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Remaining Useful Life Prediction of Machinery based on K-S Distance and LSTM Neural Network
2019
International Journal of Performability Engineering
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Based on the Long and Short Term Memory (LSTM) theory, an online method of remaining useful life prediction is proposed. ...
The bearing life vibration data verification shows that the Kolmogorov-Smirnov distance is sensitive to the development and expansion of the defects. ...
Acknowledgments The original bearing full life vibration data was provided by the University of Cincinnati via its Intelligent Maintenance Center. ...
doi:10.23940/ijpe.19.03.p18.895901
fatcat:3ilmtg7zhjgm5bq6mkj7a7enle
Degradation assessment of bearing based on machine learning classification matrix
2021
Eksploatacja i Niezawodnosc
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In the broad framework of degradation assessment of bearing, the final objectives of bearing condition monitoring is to evaluate different degradation states and to estimate the quantitative analysis of ...
Machine learning classification matrices have been used to train models based on health data and real time feedback. ...
tool, long short-term memory and recurrent neural network. ...
doi:10.17531/ein.2021.2.20
fatcat:wcrzvh33m5aqroytctuw76xsne
Remaining Useful Life Estimation Using Long Short-term Memory Neural Networks and Deep Fusion
2020
IEEE Access
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INDEX TERMS Machine health monitoring, remaining useful life (RUL), long-short term memory, recurrent neural network, data compression. ...
Recently Long Short-Term Memory (LSTM) neural networks have been proposed to overcome these issues and in this paper we create a LSTM network and data fusion approach that can estimate the RUL with compressed ...
This paper proposes a deep learning end-to-end Long-short Term Memory network with data fusion (LSTM-Fusion) method for RUL estimation in prognostics. ...
doi:10.1109/access.2020.2966827
fatcat:hqgla5vqonbshmpjvulftgkliu
A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools
2018
Inventions
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health diagnosis applications [101], a DL method for signal recognition and the diagnosis of spacecraft [102], long short-term memory neural network (LSTMNN) for fault diagnosis and estimation of the ...
Inventions 2018, 3, 41 2 of 28 state automata [4], the continuous time RNN approach to dynamical systems [5], the RNN scheme for long short-term memory (LSTM) [6,7], the echo state network (ESN) approach ...
[150] addressed a Long Short-Term Memory-based Encoder-Decoder (LSTM-ED) algorithm to acquire an unsupervised health index (HI) for a system employing multi-sensor time-series data. ...
doi:10.3390/inventions3030041
fatcat:6qrwhmrl2bfwrgmovqvsyx5p3y
Transfer Learning for Prognostics and Health Management (PHM) of Marine Air Compressors
2021
Journal of Marine Science and Engineering
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Transfer learning has shown excellent capabilities in image classification problems. Little work has been done to explore and exploit the use of transfer learning in prognostics. ...
Due to the scarcity of enough reliable data, transfer learning is established as a successful approach to improve and reduce the need to labelled examples. ...
We are also grateful to the Norwegian University of Science and Technology, Norway, to support open access.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/jmse9010047
fatcat:5f3udtdi4vemdf6ut47wc3brue
Author Index
2021
2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)
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Bi, Xinjie Visibility Graph based Feature Extraction for Fault Diagnosis of Rolling Bearings [570256] Bin, Jie Optimization Method of Virtual Sand Table Background Server Based on Unity 3D [571175] C ...
Long Short-Term Memory Neural Network for PECVD Process Quality Prediction [570394] Xiang, Jiayang Adversarial Domain Adaptation for Transfer Fault Diagnosis of Roller Bearings based on Multi-MMD Alignment ...
Long Short-term Memory Network [570406] Yuan, Liu
-Yin 2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing) Full-Parameters Identification Technique of Attenuated Oscillations ...
doi:10.1109/phm-nanjing52125.2021.9612757
fatcat:h4xp5wbdvjdpvmsri2dktwupbi
Prediction of Bearings Remaining Useful Life Across Working Conditions based on Transfer Learning and Time Series Clustering
2021
IEEE Access
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ACKNOWLEDGEMENTS Many thanks to Prof. Mingjian Zuo from the University of Alberta, Canada, for multiple in-depth discussions. ...
In recent years, various deep learning techniques, e.g., convolutional neural networks (CNN) [6] , deep brief networks (DBN) [7] , long short term memory (LSTM) [8] and hybrid deep learning method ...
Prognostic and health management (PH-M) of rolling bearings play a key role in condition monitoring for whole machinery. ...
doi:10.1109/access.2021.3117002
fatcat:5oixynwvdvbnzgtgog27phtmxq
Prediction of Bearings Remaining Useful Life Across Working Conditions based on Transfer Learning and Time Series Clustering
2021
IEEE Access
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Recently, data-driven remaining useful life (RUL) prediction has become a promising tool in prognostics and health management for rolling bearings. ...
To solve this problem, a new bearing RUL prediction approach is proposed by utilizing the transfer learning strategy. ...
ACKNOWLEDGEMENTS Many thanks to Prof. Mingjian Zuo from the University of Alberta, Canada, for multiple in-depth discussions. ...
doi:10.1109/access.2021.3132353
fatcat:ahgvfvfg3zhrrm3corg4ib76bm
Bearing Fault Diagnosis Method Based on EEMD and LSTM
2020
International Journal of Computers Communications & Control
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The condition monitoring and fault detection of rolling bearing are of great significance to ensure the safe and reliable operation of rotating machinery system.In the past few years, deep neural network ...
(DNN) has been recognized as an effective tool to detect rolling bearing faults. ...
The long-term short-term memory (LSTM) recurrent neural network can remember the observation results of longterm sequence interval. ...
doi:10.15837/ijccc.2020.1.3780
fatcat:rqwkzvrsfrgf7pyr5u4rluk7pa
Challenges and Opportunities of AI-Enabled Monitoring, Diagnosis & Prognosis: A Review
2021
Chinese Journal of Mechanical Engineering
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With the development of artificial intelligence (AI), especially deep learning (DL) approaches, the application of AI-enabled methods to monitor, diagnose and predict potential equipment malfunctions has ...
AbstractPrognostics and Health Management (PHM), including monitoring, diagnosis, prognosis, and health management, occupies an increasingly important position in reducing costly breakdowns and avoiding ...
Acknowledgements The authors sincerely thanks to Zheng Zhou, Zuogang Shang, Chenye Hu, Hongbing Shang for all their help on this work. ...
doi:10.1186/s10033-021-00570-7
fatcat:rih6clm2d5fazdujdymq4jynba
Real-Time Bearing Remaining Useful Life Estimation based on the Frozen Convolutional and Activated Memory Neural Network
2019
IEEE Access
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INDEX TERMS Bearings, remaining useful life estimation, multi-scale convolutional network, long short time memory neural network. ...
is accomplished by the convolutional-memory neural network, which enables to connect the convolutional layer with the long-shorttime-memory layer together to predict the continuous bearing RUL. ...
The last part utilizes long short-time memory cells, thus historical bearing conditions can be studied in the network for future predictions. ...
doi:10.1109/access.2019.2929271
fatcat:iljysitqvfe3jeu3hi4dy7knoy
Recent advancements of signal processing and artificial intelligence in the fault detection of rolling element bearings: a review
2022
Journal of Vibroengineering
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This article will provide a summary of such methods, with a focus on vibration analysis techniques. The newest advancements in this field will be recognizable to readers of this article. ...
Anyone interested in defect diagnostics of rolling element bearings can utilize this material. ...
The upgraded classic recurrent neural network (RNN), Long Short-Term Memory (LSTM), can acquire the whole historical information of input data. ...
doi:10.21595/jve.2022.22366
fatcat:65wxjwjjwzhnjbmynwh2q63zlq
Remaining useful life assessment of slewing bearing based on spatial-temporal sequence
2020
IEEE Access
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And then, the RUL prediction model is presented by combing the ST indicators and long-short-term memory network (LSTM) to establish the relationship between the ST indicators and the RUL of slewing bearings ...
INDEX TERMS Balanced position, GAN, life prediction, slewing bearing, spurious fluctuation, ST-LSTM. ...
The long-short-term memory network (LSTM) was used to predict remaining useful life (RUL) of rolling element bearing and applied to the prediction of fault time series, which achieved good prediction result ...
doi:10.1109/access.2020.2965285
fatcat:6ku3sav5mjgfncgbsceloa2rie
Compound Fault Diagnosis of Rolling Bearing Based on ACMD, Gini Index Fusion and AO-LSTM
2021
Symmetry
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For such problems, a compound fault diagnosis method based on adaptive chirp mode decomposition (ACMD), Gini index fusion and long short-term memory (LSTM) neural network optimized by Aquila Optimizer ...
Due to the symmetry of the rolling bearing structure and the rotating operation mode, it will cause the coupling modulation phenomenon when it is damaged in multiple places at the same time, which makes ...
Long Short-Term Memory (LSTM) Network A long short-term memory (LSTM) network is a special recurrent neural network (RNN). ...
doi:10.3390/sym13122386
fatcat:to2fw7q2pfggfjvoxa7jlawilu
Remaining Useful Life Prediction Method for Bearings Based on LSTM with Uncertainty Quantification
2022
Sensors
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Therefore, this paper proposes a new bearing RUL prediction method based on long-short term memory (LSTM) with uncertainty quantification. ...
To reduce the economic losses caused by bearing failures and prevent safety accidents, it is necessary to develop an effective method to predict the remaining useful life (RUL) of the rolling bearing. ...
Long−short term memory (LSTM) unit structure.
The training set is utilized to train the established LSTM model to determine and optimize the model parameters during the model training stage. ...
doi:10.3390/s22124549
pmid:35746338
pmcid:PMC9228128
fatcat:o3qzo5aemfejbhpkk33dpj5p4a
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