D002 Drill Wear Prediction with Features Extracted From the Static & Dynamic Components of Forces by Wavelet Packet Transform Using Back Propagation Neural Network

2013 ◽  
Vol 2013.7 (0) ◽  
pp. 452-456
Author(s):  
Jie XU ◽  
Keiji YAMADA ◽  
Katsuhiko SEKIYA ◽  
Ryutaro TANAKA ◽  
Yasuo YAMANE
Keyword(s):  
Neural Network ◽  
Wavelet Packet ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Wavelet Packet Transform ◽  
Wear Prediction ◽  
Dynamic Components

Power Quality Disturbance Classification Using Wavelet Packet Transform and Back Propagation Algorithm

2016 ◽  
Vol 13 (10) ◽  
pp. 7099-7109
Author(s):  
M. K Elango ◽  
A Jagadeesan ◽  
K. Mohana Sundaram
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Virtual Instrument ◽  
Wavelet Packet ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Wavelet Packet Transform ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Power Quality Disturbances

This paper develops a real time solution for detecting the Power Quality events. Fourteen events are generated through experimental setup and the signals are acquired through a voltage Data Acquisition Card, NI DAQ-9225, controlled by a Virtual Instrument software package. The features extracted from the Wavelet Transformation are fed into the Back Propagation Neural Network for training. By the virtue of a Neural Network property, it gets self-adapted and self-learned aiding in automatic classification of Power Quality Events. A combination of Wavelet Transform technique and Neural Networks are employed to detect and characterize the Power Quality Disturbances. The result obtained shows the effectiveness of the Wavelet Packet Transform based Back Propagation algorithm in classifying the Power Quality Disturbances. The results produced by the proposed methodology based Back Propagation Algorithm is verified with the Power Quality Analyser.

Effect of different features to drill-wear prediction with back propagation neural network

2014 ◽  
Vol 38 (4) ◽  
pp. 791-798 ◽  
Author(s):  
Jie Xu ◽  
Keiji Yamada ◽  
Katsuhiko Seikiya ◽  
Ryutaro Tanaka ◽  
Yasuo Yamane
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Wear Prediction

FFBPNN-BASED HIGH DROWSINESS CLASSIFICATION USING EMG AND WPT

2020 ◽  
Vol 32 (03) ◽  
pp. 2050023 ◽  
Author(s):  
Mousa Kadhim Wali
Keyword(s):  
Neural Network ◽  
Wavelet Packet ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Road Accidents ◽  
Mean Values ◽  
Physiological Signal ◽  
Feed Forward Back Propagation ◽  
Emg Signal ◽  
Amplitude Level

The detection of drowsiness level is important because it is the main reason for fatal road accidents. Electromyography of the upper arm and shoulder is an important physiological signal affected by drivers’ drowsiness, in which its amplitude level and frequency band of the sleep-deprived case are different than those of the alert state. Therefore depending on electromyography (EMG), its drowsiness frequency (80–100[Formula: see text]Hz) was detected in order to determine high drowsiness state based on wavelet packet transform (WPT) which decomposes the EMG signal into its approximation and detail coefficients up to level 4 using db2, db7, sym5 and coif5 wavelets. In this research after extraction, the two higher order statistical features, kurtosis and skewness, are computed from 3[Formula: see text]s window of the three EMG channels, and analysis of variance test is used to check whether their mean values are different for the different classes as both [Formula: see text]-values are less than 0.005 under db2 wavelet. Therefore, they were supplied to feed forward back propagation neural network (FFBPNN) as this type of neural network is used for distinguishing and classification purposes for different objects. They obtained an accuracy of 75% for detecting high levels among other levels of normal and low drowsiness with an average sensitivity of 78.63% and specificity of 75.97% because the spectrum of the EMG alert (non-drowsiness) signal of 80–100 Hz is different from that of drowsy 80–90[Formula: see text]Hz and high drowsy 78–95[Formula: see text]Hz signals.

scholarly journals Ultrasonic based concrete defects identification via wavelet packet transform and GA-BP neural network

2021 ◽  
Vol 7 ◽  
pp. e635
Author(s):  
Tianyu Hu ◽  
Jinhui Zhao ◽  
Ruifang Zheng ◽  
Pengfeng Wang ◽  
Xiaolu Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Ultrasonic Testing ◽  
High Performance ◽  
Structural Integrity ◽  
Complex Dynamics ◽  
Wavelet Packet ◽  
Back Propagation ◽  
Concrete Block ◽  
Wavelet Packet Transform ◽  
Signal Recognition

Concrete is the main material in building. Since its poor structural integrity may cause accidents, it is significant to detect defects in concrete. However, it is a challenging topic as the unevenness of concrete would lead to the complex dynamics with uncertainties in the ultrasonic diagnosis of defects. Note that the detection results mainly depend on the direct parameters, e.g., the time of travel through the concrete. The current diagnosis accuracy and intelligence level are difficult to meet the design requirement for automatic and increasingly high-performance demands. To solve the mentioned problems, our contribution of this paper can be summarized as establishing a diagnosis model based on the GA-BPNN method and ultrasonic information extracted that helps engineers identify concrete defects. Potentially, the application of this model helps to improve the working efficiency, diagnostic accuracy and automation level of ultrasonic testing instruments. In particular, we propose a simple and effective signal recognition method for small-size concrete hole defects. This method can be divided into two parts: (1) signal effective information extraction based on wavelet packet transform (WPT), where mean value, standard deviation, kurtosis coefficient, skewness coefficient and energy ratio are utilized as features to characterize the detection signals based on the analysis of the main frequency node of the signals, and (2) defect signal recognition based on GA optimized back propagation neural network (GA-BPNN), where the cross-validation method has been used for the stochastic division of the signal dataset and it leads to the BPNN recognition model with small bias. Finally, we implement this method on 150 detection signal data which are obtained by the ultrasonic testing system with 50 kHz working frequency. The experimental test block is a C30 class concrete block with 5, 7, and 9 mm penetrating holes. The information of the experimental environment, algorithmic parameters setting and signal processing procedure are described in detail. The average recognition accuracy is 91.33% for the identification of small size concrete defects according to experimental results, which verifies the feasibility and efficiency.

scholarly journals Fault Diagnosis of Reciprocating Compressor Based on Convolutional Neural Networks with Multisource Raw Vibration Signals

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Hong-bai Yang ◽  
Jiang-an Zhang ◽  
Lei-lei Chen ◽  
Hong-li Zhang ◽  
Shu-lin Liu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Recognition Rate ◽  
Back Propagation ◽  
Petroleum Industry ◽  
Back Propagation Neural Network ◽  
Reciprocating Compressor ◽  
Vibration Signals ◽  
Fault Recognition

Reciprocating compressors are widely used in petroleum industry. Due to containing complex nonlinear signal, it is difficult to extract the fault features from its vibration signals. This paper proposes a new method named Convolutional Neural Network based on Multisource Raw vibration signals (MSRCNN). The proposed method uses multisource raw vibration signals collected by several sensors as input and uses the designed CNN to operate both the feature extraction and classification. The gas valve signals of reciprocating compressor in different states are used as the experimental data. In order to test the effectiveness of the proposed method, it is compared with the traditional BP (Back-Propagation) neural network fault diagnosis method based on power spectrum energy and wavelet packet energy. In order to further test the antinoise performance of the proposed method, some noisy signals with different signal-to-noise ratios were constructed by adding white noise into sampled signals for testing. The results show that the MSRCNN model has higher fault recognition rate than the traditional methods. This indicates that the MSRCNN method not only has good fault recognition effect, but also has certain antinoise performance.

scholarly journals Single-ended fault location and early warning method of transmission line based on back propagation neural network

2021 ◽  
Vol 2121 (1) ◽  
pp. 012032
Author(s):  
Han Zhou ◽  
Minghui Liu ◽  
Xin Yu ◽  
Weiyang Wang ◽  
Jingyao Gao
Keyword(s):  
Neural Network ◽  
Transmission Line ◽  
Early Warning ◽  
Fault Location ◽  
Power Transmission ◽  
Wavelet Packet ◽  
Back Propagation ◽  
Structural Complexity ◽  
Back Propagation Neural Network ◽  
Measuring Point

Abstract For power transmission systems, accurate and reliable fault location methods can ensure rapid recovery of faulty lines and improve power supply reliability. In order to solve the problems of the structural complexity of the transmission system and the difficulty of line fault location, a single-ended fault location and early warning method of transmission line based on back propagation neural network is proposed. First, the fault line selection is performed quickly when the fault occurs. Then, the voltage fault components collected at the measuring point when the fault occurs are decomposed and reconstructed by wavelet packet to obtain the wavelet packet energy, which is used as the input sample to train through the nonlinear fitting ability of back propagation. With the help of backpropagation neural network, arbitrary complex functions can be processed, and the learning results can be accurately used for new knowledge, and circuit faults can be diagnosed conveniently and quickly. Finally, the corresponding fault distance can be output by substituting the wavelet packet energy reflecting the fault location. The simulation results show that the method has strong resistance to transition resistance and high positioning accuracy.

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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