Intelligent performance diagnostics of a gas turbine engine using user‐friendly interface neural networks

Kong, Changduk; Ki, Jayoung; Kang, Mi-Young; Kho, Seonghee

doi:10.1108/00022660410545500

Cited by 11 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Over the past decades, several studies have been done on GT diagnostics based on an MLP [113]. An ANN-based user friendly GT fault identification system was provided by Kong et al [132]. A multiple fault detection system was developed by Matuck et al [133] using this approach which is trained on simulation data.…”

Section: Multilayer Perceptronmentioning

confidence: 99%

A Review on Gas Turbine Gas-Path Diagnostics: State-of-the-Art Methods, Challenges and Opportunities

et al. 2019

View full text Add to dashboard Cite

Gas-path diagnostics is an essential part of gas turbine (GT) condition-based maintenance (CBM). There exists extensive literature on GT gas-path diagnostics and a variety of methods have been introduced. The fundamental limitations of the conventional methods such as the inability to deal with the nonlinear engine behavior, measurement uncertainty, simultaneous faults, and the limited number of sensors available remain the driving force for exploring more advanced techniques. This review aims to provide a critical survey of the existing literature produced in the area over the past few decades. In the first section, the issue of GT degradation is addressed, aiming to identify the type of physical faults that degrade a gas turbine performance, which gas-path faults contribute more significantly to the overall performance loss, and which specific components often encounter these faults. A brief overview is then given about the inconsistencies in the literature on gas-path diagnostics followed by a discussion of the various challenges against successful gas-path diagnostics and the major desirable characteristics that an advanced fault diagnostic technique should ideally possess. At this point, the available fault diagnostic methods are thoroughly reviewed, and their strengths and weaknesses summarized. Artificial intelligence (AI) based and hybrid diagnostic methods have received a great deal of attention due to their promising potentials to address the above-mentioned limitations along with providing accurate diagnostic results. Moreover, the available validation techniques that system developers used in the past to evaluate the performance of their proposed diagnostic algorithms are discussed. Finally, concluding remarks and recommendations for further investigations are provided.

show abstract

Section: Multilayer Perceptronmentioning

confidence: 99%

A Review on Gas Turbine Gas-Path Diagnostics: State-of-the-Art Methods, Challenges and Opportunities

et al. 2019

View full text Add to dashboard Cite

show abstract

“…As evident from Figure 1, the strategies can be broadly classified into priori knowledge based or model based (Isermann, 1995; Konrad et al , 1995; Stoustrup et al , 1997; Garimella and Yao, 2005; Kallesoe et al , 2006), databased and those based on hybrid methodologies. The data‐based strategies can be further segregated as those based on statistical methods (Ghanim and Jordan, 1996; Martin et al , 1996a, b; Atienza et al , 1998; MacCarthy and Wasusri, 2002; Liu et al , 2004; Kourti, 2006; Milstein et al , 2005; Gao et al , 2007; Prajapati and Mahapatra, 2008) and those based on AI techniques (Vukic et al , 1998; Knapp and Javadpour, 2000; Samarasinghe and Hashimoto, 2000; Kong et al , 2004; Li et al , 2005; Bo and Xi, 2007; Chen and Huang, 2008; Demirsi, 2008). The model based strategies for process monitoring include parameter estimation based approach (Filbert and Metzger, 1982), observer based approach (Bisiacco and Valcher, 2006; Brambilla et al , 2008; Kiyak et al , 2008) and approaches based on parity equations.…”

Section: Pmfd Strategies: Taxonomy Of the Literaturementioning

confidence: 99%

Process monitoring and fault detection strategies: a review

Das

Maiti²,

Banerjee³

2012

Int J Qual & Reliability Mgmt

View full text Add to dashboard Cite

PurposeMonitoring of a process leading to the detection of faults and determination of the root causes are essential for the production of consistent good quality end products with improved yield. The history of process monitoring fault detection (PMFD) strategies can be traced back to 1930s. Thereafter various tools, techniques and approaches were developed along with their application in diversified fields. The purpose of this paper is to make a review to categorize, describe and compare the various PMFD strategies.Design/methodology/approachTaxonomy was developed to categorize PMFD strategies. The basis for the categorization was the type of techniques being employed for devising the PMFD strategies. Further, PMFD strategies were discussed in detail along with emphasis on the areas of applications. Comparative evaluations of the PMFD strategies based on some commonly identified issues were also carried out. A general framework common to all the PMFD has been presented. And lastly a discussion into future scope of research was carried out.FindingsThe techniques employed for PMFD are primarily of three types, namely data driven techniques such as statistical model based and artificial intelligent based techniques, priori knowledge based techniques, and hybrid models, with a huge dominance of the first type. The factors that should be considered in developing a PMFD strategy are ease in development, diagnostic ability, fault detection speed, robustness to noise, generalization capability, and handling of nonlinearity. The review reveals that there is no single strategy that can address all aspects related to process monitoring and fault detection efficiently and there is a need to mesh the different techniques from various PMFD strategies to devise a more efficient PMFD strategy.Research limitations/implicationsThe review documents the existing strategies for PMFD with an emphasis on finding out the nature of the strategies, data requirements, model building steps, applicability and scope for amalgamation. The review helps future researchers and practitioners to choose appropriate techniques for PMFD studies for a given situation. Further, future researchers will get a comprehensive but precise report on PMFD strategies available in the literature to date.Originality/valueThe review starts with identifying key indicators of PMFD for review and taxonomy was proposed. An analysis was conducted to identify the pattern of published articles on PMFD followed by evolution of PMFD strategies. Finally, a general framework is given for PMFD strategies for future researchers and practitioners.

show abstract

“…In order to test the validity, ANN results were compared with multiple regression analysis and the validity of the model was found to be superior. Kong et al (2004) developed an performance diagnostics code for a gas tribune. Using neural networks the diagnostics systems could detect the single fault types such as compressor fouling, compressor turbine erosion and power turbine erosion as well as multi component combined fault types.…”

Section: Quantification Of Performancementioning

confidence: 99%

Comparing MADM Techniques for Use in Performance Measurement

Abdullah¹

2009

Proceedings of the International Symposium on the Analytic Hierarchy Process

View full text Add to dashboard Cite

Performance measurement has become one of the success factors in business world. Organizational performance measurement can be stated as a multi attribute decision problem. In this study, we evaluate the MADM techniques as a potential tool that can take place in performance measurement systems. In this manner, we will be discussing the performance measurement frameworks in the literature and then analyze current analytic applications in the area. The specific requirements in organizational performance measurement are emphasized and the possible MADM techniques are evaluated according to these requirements.

show abstract

Intelligent performance diagnostics of a gas turbine engine using user‐friendly interface neural networks

Cited by 11 publications

References 10 publications

A Review on Gas Turbine Gas-Path Diagnostics: State-of-the-Art Methods, Challenges and Opportunities

A Review on Gas Turbine Gas-Path Diagnostics: State-of-the-Art Methods, Challenges and Opportunities

Process monitoring and fault detection strategies: a review

Comparing MADM Techniques for Use in Performance Measurement

Contact Info

Product

Resources

About