Long Short-Term Memory Network-Based Normal Pattern Group for Fault Detection of Three-Shaft Marine Gas Turbine

Bai, Mingliang; Liu, Jinfu; Ma, Yujia; Zhao, Xinyu; Long, Zhenhua; Yu, Daren

doi:10.3390/en14010013

Cited by 36 publications

(19 citation statements)

References 62 publications

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“…Recently, considerable reports have been presented on its application for system and rotating machinery health management [24,50] including gas turbine prognostics [51][52][53]. There were also a few attempts to employ LSTM for gas turbine diagnostics as well [54].…”

Section: Long Short-term Memorymentioning

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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Section: Long Short-term Memorymentioning

confidence: 99%

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

2021

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“…In [23], an NN-based FD is proposed for hydraulic systems, and it is shown that using NNs improves the accuracy. A deep learned NN scheme is designed in [24] for the FD problem in gas turbines. In [25], an FLS is used to approximate the flow pattern, and then based on the FLS model, the flow is predicted, and an FD scheme is designed.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Remark 4. The results in Figures [23][24][25][26] show that the suggested FD scheme gives a good detection performance versus various faults. Furthermore, Table 2 shows a good robustness against disturbances that have been considered as external noises.…”

Section: Remarkmentioning

confidence: 99%

Non-Singleton Type-3 Fuzzy Approach for Flowmeter Fault Detection: Experimental Study in a Gas Industry

Wang

Tavoosi

Mohammadzadeh

et al. 2021

Sensors

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The main contribution of this paper is to develop a new flowmeter fault detection approach based on optimized non-singleton type-3 (NT3) fuzzy logic systems (FLSs). The introduced method is implemented on an experimental gas industry plant. The system is modeled by NT3FLSs, and the faults are detected by comparison of measured end estimated signals. In this scheme, the detecting performance depends on the estimation and modeling performance. The suggested NT3FLS is used because of the existence of a high level of measurement errors and uncertainties in this problem. The designed NT3FLS with uncertain footprint-of-uncertainty (FOU), fuzzy secondary memberships and adaptive non-singleton fuzzification results in a powerful tool for modeling signals immersed in noise and error. The level of non-singleton fuzzification and membership parameters are tuned by maximum correntropy (MC) unscented Kalman filter (KF), and the rule parameters are learned by correntropy KF (CKF) with fuzzy kernel size. The suggested learning algorithms can handle the non-Gaussian noises that are common in industrial applications. The various types of flowmeters are investigated, and the effect of common faults are examined. It is shown that the suggested approach can detect the various faults with good accuracy in comparison with conventional approaches.

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“…The authors stated that this approach outperformed the existing ones and that this method could be applied in time-sensitive scenarios. Similarly, in [82], a method to accomplish fault detection with only normal data introducing the concept of the normal pattern group was proposed. A group of LSTM machines was used to characterise the measurable healthy parameters of the gas turbines to later identify the gas path faults.…”

Section: Fault Detectionmentioning

confidence: 99%

Machine-Learning-Based Condition Assessment of Gas Turbines—A Review

2021

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Condition monitoring, diagnostics, and prognostics are key factors in today’s competitive industrial sector. Equipment digitalisation has increased the amount of available data throughout the industrial process, and the development of new and more advanced techniques has significantly improved the performance of industrial machines. This publication focuses on surveying the last decade of evolution of condition monitoring, diagnostic, and prognostic techniques using machine-learning (ML)-based models for the improvement of the operational performance of gas turbines. A comprehensive review of the literature led to a performance assessment of ML models and their applications to gas turbines, as well as a discussion of the major challenges and opportunities for the research on these kind of engines. This paper further concludes that the combination of the available information captured through the collectors and the ML techniques shows promising results in increasing the accuracy, robustness, precision, and generalisation of industrial gas turbine equipment.

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Long Short-Term Memory Network-Based Normal Pattern Group for Fault Detection of Three-Shaft Marine Gas Turbine

Cited by 36 publications

References 62 publications

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

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

Non-Singleton Type-3 Fuzzy Approach for Flowmeter Fault Detection: Experimental Study in a Gas Industry

Machine-Learning-Based Condition Assessment of Gas Turbines—A Review

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