A Fault Diagnosis Method of Insulator String Based on Infrared Image Feature Extraction and Probabilistic Neural Network

2017 ◽  
Author(s):  
Lijun Xiao ◽  
Qiang Mao ◽  
Penghao Lan ◽  
Xiaochun Zang ◽  
Zhiwei Liao
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Probabilistic Neural Network ◽  
Infrared Image ◽  
Image Feature ◽  
Image Feature Extraction ◽  
Diagnosis Method

scholarly journals Cell-Free DNA Pattern Identification and its Classification using Convolution Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 5362-5364
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
User Interface ◽  
Graphical User Interface ◽  
Probabilistic Method ◽  
Convolution Neural Network ◽  
Image Feature ◽  
Image Feature Extraction ◽  
Free Dna ◽  
Dna Pattern

Circulating cell DNA (cfDNA) design identification assumes a cardinal job in fetal drug, transplantation and oncology. Be that as it may, it has additionally demonstrated to be a biomarker for different maladies. There are numerous order strategies by which the acknowledgment and arrangement should be possible. So as to have a superior time unpredictability and improve the precision further, this strategy targets distinguishing and arranging the general DNA examples and ailments related with them utilizing cfDNA Images in a Convolution Neural Network. A probabilistic method is used for cfDNA image feature extraction, fragmentation and interpretation. Graphical User Interface is the platform where this method is employed since it uses visual indicators in place of text-based interface. The aftereffects of this test demonstrate that the Convolution Neural Network calculation can perceive cfDNA successions accurately and successfully with no dubiety. Prepared with examples, the CNN can effectively characterize the picture surrendered to coordinated and unparalleled examples with numerical highlights.

scholarly journals Fault Diagnosis Method for Engine Control System Based on Probabilistic Neural Network and Support Vector Machine

2019 ◽  
Vol 9 (19) ◽  
pp. 4122 ◽  
Author(s):  
Bo Wang ◽  
Hongwei Ke ◽  
Xiaodong Ma ◽  
Bing Yu
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Control System ◽  
Fault Diagnosis ◽  
Probabilistic Neural Network ◽  
Support Vector ◽  
Engine Control ◽  
Piston Engine ◽  
Engine Control System ◽  
Diagnosis Method

Due to the poor working conditions of an engine, its control system is prone to failure. If these faults cannot be treated in time, it will cause great loss of life and property. In order to improve the safety and reliability of an aero-engine, fault diagnosis, and optimization method of engine control system based on probabilistic neural network (PNN) and support vector machine (SVM) is proposed. Firstly, using the German 3 W piston engine as a control object, the fault diagnosis scheme is designed and introduced briefly. Then, the fault injection is performed to produce faults, and the data sample for engine fault diagnosis is established. Finally, the important parameters of PNN and SVM are optimized by particle swarm optimization (PSO), and the results are analyzed and compared. It shows that the engine fault diagnosis method based on PNN and SVM can effectively diagnose the common faults. Under the optimization of PSO, the accuracy of PNN and SVM results are significantly improved, the classification accuracy of PNN is up to 96.4%, and the accuracy of SVM is up to 98.8%, which improves the application of them in fault diagnosis technology of aero-piston engine control system.

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.

scholarly journals Mechanical Fault Diagnosis Using Color Image Recognition of Vibration Spectrogram Based on Quaternion Invariable Moment

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Liang Hua ◽  
Yujian Qiang ◽  
Juping Gu ◽  
Ling Chen ◽  
Xinsong Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Extraction Method ◽  
Learning Algorithm ◽  
Probabilistic Neural Network ◽  
Invariant Moment ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Neural Network Algorithm

Automatic extraction of time-frequency spectral image of mechanical faults can be achieved and faults can be identified consequently when rotating machinery spectral image processing technology is applied to fault diagnosis, which is an advantage. Acquired mechanical vibration signals can be converted into color time-frequency spectrum images by the processing of pseudo Wigner-Ville distribution. Then a feature extraction method based on quaternion invariant moment was proposed, combining image processing technology and multiweight neural network technology. The paper adopted quaternion invariant moment feature extraction method and gray level-gradient cooccurrence matrix feature extraction method and combined them with geometric learning algorithm and probabilistic neural network algorithm, respectively, and compared the recognition rates of rolling bearing faults. The experimental results show that the recognition rates of quaternion invariant moment are higher than gray level-gradient cooccurrence matrix in the same recognition method. The recognition rates of geometric learning algorithm are higher than probabilistic neural network algorithm in the same feature extraction method. So the method based on quaternion invariant moment geometric learning and multiweight neural network is superior. What is more, this algorithm has preferable generalization performance under the condition of fewer samples, and it has practical value and acceptation on the field of fault diagnosis for rotating machinery as well.

Sign in / Sign up

Export Citation Format

Share Document