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Signal-Based Intelligent Hydraulic Fault Diagnosis Methods: Review and Prospects
2019
Chinese Journal of Mechanical Engineering
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In this paper, the main technology used in an intelligent fault diagnosis and the current research status of hydraulic system fault diagnosis are summarized and analyzed. ...
The significant prospect of applying deep learning in the field of intelligent fault diagnosis is presented, and the main ideas, methods, and principles of several typical DNNs are described and summarized ...
Acknowledgements The authors sincerely thanks to Professor Ting Rui of Army Engineering University for his critical discussion and reading during manuscript preparation. ...
doi:10.1186/s10033-019-0388-9
fatcat:lho5v4o7djhjbhfz2t33pd7as4
Bearing Intelligent Fault Diagnosis Based on Wavelet Transform and Convolutional Neural Network
2020
Shock and Vibration
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In this paper, a method combining Wavelet transform (WT) and Deformable Convolutional Neural Network (D-CNN) is proposed to realize accurate real-time fault diagnosis of end-to-end rolling bearing. ...
Secondly, the time-frequency map of the signal is obtained by time-frequency transform using Wavelet analysis. Finally, the D-CNN is used for feature extraction and classification. ...
In view of the above problems, scholars put forward fault diagnosis methods based on intelligent algorithms. e methods based on intelligent algorithms mainly consist of Deep Belief Networks (DBNs), Convolutional ...
doi:10.1155/2020/6380486
fatcat:b4gu6hcs2bcenphcrb6hjsclpu
A novel method of combining generalized frequency response function and convolutional neural network for complex system fault diagnosis
2020
PLoS ONE
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To solve the problem of low accuracy in traditional fault diagnosis methods, a novel method of combining generalized frequency response function(GFRF) and convolutional neural network(CNN) is proposed. ...
fault states; In order to improve the ability of fault feature extraction, a convolution neural network (CNN) with gradient descent learning rate and alternate convolution layer and pooling layer is designed ...
[4] treated current signal as fault features, and convert the current signal by continuous wavelet transform (CWT) to realize the diagnosis of broken rotor bars in squirrel cage induction motor; Ref ...
doi:10.1371/journal.pone.0228324
pmid:32017780
pmcid:PMC6999895
fatcat:abchaej76jaevpexpefa6iabya
Fault Diagnosis and Prognosis of Mechatronic Systems Using Artificial Intelligence and Estimation Theory
2022
Electronics
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Industrial processes, manufacturing systems, transportation systems, and related mechatronic systems are becoming more and more complex and may fail, affecting the reliability, safety, and quality of industrial ...
Fast Fourier Transform and Multi-Layer Perceptron 6 Rolling bearing fault diagnosis Vibration Raw signal and 1D-Convolutional Neural Networks ...
Antennae Search (BAS) algorithm optimized Deep Belief Network (DBN). ...
doi:10.3390/electronics11213528
fatcat:qvusmsexfjf2radkbhqmmx37ky
Sensor Signal and Information Processing II
2020
Sensors
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methodologies such as deep learning, machine learning, compressive sensing, and variational Bayesian. ...
This Special Issue compiles a set of innovative developments on the use of sensor signals and information processing. ...
Acknowledgments: The authors of the submissions have expressed their appreciation to the work of the anonymous reviewers and the Sensors editorial team for their cooperation, suggestions and advice. ...
doi:10.3390/s20133751
pmid:32635516
fatcat:5ameltnk6baoldtldmt2ozdxl4
Faults and Diagnosis Methods of Permanent Magnet Synchronous Motors: A Review
2019
Applied Sciences
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Permanent magnet synchronous motors (PMSM) have been used in a lot of industrial fields. In this paper, a review of faults and diagnosis methods of PMSM is presented. ...
The research summarized in this paper mainly includes fault performance, harmonic characteristics, different time-frequency analysis techniques, intelligent diagnosis algorithms proposed recently and so ...
Deep learning models used in the field of fault diagnosis includes deep stacking network (DSN) [104] , deep belief networks (DBN) [105] , long short-term memory (LSTM) [106] and so on. ...
doi:10.3390/app9102116
fatcat:o727fgz3sjdjrgeq254f64a2vu
Research on Rolling Bearing Fault Diagnosis Method Based on Generative Adversarial and Transfer Learning
2022
Processes
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Firstly, the dataset is divided into the source and target domains, and the signals are transformed into pictures by continuous wavelet transform. ...
network with Resnet50 as the backbone for processing to extract similar features. ...
Acknowledgments: The authors would like to thank the companies involved in this article and their engineers for their help with the data required for this article. ...
doi:10.3390/pr10081443
fatcat:swxr2xoi5vfexlbrufqz7otmve
Basic research on machinery fault diagnostics: Past, present, and future trends
2017
Frontiers of Mechanical Engineering
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, signal processing, and intelligent diagnostics. ...
High-value machineries require condition monitoring and fault diagnosis to guarantee their designed functions and performance throughout their lifetime. ...
, and reproduction in any medium, provided the appropriate credit is given to the original author(s) and the source, and a link is provided to the Creative Commons license, indicating if changes were made ...
doi:10.1007/s11465-018-0472-3
fatcat:xngm4jcct5berhuf33rqoxfc6i
Method of state identification of rolling bearings based on deep domain adaptation under varying loads
2019
IET Science, Measurement & Technology
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The deep domain adaptation method integrates the convolutional and pooling theory with the deep belief network (DBN) that enables the construction of a convolutional Gaussian-Bernoulli DBN, which is used ...
Experimental results show that the proposed method can make full use of unlabelled data, mine the deep features of vibration signals, and reduce the divergence between data of the same state. ...
China (51805120), the Natural Science Foundation of Heilongjiang Province (LH2019E058), the University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province (UNPYSCT-2017091), and ...
doi:10.1049/iet-smt.2019.0043
fatcat:z5pt3fvlqncgdcfhte3swyaq4u
2020 Index IEEE Transactions on Instrumentation and Measurement Vol. 69
2020
IEEE Transactions on Instrumentation and Measurement
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Converter Using All-Digital Nested Delay-Locked Loops With 50-ps Resolution and High Throughput for LiDAR TIM Nov. 2020 9262-9271 Helsen, J., see Huchel, L., TIM July 2020 4145-4153 Hemavathi, N., ...
Meenalochani, M., and Sudha, S., Influence of Received Signal Strength on Prediction of Cluster Head and Number of Rounds; TIM June 2020 3739-3749 Hendeby, G., see Kasebzadeh, P., TIM Aug. 2020 5862 ...
., +, TIM July 2020 4387-4394
Belief networks
An Enhanced Intelligent Diagnosis Method Based on Multi-Sensor Image
Fusion via Improved Deep Learning Network. ...
doi:10.1109/tim.2020.3042348
fatcat:a5f4fsqs45fbbetre6zwsg3dly
A Fine-Grained Approach for EEG-Based Emotion Recognition Using Clustering and Hybrid Deep Neural Networks
2023
Electronics
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Additionally, we integrate the frequency domain and spatial features of emotional EEG signals and feed these features into a serial network that combines a convolutional neural network (CNN) and a long ...
In recent times, there has been a growing trend in using deep learning techniques for EEG emotion recognition. ...
Deep belief network (DBN), CNN and RNN are the most commonly used deep learning techniques for emotion recognition tasks, followed by multilayer perceptron neural network (MLPNN) [18] . ...
doi:10.3390/electronics12234717
fatcat:njy3ktctirgptdbek5s44vmoui
Fault Diagnosis of Planetary Gearbox Based on Motor Current Signal Analysis
2020
Shock and Vibration
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The convolutional neural network (CNN), which can automatically extract features, is also adopted. ...
Induction motor current signal analysis (MCSA) is a noninvasive method that uses current to detect faults. Currently, most MCSA-based fault diagnosis studies focus on the parallel shaft gearbox. ...
[27] used the deep belief network to fuse the vibration signal and current signal to classify the fault of a two-stage gearbox. ...
doi:10.1155/2020/8854776
fatcat:yak25unzd5hyhlyhymqxlbkywm
2020 Index IEEE/ACM Transactions on Audio, Speech, and Language Processing Vol. 28
2020
IEEE/ACM Transactions on Audio Speech and Language Processing
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., +, TASLP 2020 2848-2864
Belief networks
Simultaneous Tracking and Separation of Multiple Sources Using Factor
Graph Model. ...
., +, TASLP 2020 2412-2426 Sound Events Recognition and Retrieval Using Multi-Convolutional-Channel Sparse Coding Convolutional Neural Networks. ...
T Target tracking Multi-Hypothesis Square-Root Cubature Kalman Particle Filter for Speaker Tracking in Noisy and Reverberant Environments. Zhang, Q., +, TASLP 2020 1183 -1197 ...
doi:10.1109/taslp.2021.3055391
fatcat:7vmstynfqvaprgz6qy3ekinkt4
PHM SURVEY: Implementation of Prognostic Methods for Monitoring Industrial Systems
2022
Energies
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PHM uses methods, tools and algorithms for monitoring, anomaly detection, cause diagnosis, prognosis of the remaining useful life (RUL) and maintenance optimization. ...
More specifically, this paper establishes a state of the art in prognostic methods used today in the PHM strategy. ...
In the literature, the most recently used DL algorithms can be classified into tree main categories: Convolutional Neural Networks (CNN), Deep Belief Neural Networks (DBN), and long short-term memory networks ...
doi:10.3390/en15196909
fatcat:73kxor2hyncdvdu4mptk4pseyq
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
[article]
2021
arXiv
pre-print
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architectures, such as CNNs, RNNs, GNNs, and Transformers. ...
The last decade has witnessed an experimental revolution in data science and machine learning, epitomised by deep learning methods. ...
Acknowledgements This text represents a humble attempt to summarise and synthesise decades of existing knowledge in deep learning architectures, through the geometric lens of invariance and symmetry. ...
arXiv:2104.13478v2
fatcat:odbzfsau6bbwbhulc233cfsrom
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