Gas path fault diagnostics using a hybrid intelligent method for industrial gas turbine engines

Amare, D. F.; Aklilu, Tesfamichael Baheta; Gilani, Syed I.U.

doi:10.1007/s40430-018-1497-6

Cited by 22 publications

(13 citation statements)

References 46 publications

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“…To diagnose three simultaneous defects in a two-shaft GPU, a system developed by integrating an Autoassociative Neural Network (AANN), nested machine learning classifiers (ML) and a Multilayer Perceptron (MLP) was used in [8]. The purpose of each functional component of the system is considered.…”

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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“…This means that it is difficult to design a method that is fast while also achieving the best performance. To solve this problem, hybrid approaches have been designed to combine the best characteristics of different methods in an efficient way [16][17][18]. Thus, the limitations of one method can be overcome by the advantages of another.…”

Section: Introductionmentioning

confidence: 99%

Aircraft Engine Gas-Path Monitoring and Diagnostics Framework Based on a Hybrid Fault Recognition Approach

2021

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Considering the importance of continually improving the algorithms in aircraft engine diagnostic systems, the present paper proposes and benchmarks a gas-path monitoring and diagnostics framework through the Propulsion Diagnostic Methodology Evaluation Strategy (ProDiMES) software developed by NASA. The algorithm uses fleet-average and individual engine baseline models to compute feature vectors that form a fault classification with healthy and faulty engine classes. Using this classification, a hybrid fault-recognition technique based on regularized extreme learning machines and sparse representation classification was trained and validated to perform both fault detection and fault identification as a common process. The performance of the system was analyzed along with the results of other diagnostic frameworks through four stages of comparison based on different conditions, such as operating regimes, testing data, and metrics (detection, classification, and detection latency). The first three stages were devoted to the independent algorithm development and self-evaluation, while the final stage was related to a blind test case evaluated by NASA. The comparative analysis at all stages shows that the proposed algorithm outperforms all other diagnostic solutions published so far. Considering the advantages and the results obtained, the framework is a promising tool for aircraft engine monitoring and diagnostic systems.

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“…Dou and Zhou [19] compared and analyzed four direct classification methods like probabilistic neural network for intelligent fault diagnosis of rotating machine. Amare et al [20] combined Auto Associative Neural Network (AANN), nested Machine Learning Classifier (MLC) and Multi-Layer Perceptron (MLP), and proposed a hybrid intelligent technology to diagnose three simultaneous failures in a double axis of the industrial gas turbine engine.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Long Short-Term Memory Model for Fault Early Warning of Nuclear Power Turbine

Liu

Shen

et al. 2020

IEEE Access

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Fault early warning of equipment in nuclear power plant can effectively reduce unplanned forced shutdown and avoid significant safety accidents. This paper presents a Bayesian Long Short-Term Memory (LSTM) neural network method for fault early warning method of nuclear power turbine. The Long Short-Term Memory neural network prediction model is developed to address data uncertainty while taking into account complicated situation of the equipment operation. Quantitative reliability validation method is established based on Bayesian inference. A wavelet packet multi-scale time-frequency analysis is employed for data denoising. A Probabilistic Principal Component Analysis (PPCA) method combined with key factor analysis is proposed for dimension reduction and dealing with the data uncertainty. The principal component inverse search method is developed to identify the critical factors mainly contributing to the turbine fault. Numerical results indicate that the proposed novel model is validated with Bayesian confidence of 92% by using the real-world steam turbine data and the model can provide accurate warning in the early creep stage of the fault. INDEX TERMS Bayesian inference, long short-term memory, discrete wavelet packet transform, nuclear power turbine, probabilistic principal component analysis.

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Gas path fault diagnostics using a hybrid intelligent method for industrial gas turbine engines

Cited by 22 publications

References 46 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

Aircraft Engine Gas-Path Monitoring and Diagnostics Framework Based on a Hybrid Fault Recognition Approach

Bayesian Long Short-Term Memory Model for Fault Early Warning of Nuclear Power Turbine

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