scholarly journals Bearing Fault Identification Based on Deep Convolution Residual Network

Mechanika ◽  
2021 ◽  
Vol 27 (3) ◽  
pp. 229-236
Author(s):  
Tong ZHOU ◽  
Yuan LI ◽  
Yijia JING ◽  
Yifei TONG
Keyword(s):  
Feature Extraction ◽  
Deep Neural Networks ◽  
High Accuracy ◽  
Normal Operation ◽  
Fault Identification ◽  
Mechanical Equipment ◽  
Residual Network ◽  
Bearing Fault ◽  
Fitting Ability ◽  
Very High

Bearings are important parts in industrial production and are related to the normal operation of mechanical equipment. For bearing fault identification， traditional method often includes feature extraction, which involves professional prior knowledge and is time-consuming. This paper proposes the deep convolution residual network (DCRN) to identify the bearing fault. Based on the end-to-end learning characteristics of deep neural networks, this method can directly use raw data for training, and does not require feature extraction. Moreover, under the effect of skip connection, DCRN can exert the powerful fitting ability of deep neural network. In this paper, by stacking residual blocks, three different architecture of DCRN are designed and all three achieve very high accuracy, respectively 99.60%, 99.71% and 99.81%. Compared with other methods, DCRN have better generalization performance.

scholarly journals Intelligent Fault Identification for Rolling Bearings Fusing Average Refined Composite Multiscale Dispersion Entropy-Assisted Feature Extraction and SVM with Multi-Strategy Enhanced Swarm Optimization

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 527
Author(s):  
Huibin Shi ◽  
Wenlong Fu ◽  
Bailin Li ◽  
Kaixuan Shao ◽  
Duanhao Yang
Keyword(s):  
Feature Extraction ◽  
Normal Operation ◽  
Fault Identification ◽  
Support Vector ◽  
Mechanical Equipment ◽  
Rolling Bearings ◽  
Swarm Optimization ◽  
Penalty Factor ◽  
Kernel Parameter ◽  
Factor C

Rolling bearings act as key parts in many items of mechanical equipment and any abnormality will affect the normal operation of the entire apparatus. To diagnose the faults of rolling bearings effectively, a novel fault identification method is proposed by merging variational mode decomposition (VMD), average refined composite multiscale dispersion entropy (ARCMDE) and support vector machine (SVM) optimized by multistrategy enhanced swarm optimization in this paper. Firstly, the vibration signals are decomposed into different series of intrinsic mode functions (IMFs) based on VMD with the center frequency observation method. Subsequently, the proposed ARCMDE, fusing the superiorities of DE and average refined composite multiscale procedure, is employed to enhance the ability of the multiscale fault-feature extraction from the IMFs. Afterwards, grey wolf optimization (GWO), enhanced by multistrategy including levy flight, cosine factor and polynomial mutation strategies (LCPGWO), is proposed to optimize the penalty factor C and kernel parameter g of SVM. Then, the optimized SVM model is trained to identify the fault type of samples based on features extracted by ARCMDE. Finally, the application experiment and contrastive analysis verify the effectiveness of the proposed VMD-ARCMDE-LCPGWO-SVM method.

scholarly journals Intelligent Fault Diagnosis Method Using Acoustic Emission Signals for Bearings under Complex Working Conditions

2020 ◽  
Vol 10 (20) ◽  
pp. 7068
Author(s):  
Minh Tuan Pham ◽  
Jong-Myon Kim ◽  
Cheol Hong Kim
Keyword(s):  
Acoustic Emission ◽  
Feature Extraction ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Extraction Methods ◽  
High Accuracy ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Ae Signals

Recent convolutional neural network (CNN) models in image processing can be used as feature-extraction methods to achieve high accuracy as well as automatic processing in bearing fault diagnosis. The combination of deep learning methods with appropriate signal representation techniques has proven its efficiency compared with traditional algorithms. Vital electrical machines require a strict monitoring system, and the accuracy of these machines’ monitoring systems takes precedence over any other factors. In this paper, we propose a new method for diagnosing bearing faults under variable shaft speeds using acoustic emission (AE) signals. Our proposed method predicts not only bearing fault types but also the degradation level of bearings. In the proposed technique, AE signals acquired from bearings are represented by spectrograms to obtain as much information as possible in the time–frequency domain. Feature extraction and classification processes are performed by deep learning using EfficientNet and a stochastic line-search optimizer. According to our various experiments, the proposed method can provide high accuracy and robustness under noisy environments compared with existing AE-based bearing fault diagnosis methods.

Feature Extraction of Rolling Bearing Fault Diagnosis

2012 ◽  
Vol 190-191 ◽  
pp. 993-997
Author(s):  
Li Jie Sun ◽  
Li Zhang ◽  
Yong Bo Yang ◽  
Da Bo Zhang ◽  
Li Chun Wu
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Attribute Reduction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Mechanical Equipment ◽  
Characteristic Parameters ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Experimental Trials

Mechanical equipment fault diagnosis occupies an important position in the industrial production, and feature extraction plays an important role in fault diagnosis. This paper analyzes various methods of feature extraction in rolling bearing fault diagnosis and classifies them into two big categories, which are methods of depending on empirical rules and experimental trials and using objective methods for screening. The former includes five methods: frequency as the characteristic parameters, multi-sensor information fusion method, rough set attribute reduction method, "zoom" method and vibration signal as the characteristic parameters. The latter includes two methods: sensitivity extraction and data mining methods to select attributes. Currently, selection methods of feature parameters depend heavily on empirical rules and experimental trials, thus extraction results are be subjected to restriction from subjective level, feature extraction in the future will develop toward objective screening direction.

A Novel Approach of Multiscale Feature Extraction for Gearbox Condition Monitoring

2012 ◽  
Vol 160 ◽  
pp. 25-29
Author(s):  
Wei Guo Huang ◽  
Zhong Kui Zhu ◽  
Cheng Li ◽  
Peng Li
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Extraction Method ◽  
Work Conditions ◽  
High Accuracy ◽  
Fault Identification ◽  
Discrete Wavelet ◽  
Feature Extraction Method ◽  
Novel Approach ◽  
Accuracy And Stability

This paper proposes a novel multiscale slope feature extraction method using wavelet-based multiresolution anlaysis for gearboxes fault identification. The new method mainly includes the discrete wavelet transform (DWT), the variances calculation of multiscale detailed signals, and the wavelet-based multiscale slope features estimation. Experimental results show that the wavelet-based multiscale slope features show excellent clustering for different work conditions and have the merits of high accuracy and stability in classifying different conditions of gearbox.

scholarly journals Stellar magnetic imaging from spectropolarimetric data

1996 ◽  
Vol 176 ◽  
pp. 53-60 ◽  
Author(s):  
J.-F. Donati
Keyword(s):  
Small Amplitude ◽  
High Accuracy ◽  
Spectral Lines ◽  
New Method ◽  
Doppler Imaging ◽  
Magnetic Imaging ◽  
Active Stars ◽  
Surface Fields ◽  
Very High

In this paper, I will review the capabilities of magnetic imaging (also called Zeeman-Doppler imaging) to reconstruct spot distributions of surface fields from sets of rotationnally modulated Zeeman signatures in circularly polarised spectral lines. I will then outline a new method to measure small amplitude magnetic signals (typically 0.1% for cool active stars) with very high accuracy. Finally, I will present and comment new magnetic images reconstructed from data collected in 1993 December at the Anglo-Australian Telescope (AAT).

Additive DenseNet: Dense connections based on simple addition operations

2020 ◽  
pp. 1-11
Author(s):  
Dawei Yu ◽  
Jie Yang ◽  
Yun Zhang ◽  
Shujuan Yu
Keyword(s):  
Text Classification ◽  
Deep Neural Networks ◽  
Model Complexity ◽  
Residual Network ◽  
Large Margin ◽  
Simple Addition ◽  
High Resource ◽  
Model Training ◽  
Low Efficiency ◽  
Resource Economy

The Densely Connected Network (DenseNet) has been widely recognized as a highly competitive architecture in Deep Neural Networks. And its most outstanding property is called Dense Connections, which represent each layer’s input by concatenating all the preceding layers’ outputs and thus improve the performance by encouraging feature reuse to the extreme. However, it is Dense Connections that cause the challenge of dimension-enlarging, making DenseNet very resource-intensive and low efficiency. In the light of this, inspired by the Residual Network (ResNet), we propose an improved DenseNet named Additive DenseNet, which features replacing concatenation operations (used in Dense Connections) with addition operations (used in ResNet), and in terms of feature reuse, it upgrades addition operations to accumulating operations (namely ∑ (·)), thus enables each layer’s input to be the summation of all the preceding layers’ outputs. Consequently, Additive DenseNet can not only preserve the dimension of input from enlarging, but also retain the effect of Dense Connections. In this paper, Additive DenseNet is applied to text classification task. The experimental results reveal that compared to DenseNet, our Additive DenseNet can reduce the model complexity by a large margin, such as GPU memory usage and quantity of parameters. And despite its high resource economy, Additive DenseNet can still outperform DenseNet on 6 text classification datasets in terms of accuracy and show competitive performance for model training.

scholarly journals High accuracy data-driven heliostat calibration and state prediction with pretrained deep neural networks

Solar Energy ◽  
2021 ◽  
Vol 218 ◽  
pp. 48-56
Author(s):  
Max Pargmann ◽  
Daniel Maldonado Quinto ◽  
Peter Schwarzbözl ◽  
Robert Pitz-Paal
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
High Accuracy ◽  
Data Driven ◽  
State Prediction ◽  
Accuracy Data

Hole Accuracy during Deep Hole Drilling for Hydraulic Cylinder Application

2014 ◽  
Vol 984-985 ◽  
pp. 67-72 ◽  
Author(s):  
R. Clifford Benjamin Raj ◽  
B. Anand Ronald ◽  
A. Velayudham ◽  
Prasmit Kumar Nayak
Keyword(s):  
Feed Rate ◽  
Drill Hole ◽  
Hydraulic Cylinder ◽  
High Accuracy ◽  
Diameter Ratio ◽  
Hole Drilling ◽  
Deep Hole ◽  
Deep Hole Drilling ◽  
Very High ◽  
Continuous Chip

Deep-hole drilling is a process in which the hole length will be very high when compared to diameter of the drill hole (i.e. length to diameter ratio will be greater than 5). Drilling a deep hole with very high accuracy is difficult process. The current project is about the production of deep hole with the aim to produce a chip which is not a continuous chip and also not a powdery chip. These conditions can be attained by varying the spindle speed and the tool feed rate.

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