Adaptive detection and prediction of performance degradation in off-shore turbomachinery

Zagórowska, Marta; Spüntrup, Frederik Schulze; Ditlefsen, Arne-Marius; Imsland, Lars; Lunde, Erling; Thornhill, Nina F.

doi:10.1016/j.apenergy.2020.114934

Cited by 21 publications

(11 citation statements)

References 25 publications

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“…The influence of degradation will appear both in the compressor maps and in the dynamics. A degradation indicator for industrial compressors has been developed by [18]. Up state Degraded state Fig.…”

Section: Influence Of Degradation On the Behaviourmentioning

confidence: 99%

“…The speed N of the compressor is one of the main factors contributing to the degradation [9]. A further review of models of degradation in turbomachinery is provided in [18].…”

Section: Degradationmentioning

confidence: 99%

“…The subscript T emphasises the time-dependence of the degradation function. The parameters in (5) can be identified in the way described by [18].…”

Section: A Factor-free Component Of the Model Of Degradationmentioning

confidence: 99%

See 2 more Smart Citations

Load-Sharing With Degradation Management in a Compressor Station

Zagórowska

Haugen²,

Skourup³

et al. 2024

IEEE Trans. Automat. Sci. Eng.

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Management of compressor degradation is often considered from the perspective of maintenance of the compressor, but most frameworks for the operation of compressors do not take degradation into account.This paper proposes a method for operation of compressors that takes into account the current level of degradation in order to manage further degradation. The algorithm can be used in maintenance planning frameworks, in particular if the timings of maintenance activities are fixed. The algorithm can extend the lifetime of a compressor by mitigating its degradation, or, conversely, can intensify the degradation to reach the maximum level in time for planned maintenance.The performance of the algorithm has been demonstrated in a case study for five compressors. A comparison with equal load approach shows that the new algorithm improves the operation of the system by managing the degradation of selected compressors. Explicit management of degradation allows an extension of the lifetime of selected compressors before maintenance must be performed. Conversely, by ensuring that the desired level of degradation is attained before pre-planned maintenance actions, it contributes to increased efficacy of maintenance actions.

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Section: Influence Of Degradation On the Behaviourmentioning

confidence: 99%

“…The speed N of the compressor is one of the main factors contributing to the degradation [9]. A further review of models of degradation in turbomachinery is provided in [18].…”

Section: Degradationmentioning

confidence: 99%

See 1 more Smart Citation

Load-Sharing With Degradation Management in a Compressor Station

Zagórowska

Haugen²,

Skourup³

et al. 2024

IEEE Trans. Automat. Sci. Eng.

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“…Hybrid techniques that apply a collective problem-solving approach have shown promising performance when the methods are integrated on the basis of their complementary strengths [15,16]. In general, the current literature on gas turbine diagnostics, for instance [17][18][19], shows that advanced diagnostic method development is still a subject of considerable research effort.…”

Section: Introductionmentioning

confidence: 99%

Aircraft Engine Performance Monitoring and Diagnostics Based on Deep Convolutional Neural Networks

2021

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The rapid advancement of machine-learning techniques has played a significant role in the evolution of engine health management technology. In the last decade, deep-learning methods have received a great deal of attention in many application domains, including object recognition and computer vision. Recently, there has been a rapid rise in the use of convolutional neural networks for rotating machinery diagnostics inspired by their powerful feature learning and classification capability. However, the application in the field of gas turbine diagnostics is still limited. This paper presents a gas turbine fault detection and isolation method using modular convolutional neural networks preceded by a physics-driven performance-trend-monitoring system. The trend-monitoring system was employed to capture performance changes due to degradation, establish a new baseline when it is needed, and generatefault signatures. The fault detection and isolation system was trained to step-by-step detect and classify gas path faults to the component level using fault signatures obtained from the physics part. The performance of the method proposed was evaluated based on different fault scenarios for a three-shaft turbofan engine, under significant measurement noise to ensure model robustness. Two comparative assessments were also carried out: with a single convolutional-neural-network-architecture-based fault classification method and with a deep long short-term memory-assisted fault detection and isolation method. The results obtained revealed the performance of the proposed method to detect and isolate multiple gas path faults with over 96% accuracy. Moreover, sharing diagnostic tasks with modular architectures is seen as relevant to significantly enhance diagnostic accuracy.

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“…One of the most commonly used rotating equipment in industrial companies is the compressor for the production of compressed air (Perstedt & Tuhkanen, 2017). Although the causes of failure of rotating equipment are very often related to the phenomena of wear of the rotating elements and vibration (El Adraoui, Gziri, and Mousrij, 2020a), performances of the compressors deteriorates due to detrimental effects of fouling on the aerodynamic flow characteristic (Hanachi, Liu, Banerjee, and Chen, 2016;Jombo, Pecinka, Sampath, and Mba, 2018;Zagorowska, Spüntrup, Ditlefsen, Imsland, Lunde, and Thornhill, 2020). Faced with all its causes of degradation, predictive maintenance seems to be the most suitable solution to improve the availability of rotating machines (Hanachi et al, 2016;El Adraoui, Gziri, and Mousrij, 2020b;Jeong, Son, Cho, Baik, and Lee, 2020).…”

Section: Introductionmentioning

confidence: 99%

Integration of future maintenance actions in the prediction parameters of the ATLAS COPCO ZR 200 compressor

Tjahè

Fotso²,

Medard³

et al. 2021

IJPHM

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The prediction of several failure modes of an industrial equipment requires the development of prediction systems with several interdependent parameters. The integration of future maintenance actions with this type of prediction system is a major asset for maintenance decision making. This is even more relevant in the event that after having predicted the future occurrence of several failure modes, the maintenance department does not have the necessary resources to correct all the predicted failure modes at once. In this case it becomes necessary to know how much longer the equipment will work if future partial maintenance actions that do not correct all failure modes are implemented. It is to contribute to the resolution of this problem that we propose an architecture integrating the future maintenance actions to the prediction of several interdependent parameters. This architecture is based on the association of Proportional Integral Derivative regulators to Neuro-Fuzzy systems taking into account the four previous instants to predict the next instant. An application is made with accuracies of the order of 70% for the prediction of the phenomena of fouling of the coolers and of the order of 90% for the prediction of the phenomena of clogging of the filters of the ATLAS COPCO compressor, this with Central Processing Unit values not exceeding one minute.

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Adaptive detection and prediction of performance degradation in off-shore turbomachinery

Cited by 21 publications

References 25 publications

Load-Sharing With Degradation Management in a Compressor Station

Load-Sharing With Degradation Management in a Compressor Station

Aircraft Engine Performance Monitoring and Diagnostics Based on Deep Convolutional Neural Networks

Integration of future maintenance actions in the prediction parameters of the ATLAS COPCO ZR 200 compressor

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