through differential equations and the laws of physics, has begun to gain importance for a little more than a decade. NARX (Nonlinear autoregressive network with exogenous inputs) is one of the models used to identify GT because it provides good results. However, existing studies need to show a systematic method to generate robust NARX models that can identify a GT with satisfactory accuracy. In this sense, a systematic method is proposed to design NARX models for identifying a GT, which consists of nine precise steps that go from identifying GT variables to obtaining the optimized NARX model. To validate the method, it was applied to a case study of a 215 MW SIEMENS TG, model SGT6-5000F, using a set of 2305 real-time series data records, obtaining a NARX model with an MSE of 1.945 × 10−5, RMSE of 0.4411% and a MAPE of 0.0643.

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Micro Gas Turbines in the Future Smart Energy System: Fleet Monitoring, Diagnostics, and System Level Requirements

Cited by 16 publications

References 57 publications

Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

Review of fault detection techniques for predictive maintenance

System Identification Methodology of a Gas Turbine Based on Artificial Recurrent Neural Networks

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