Multi-mode diagnosis of a gas turbine engine using an adaptive neuro-fuzzy system

Hanachi, Houman; Liu, Jie; Mechefske, Christopher

doi:10.1016/j.cja.2017.11.017

Cited by 51 publications

(17 citation statements)

References 30 publications

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“…Considering that individual elements of a gas turbine engine (GTE) are not available for direct measurements of their condition, the change in the technical condition of these elements is determined on the basis of measured technological parameters. For this, [12] proposed to diagnose GTE on the basis of the adaptive neuro-fuzzy inference system (ANFIS). It is shown that the performance of the developed system was checked using a high-precision model of a gas turbine engine and a generated set of measuring parameters of the gas path, taking into account various conditions.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Software implemented fault diagnosis of natural gas pumping unit based on feedforward neural network

Kozlenko

Zamikhovska

Zamikhovskyі

2021

EEJET

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In recent years, more and more attention has been paid to the use of artificial neural networks (ANN) for the diagnostics of gas pumping units (GPU). Usually, ANN training is carried out on GPU workflow models, and generated sets of diagnostic data are used to simulate defect conditions. At the same time, the results obtained do not allow assessing the real state of the GPU. It is proposed to use the characteristics of the acoustic and vibration processes of the GPU as the input data of the ANN. A descriptive statistical analysis of real vibration and acoustic processes generated by the operation of the GPU type GTK-25-i (Nuovo Pignone, Italy) was carried out. The formation of batches of diagnostic features arriving at the input of the ANN was carried out. Diagnostic features are the five maximum amplitude components of the acoustic and vibration signals, as well as the value of the standard deviation for each sample. Diagnostic features are calculated directly in the ANN input data pipeline in real time for three technical states of the GPU. Using the frameworks TensorFlow, Keras, NumPy, pandas, in the Python 3 programming language, an architecture was developed for a deep fully connected feedforward ANN, trained on the backpropagation algorithm. The results of training and testing the developed ANN are presented. During testing, it was found that the signal classification precision for the “nominal” state of all 1,475 signal samples is 1.0000, for the “current” state, precision equals 0.9853, and for the “defective” state, precision is 0.9091. The use of the developed ANN makes it possible to classify the technical states of the GPU with an accuracy sufficient for practical use, which will prevent the occurrence of GPU failures. ANN can be used to diagnose GPU of any type and power

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Software implemented fault diagnosis of natural gas pumping unit based on feedforward neural network

Kozlenko

Zamikhovska

Zamikhovskyі

2021

EEJET

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“…The physical model displays the estimated state and also a proportional number of how close the predicted value output is to the measured data. This number is the Normalized Root Mean Square Error (NRMSE) given in Equation (4) [28], where p is the vector from predict value, m is the vector from measured data, and both have n elements.…”

Section: Physical Modelmentioning

confidence: 99%

Empowering Predictive Maintenance: A Hybrid Method to Diagnose Abnormal Situations

et al. 2020

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Aerospace systems are composed of hundreds or thousands of components and complex subsystems which need an appropriate health monitoring capability to enable safe operation in various conditions. In terms of monitoring systems, it is possible to find a considerable number of state-of-the-art works in the literature related to ad-hoc solutions. Still, it is challenging to reuse them even with subtle differences in analogous subsystems or components. This paper proposes the Generic Anomaly Detection Hybridization Algorithm (GADHA) aiming to build a more reusable algorithm to support anomaly detection. The solution consists of analyzing different supervised machine learning classification algorithms combined in ensemble techniques, with a physical model when available, and two levels of a decision to estimate the current state of the monitored system. Finally, the proposed algorithm assures at least equal, or, more typically, better, overall accuracy in fault detection and isolation than the application of such algorithms alone, through few adaptations.

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“…The diagnosis of gas turbines is generally carried out through knowledge of its normal behavior, total control of the different operating modes is then essential when considering an advanced diagnosis of this type of machine [9,11,14,16,20,27,29]. Indeed, the application of fuzzy logic techniques improves the reliability of the monitoring system and the sensitivity of fault detection, it is also capable of causing, in severe failures, a turbine stop or allowing the system to continue to operate in degraded mode in the event of a problem which does not require an immediate stop [2,6,8,10,12,22,24,28,30].…”

Section: Fuzzy Diagnosticsmentioning

confidence: 99%

Faults detection based on fuzzy concepts for vibrations monitoring in gas turbine

Alili¹,

Hafaifa²,

Iratni³

2020

Diagnostyka

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Multi-mode diagnosis of a gas turbine engine using an adaptive neuro-fuzzy system

Cited by 51 publications

References 30 publications

Software implemented fault diagnosis of natural gas pumping unit based on feedforward neural network

Software implemented fault diagnosis of natural gas pumping unit based on feedforward neural network

Empowering Predictive Maintenance: A Hybrid Method to Diagnose Abnormal Situations

Faults detection based on fuzzy concepts for vibrations monitoring in gas turbine

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