scholarly journals Motor Fault Detection Using Wavelet Transform and Improved PSO-BP Neural Network

Processes ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1322 ◽  
Author(s):  
Chun-Yao Lee ◽  
Yi-Hsin Cheng
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Fault Detection ◽  
Bp Neural Network ◽  
Short Circuit ◽  
Back Propagation ◽  
Operating Environment ◽  
Cause Of Failure ◽  
Bearing Damage ◽  
Improved Pso

This paper proposes a motor fault detection method based on wavelet transform (WT) and improved PSO-BP neural network which is combined with improved particle swarm optimization (PSO) and a back propagation (BP) neural network with linearly increasing inertia weight. First, this research used WT to analyze the current signals of the healthy motor, bearing damage motor, stator winding inter-turn short circuit motor, and broken rotor bar motor. Second, features after completing the signal analysis were extracted, and three types of classifiers were used to classify. The results show that the improved PSO-BP neural network can effectively detect the cause of failure. In addition, in order to simulate the actual operating environment of the motor, this study added white noise with signal noise ratios of 30 dB, 25 dB, and 20 dB to verify that this model has a better anti-noise ability.

scholarly journals Solar PV Fault Classification using Back Propagation Neural Network

2020 ◽  
Vol 8 (6) ◽  
pp. 5568-5574
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Short Circuit ◽  
Back Propagation ◽  
Error Rates ◽  
Fine Tuning ◽  
Solar Irradiation ◽  
Fault Classification ◽  
Photovoltaic Module ◽  
Solar Pv

Solar energy is that the foremost abundant, inexhaustible and clean of all renewable energy resources. Interest in electrical solar PV power generation has accumulated in recent years due to its benefits. This wide distribution of physical phenomenon panel production wasn't followed by watching, fault detection and designation functions to verify higher gain. In this paper, real time fault analysis and fault detection is done by using Back propagation. By simulating various fault conditions, the performances of a faulty electrical solar photovoltaic module have been compared with respect to its faultless model by quantifying the precise differential residue which can be associated with it. The deformations and faults induced on the I-V curves and P-V curves have been studied to generate data for neural network analysis for different types of faults. Five different fault cases like module to module fault, module - ground faults, short circuit fault, and different shading patterns of modules and solar cells are considered. The MATLAB simulation model’s results show the respective results for various fault conditions along with variation of different solar irradiation which commonly occur in the photovoltaic systems. The projected technique is often generalized and extended to additional sorts of faults. This faults condition was analyzed by using Backpropagation Based Neural Network (BPANN). Back propagation technique ensures fine tuning the weights of neural network to get lower error rates making the model more reliable, therefore the BP-ANN technique contributes in improving the overall accuracy for fault detection in the system using Artificial Neural Network.

scholarly journals A Method for Real-Time Fault Detection of Liquid Rocket Engine Based on Adaptive Genetic Algorithm Optimizing Back Propagation Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5026
Author(s):  
Huahuang Yu ◽  
Tao Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Detection ◽  
Real Time ◽  
Bp Neural Network ◽  
Rocket Engine ◽  
Back Propagation ◽  
Liquid Oxygen ◽  
Adaptive Genetic Algorithm ◽  
Liquid Rocket

A real-time fault diagnosis method utilizing an adaptive genetic algorithm to optimize a back propagation (BP) neural network is intended to achieve real-time fault detection of a liquid rocket engine (LRE). In this paper, the authors employ an adaptive genetic algorithm to optimize a BP neural network, produce real-time predictions regarding sensor data, compare the projected value to the actual data collected, and determine whether the engine is malfunctioning using a threshold judgment mechanism. The proposed fault detection method is simulated and verified using data from a certain type of liquid hydrogen and liquid oxygen rocket engine. The experiment results show that this method can effectively diagnose this liquid hydrogen and liquid oxygen rocket engine in real-time. The proposed method has higher system sensitivity and robustness compared with the results obtained from a single BP neural network model and a BP neural network model optimized by a traditional genetic algorithm (GA), and the method has engineering application value.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

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