Research of Hard Fault Diagnosis Simulation Platform of Aero-Engine's Key Sensors Based on Neural Network

The purpose of the research is to establish a fault diagnosis model of the aero-engines key sensors using the artificial neural networks to replace the engines mathematical model, so as to establish a hard fault diagnosis simulation platform to monitor the performances of the engine sensors on real-time, to judge the engine failure mode timely, and to locate the fault type of sensors accurately. By analyzing the correlations of the parameters that affect the conditions of the engine, a three-layer BP network model is established. The related QAR (Quick Access Recorder) data are used to simulate and analyze the models using the MATLAB. Combined with the characteristics of the hard failure of the critical engine sensors and the correlation of the parameters, the fault diagnosis simulation platform is established. Then, the parameters of the normal engine and the failure engine are used respectively to evaluate and validate the platform. The simulation results show that the platform can judge the critical sensors faults of the engine accurately, and can locate the type of sensors reliably.

Download Full-text

Long distance wireless fault diagnosis for photovoltaic modules based on back propagation neural network

International Journal of Electrical Engineering Education ◽

10.1177/0020720920940601 ◽

2020 ◽

pp. 002072092094060

Author(s):

Ling Chen ◽

Wei Han ◽

Hai-Tao Li ◽

Zi-Kun Xu ◽

Jing-Wei Zhang ◽

...

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Back Propagation ◽

Long Distance ◽

Partial Shading ◽

Bp Network ◽

Depth Analysis ◽

Pv Modules ◽

Diagnosis Method ◽

Diagnosis Model

Various faults of photovoltaic (PV) modules inevitably occur in the work process, since PV modules are installed in hostile situation. To obtain the types of failure, a novel fault diagnosis method based on back propagation (BP) neural network with Levenberg-Marquardt (L-M) algorithm for PV modules is proposed. Through the in-depth analysis the output of PV modules under normal and fault conditions, the input variables of the diagnosis model are acquired. The high-speed and real-time fault diagnosis model for PV modules is first designed based on TMS320VC5402 DSP and long-distance wireless fault diagnosis is realized by Zigbee technology. The simulation and experimental results show that the fault diagnosis method for PV modules based on BP network with L-M algorithm can effectively detect four types of fault for PV modules such as open circuit, short circuit, partial shading and abnormal degradation. The numerical results verify the effectiveness and correctness of the proposed method, which can provide a great educational benefit of PV operation technology.

Download Full-text

Garbage Crusher Fault Diagnosis Based on RBF Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.16-19.971 ◽

2009 ◽

Vol 16-19 ◽

pp. 971-975

Author(s):

Yong Hou Sun ◽

Cong Li ◽

Mei Fa Huang ◽

Hui Jing

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Rbf Neural Network ◽

Structure And Properties ◽

Automatic Equipment ◽

Rbf Network ◽

The Neural Network ◽

Diagnosis Model ◽

Simulation Results ◽

Training Simulation

The garbage crusher is a new kind of crusher for garbage crushing when processing Municipal Solid Waste (MSW). With the development of automatic equipment and the complication of structure and properties of the garbage crusher, the fault diagnosis of garbage crusher is very important. In this paper, according to the fault symptoms and parameters, Radial Basis Function Neural Network (RBF NN) is used for fault diagnosis of the garbage crusher. The structure and inference of RBF NN are discussed in detail. The garbage crusher fault diagnosis model is established based on RBF network. At last, the fault of mechanical system is taken as an example of garbage crusher fault diagnosis. Training simulation results of the neural network are given base on MATLAB software. The result shows the RBF NN is suitable for fault diagnosis of garbage crusher.

Download Full-text

Application of Deep Learning in Fault Diagnosis of Rotating Machinery

Processes ◽

10.3390/pr9060919 ◽

2021 ◽

Vol 9 (6) ◽

pp. 919

Author(s):

Wanlu Jiang ◽

Chenyang Wang ◽

Jiayun Zou ◽

Shuqing Zhang

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Fault Diagnosis ◽

Rotating Machinery ◽

Generative Adversarial Networks ◽

Extraction Ability ◽

One Dimensional ◽

Adversarial Networks ◽

Diagnosis Model ◽

The One

The field of mechanical fault diagnosis has entered the era of “big data”. However, existing diagnostic algorithms, relying on artificial feature extraction and expert knowledge are of poor extraction ability and lack self-adaptability in the mass data. In the fault diagnosis of rotating machinery, due to the accidental occurrence of equipment faults, the proportion of fault samples is small, the samples are imbalanced, and available data are scarce, which leads to the low accuracy rate of the intelligent diagnosis model trained to identify the equipment state. To solve the above problems, an end-to-end diagnosis model is first proposed, which is an intelligent fault diagnosis method based on one-dimensional convolutional neural network (1D-CNN). That is to say, the original vibration signal is directly input into the model for identification. After that, through combining the convolutional neural network with the generative adversarial networks, a data expansion method based on the one-dimensional deep convolutional generative adversarial networks (1D-DCGAN) is constructed to generate small sample size fault samples and construct the balanced data set. Meanwhile, in order to solve the problem that the network is difficult to optimize, gradient penalty and Wasserstein distance are introduced. Through the test of bearing database and hydraulic pump, it shows that the one-dimensional convolution operation has strong feature extraction ability for vibration signals. The proposed method is very accurate for fault diagnosis of the two kinds of equipment, and high-quality expansion of the original data can be achieved.

Download Full-text

Power Transform Fault Diagnosis Based on the Adaptive Mutation Particle Swarm Optimization

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.756-759.3804 ◽

2013 ◽

Vol 756-759 ◽

pp. 3804-3808

Author(s):

Zhi Mei Duan ◽

Jia Tang Cheng

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Fault Diagnosis ◽

Power Transformer ◽

Recognition Rate ◽

Particle Swarm ◽

Adaptive Mutation ◽

Bp Network ◽

Swarm Optimization ◽

Fault Recognition

In order to improve the accuracy of fault diagnosis of power transformer, in this paper, a method is proposed that optimize the weight of BP neural network by adaptive mutation particle swarm optimization (AMPSO). According to the characteristic of transformer fault, the optimized neural network is used to diagnose fault of the power transformer. Individual particles action is amended by this algorithm and local minima problems of the standard PSO and BP network are overcooked. The experimental results show that, the method can classify transformer faults, and effectively improve the fault recognition rate.

Download Full-text

Transformer Fault Diagnosis Based on Improved Quantum Genetic Algorithm and BP Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.29-32.1543 ◽

2010 ◽

Vol 29-32 ◽

pp. 1543-1549 ◽

Cited By ~ 1

Author(s):

Jie Wei ◽

Hong Yu ◽

Jin Li

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Fault Diagnosis ◽

Bp Neural Network ◽

Fuzzy Theory ◽

Back Propagation ◽

Gas Analysis ◽

Bp Network ◽

Quantum Genetic Algorithm ◽

Transformer Fault

Three-ratio of the IEC is a convenient and effective approach for transformer fault diagnosis in the dissolved gas analysis (DGA). Fuzzy theory is used to preprocess the three-ratio for its boundary that is too absolute. As the same time, an improved quantum genetic algorithm IQGA (QGASAC) is used to optimize the weight and threshold of the back propagation (BP). The local and global searching ability of the QGASAC approach is utilized to find the BP optimization solution. It can overcome the slower convergence velocity and hardly getting the optimization of the BP neural network. So, aiming at the shortcoming of BP neural network and three-ratio, blurring the boundary of the gas ratio and the QGASAC algorithm is introduced to optimize the BP network. Then the QGASAC-IECBP method is proposed in this paper. Experimental results indicate that the proposed algorithm in this paper that both convergence velocity and veracity are all improved to some extent. And in this paper, the proposed algorithm is robust and practical.

Download Full-text

Research on Fault Diagnosis of Hydraulic System of Fast Erecting Device Based on Fuzzy Neural Network

International Journal of Fluid Power ◽

10.13052/ijfp1439-9776.2321 ◽

2022 ◽

Author(s):

Yangbing Zheng ◽

Xiao Xue ◽

Jisong Zhang

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Bp Neural Network ◽

Hydraulic System ◽

Fuzzy Neural Network ◽

Simulation Analysis ◽

Fuzzy Relation ◽

Analysis Procedure ◽

Fuzzy Neural ◽

Simulation Results

In order to improve the fault diagnosis effectiveness of hydraulic system in erecting devices, the fuzzy neural neural network is applied to carry out fault diagnosis of hydraulic system. Firstly, the main faults of hydraulic system of erecting mechanism are summarized. The main faults of hydraulic system of erecting devices concludes abnormal noise, high temperature of hydraulic oil of hydraulic system, leakage of hydraulic system, low operating speed of hydraulic system, and the characteristics of different faults are analyzed. Secondly, basic theory of fuzzy neural network is studied, and the framework of fuzzy neural network is designed. The inputting layer, fuzzy layer, fuzzy relation layer, relationship layer after fuzzy operation and outputting layer of fuzzy neural network are designed, and the corresponding mathematical models are confirmed. The analysis procedure of fuzzy neural network is established. Thirdly, simulation analysis is carried out for a hydraulic system in erecting device, the BP neural network reaches convergence after 600 times iterations, and the fuzzy neural network reaches convergence after 400 times iterations, fuzzy neural network can obtain higher accuracy than BP neural network, and running time of fuzzy neural network is less than that of BP neural network, therefore, simulation results show that the fuzzy neural network can effectively improve the fault diagnosis efficiency and precision. Therefore, the fuzzy neural network is reliable for fault diagnosis of hydraulic system in erecting devices, which has higher fault diagnosis effect, which can provide the theory basis for healthy detection of hydraulic system in erecting devices.

Download Full-text