Automatic identification of malfunctions of large turbomachinery during transient states with genetic algorithm optimization

• Autorzy: Barszcz Tomasz , Zabaryłło Mateusz

Identyfikatory

DOI

10.24425/mms.2022.138551

• Języki publikacji: EN

Abstrakty

EN

Turbines and generators operating in the power generation industry are a major source of electrical energy worldwide. These are critical machines and their malfunctions should be detected in advance in order to avoid catastrophic failures and unplanned shutdowns. A maintenance strategy which enables to detect malfunctions at early stages of their existence plays a crucial role in facilities using such types of machinery. The best source of data applied for assessment of the technical condition are the transient data measured during start-ups and coast-downs. Most of the proposed methods using signal decomposition are applied to small machines with a rolling element bearing in steady-state operation with a shaft considered as a rigid body. The machines examined in the authors’ research operate above their first critical rotational speed interval and thus their shafts are considered to be flexible and are equipped with a hydrodynamic sliding bearing. Such an arrangement introduces significant complexity to the analysis of the machine behavior, and consequently, analyzing such data requires a highly skilled human expert. The main novelty proposed in the paper is the decomposition of transient vibration data into components responsible for particular failure modes. The method is automated and can be used for identification of turbogenerator malfunctions. Each parameter of a particular decomposed function has its physical representation and can help the maintenance staff to operate the machine properly. The parameters can also be used by the managing personnel to plan overhauls more precisely. The method has been validated on real-life data originating from a 200 MW class turbine. The real-life field data, along with the data generated by means of the commercial software utilized in GE’s engineering department for this particular class of machines, was used as the reference data set for an unbalanced response during the transients in question.

Słowa kluczowe

EN

machine learning; fault detection; transient; turbine generator; genetic algorithm

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2022
• Tom: Vol. 29, nr 1
• Strony: 175--190
• Opis fizyczny: Bibliogr. 23 poz., rys., tab., wykr., wzory

Twórcy

autor

Barszcz Tomasz

• AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland

autor

Zabaryłło Mateusz

• GE Power, ul. Stoczniowa 2, 82-300 Elblag, Poland

Bibliografia

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Uwagi

1. This paper is partially supported by grant No. POIR.04.01.04-00-0080/19, funded by the National Centre for Research and Development (Poland). Moreover, the authors would also like to express their gratitude to GH for sharing the engineering data and providing an opportunity to collect transient data from a real turboset.

2. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).