Fault Diagnosis of Gas Turbine Fuel Systems Based on Improved SOM Neural Network

2019 ◽  
pp. 252-265
Author(s):  
Zhe Chen ◽  
Yiyao Zhang ◽  
Hailei Gong ◽  
Xinyi Le ◽  
Yu Zheng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Gas Turbine ◽  
Som Neural Network

scholarly journals Minimal Structural ART Neural Network and Fault Diagnosis Application of Gas Turbine

2016 ◽  
Vol 10 (1) ◽  
pp. 13-22
Author(s):  
Qingyang Xu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Gas Turbine ◽  
Cognitive Process ◽  
Simple Structure ◽  
High Efficiency ◽  
Complex Structures ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Binary Input

Adaptive Resonance Theory (ART) model is a special neural network based on unsupervised learning which simulates the cognitive process of human. However, ART1 can be only used for binary input, and ART2 can be used for binary and analog vectors which have complex structures and complicated calculations. In order to improve the real-time performance of the network, a minimal structural ART is proposed which combines the merits of the two models by subsuming the bottom-up and top-down weight. The vector similarity test is used instead of vigilance test. Therefore, this algorithm has a simple structure like ART1 and good performance as ART2 which can be used for both binary and analog vector classification, and it has a high efficiency. Finally, a gas turbine fault diagnosis experiment exhibits the validity of the new network.

Research on the Fault Diagnosis Technology of Diesel Engine Based on the Instantaneous Speed

2009 ◽  
Vol 413-414 ◽  
pp. 547-552 ◽  
Author(s):  
Yi Hu ◽  
Rui Ping Zhou ◽  
Jian Guo Yang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Diesel Engine ◽  
Vibration Amplitude ◽  
Good Effect ◽  
Cylinder Pressure ◽  
Angular Vibration ◽  
Diagnosis Method ◽  
Som Neural Network ◽  
Instantaneous Speed

The instantaneous speed signals of diesel contain lots of information about machine states, which is useful for fault diagnosis of diesel engine. Mixed fault diagnosis method of diesel engine based on the instantaneous speed has been proposed, which combines with the lower order angular vibration amplitude and SOM neural network to diagnose the cylinder pressure fault, then extracts three feature parameters of instantaneous speed to locate the fault cylinder. The method can detect the cylinder pressure fault accurately in diesel engine and locate the fault cylinder. The experimental confirmation shows that it has good effect on fault diagnosis of diesel engine.

An Intelligent Fault Diagnosis Method Of Marine Seawater Cooling System Based On SOM Neural Network

2021 ◽  
Author(s):  
Lei Guo ◽  
Jundong Zhang ◽  
Yongjiu Zou ◽  
Guochang Qi ◽  
Keyu Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Cooling System ◽  
Intelligent Fault Diagnosis ◽  
Diagnosis Method ◽  
Som Neural Network

Real-Time Fault Diagnosis for Gas Turbine Blade Based on Output-Hidden Feedback Elman Neural Network

2018 ◽  
Vol 23 (S1) ◽  
pp. 95-102 ◽  
Author(s):  
Pengcheng Zhuo ◽  
Ying Zhu ◽  
Wenxuan Wu ◽  
Junqing Shu ◽  
Tangbin Xia
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Real Time ◽  
Gas Turbine ◽  
Turbine Blade ◽  
Gas Turbine Blade ◽  
Elman Neural Network

A novel gearbox fault feature extraction and classification using Hilbert empirical wavelet transform, singular value decomposition, and SOM neural network

2017 ◽  
Vol 24 (12) ◽  
pp. 2512-2531 ◽  
Author(s):  
Boualem Merainani ◽  
Chemseddine Rahmoune ◽  
Djamel Benazzouz ◽  
Belkacem Ould-Bouamama
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Singular Value ◽  
Vibration Signals ◽  
Empirical Wavelet Transform ◽  
Som Neural Network ◽  
Value Decomposition

There are growing demands for condition monitoring and fault diagnosis of rotating machinery to lower unscheduled breakdown. Gearboxes are one of the fundamental components of rotating machinery; their faults identification and classification always draw a lot of attention. However, non-stationary vibration signals and low energy of weak faults makes this task challenging in many cases. Thus, a new fault diagnosis method which combines the Hilbert empirical wavelet transform (HEWT), singular value decomposition (SVD), and self-organizing feature map (SOM) neural network is proposed in this paper. HEWT, a new self-adaptive time-frequency analysis was applied to the vibration signals to obtain the instantaneous amplitude matrices. Then, the singular value vectors, as the fault feature vectors were acquired by applying the SVD. Last, the SOM was used for automatic gearbox fault identification and classification. An electromechanical model comprising an induction motor coupled with a single stage spur gearbox is considered where the vibration signals of four typical operation modes were simulated. The conditions include the healthy gearbox, input shaft slant crack, tooth cracking, and tooth surface pitting. Obtained results show that the proposed method effectively identifies the gearbox faults at an early stage and realizes automatic fault diagnosis. Moreover, performance evaluation and comparison between the proposed HEWT–SVD method and Hilbert–Huang transform (HHT)–SVD approach show that the HEWT–SVD is better for feature extraction.

scholarly journals Research on Gas-Path Fault-Diagnosis Method of Marine Gas Turbine Based on Exergy Loss and Probabilistic Neural Network

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4701 ◽  
Author(s):  
Yunpeng Cao ◽  
Xinran Lv ◽  
Guodong Han ◽  
Junqi Luan ◽  
Shuying Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Gas Turbine ◽  
Probabilistic Neural Network ◽  
Second Law Of Thermodynamics ◽  
Fault Detection And Isolation ◽  
Exergy Loss ◽  
Diagnosis Method ◽  
Gas Path Fault ◽  
Faulty Component

In order to improve the accuracy of gas-path fault detection and isolation for a marine three-shaft gas turbine, a gas-path fault diagnosis method based on exergy loss and a probabilistic neural network (PNN) is proposed. On the basis of the second law of thermodynamics, the exergy flow among the subsystems and the external environment is analyzed, and the exergy model of a marine gas turbine is established. The exergy loss of a marine gas turbine under the healthy condition and typical gas-path faulty condition is analyzed, and the relative change of exergy loss is used as the input of the PNN to detect the gas-path malfunction and locate the faulty component. The simulation case study was conducted based on a three-shaft marine gas turbine with typical gas-path faults. Several results show that the proposed diagnosis method can accurately detect the fault and locate the malfunction component.

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