Condition Monitoring of Combustion System on Industrial Gas Turbines Based on Trend and Noise Analysis

Martínez-García, Miguel; Garlick, Mike; Latimer, Anthony; Cruz-Manzo, Samuel

doi:10.1115/gt2017-64288

Cited by 4 publications

(3 citation statements)

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“…2) When the PBT measurements present "Erratic Signals" consistently, the case is normally caused by the presence of carryover/condensate within the gas fuel system, which can impinge on combustion hardware causing damage. [12]…”

Section: B Expert Knowledge-"problems"mentioning

confidence: 99%

A Knowledge Transfer Platform for Fault Diagnosis of Industrial Gas Turbines

Zhang

Jombo

Latimer

2018

2018 IEEE 22nd International Conference on Intelligent Engineering Systems (INES)

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The aim of this paper is to introduce the bases of an intelligent fault diagnostic platform, which assists in detecting mechanical failures of Industrial Gas Turbines (IGTs). This comprises an integration of an expert system and its complementary signal processing techniques. The essential characteristic here is not to exclude humans (experts) from the diagnostic process, but rather to transfer their knowledge and experience to a computerized platform. The automated process executed by the computerized platform is to ensure the scalability and consistency in fault diagnosis; while the humans are required to corroborate the transparency and liability of the outcomes. In this paper, a Knowledge Transfer Platform (KTP) is proposed for fault diagnosis of industrial systems. It is then designed and tested for combustion fault diagnosis using field data of IGTs. The preliminary results have revealed the feasibility and efficacy of the proposed scheme, which has the potential to be further extended to a large industrial scale and to different engineering diagnostic applications.

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Section: B Expert Knowledge-"problems"mentioning

confidence: 99%

A Knowledge Transfer Platform for Fault Diagnosis of Industrial Gas Turbines

Zhang

Jombo

Latimer

2018

2018 IEEE 22nd International Conference on Intelligent Engineering Systems (INES)

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“…Additionally, the trend analysis uses the trend slope and noise values, which are also considered when reporting alarm situations. The successfully implemented examples of this were introduced for wind [ 3 , 4 ] and gas [ 5 ] turbines. Unfortunately, proposing a single indicator that contains enough information is a problematic and sometimes even impossible task.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-Logic-Based Recommendation System for Processing in Condition Monitoring

Górski

Heesch

Dziendzikowski

et al. 2022

Sensors

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The development of a machine’s condition monitoring system is often a challenging task. This process requires the collection of a sufficiently large dataset on signals from machine operation, context information related to the operation conditions, and the diagnosis experience. The two referred problems are today relatively easy to solve. The hardest to describe is the diagnosis experience because it is based on imprecise and non-numerical information. However, it is essential to process acquired data to develop a robust monitoring system. This article presents a framework for a system dedicated to recommending processing algorithms for condition monitoring. It includes a database and fuzzy-logic-based modules composed within the system. Based on the contextual knowledge provided by the user, the procedure suggests processing algorithms. This paper presents the evaluation of the proposed agent on two different parallel gearboxes. The results of the system are processing algorithms with assigned model types. The obtained results show that the algorithms recommended by the system achieve a higher accuracy than those selected arbitrarily. The results obtained allow for an average of 5 to 14.5% higher accuracy.

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“…Industrial gas turbines are energy systems widely used in industries such as power plants, transportation, aerospace, and off-shore platforms [ 1 , 2 , 3 , 4 ]. As an important component in industry, associated key issues are maintenance and performance enhancement [ 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. To reduce maintenance costs as well as to improve the performance of gas turbines, condition monitoring solutions are deployed.…”

Section: Introductionmentioning

confidence: 99%

Operational Modes Detection in Industrial Gas Turbines Using an Ensemble of Clustering Methods

Ghazvini

Sànchez–Marrè

Bahilo

et al. 2021

Sensors

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Operational modes of a process are described by a number of relevant features that are indicative of the state of the process. Hundreds of sensors continuously collect data in industrial systems, which shows how the relationship between different variables changes over time and identifies different modes of operation. Gas turbines’ operational modes are usually defined regarding their expected energy production, and most research works either are focused a priori on obtaining these modes solely based on one variable, the active load, or assume a fixed number of states and build up predictive models to classify new situations as belonging to the predefined operational modes. However, in this work, we take into account all available parameters based on sensors’ data because other factors can influence the system status, leading to the identification of a priori unknown operational modes. Furthermore, for gas turbine management, a key issue is to detect these modes using a real-time monitoring system. Our approach is based on using unsupervised machine learning techniques, specifically an ensemble of clusters to discover consistent clusters, which group data into similar groups, and to generate in an automatic way their description. This description, upon interpretation by experts, becomes identified and characterized as operational modes of an industrial process without any kind of a priori bias of what should be the operational modes obtained. Our proposed methodology can discover and identify unknown operational modes through data-driven models. The methodology was tested in our case study with Siemens gas turbine data. From available sensors’ data, clusters descriptions were obtained in an automatic way from aggregated clusters. They improved the quality of partitions tuning one consistency parameter and excluding outlier clusters by defining filtering thresholds. Finally, operational modes and/or sub-operational modes were identified with the interpretation of the clusters description by process experts, who evaluated the results very positively.

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Condition Monitoring of Combustion System on Industrial Gas Turbines Based on Trend and Noise Analysis

Cited by 4 publications

References 0 publications

A Knowledge Transfer Platform for Fault Diagnosis of Industrial Gas Turbines

A Knowledge Transfer Platform for Fault Diagnosis of Industrial Gas Turbines

Fuzzy-Logic-Based Recommendation System for Processing in Condition Monitoring

Operational Modes Detection in Industrial Gas Turbines Using an Ensemble of Clustering Methods

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