Journal ArticleDOI
Degradation evaluation of slewing bearing using HMM and improved GRU
TLDR
Experiments comparing with several algorithms show that the proposed methods can effectively evaluate the health condition of the slewing bearing.About:
This article is published in Measurement.The article was published on 2019-11-01. It has received 47 citations till now. The article focuses on the topics: Slewing bearing.read more
Citations
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Journal ArticleDOI
Failure prediction, monitoring and diagnosis methods for slewing bearings of large-scale wind turbine: A review
TL;DR: Current situation of researches on wind turbine slewing bearing is summarized systematically and failure prediction, monitoring and diagnosis methods of slewing bearings for industries are reviewed and summarized, which can be potentially used for wind energy industry.
Journal ArticleDOI
Meta deep learning based rotating machinery health prognostics toward few-shot prognostics
TL;DR: Meta learning is introduced into this field for the first time, and meta deep learning (MDL) based health prognostic methodologies toward few-shot prognostics are further proposed, and appealing predictions compared with existing methods demonstrate the superiority of the proposed MDL health progNostics.
Journal ArticleDOI
Mechanical fault time series prediction by using EFMSAE-LSTM neural network
TL;DR: The experimental results prove the availability and superiority of the method combining error fusion of multiple sparse auto-encoders with long short-term memory for predicting mechanical fault time series.
Journal ArticleDOI
Health condition monitoring of machines based on long short-term memory convolutional autoencoder
Zhuang Ye,Jianbo Yu +1 more
TL;DR: Wang et al. as mentioned in this paper proposed a long short-term memory convolutional autoencoder (LSTMCAE), where LSTM and CNN units are embedded in a specific network for feature learning from sensor signals based on unsupervised learning.
Journal ArticleDOI
Stationary subspaces-vector autoregressive with exogenous terms methodology for degradation trend estimation of rolling and slewing bearings
Peng Ding,Minping Jia,Xiaoan Yan +2 more
TL;DR: This work explores internal dynamic structural regression based prognostics containing establishing multi-endogenous degradation indicators with weak-stationary traits and an interpretable and lightweight vector autoregression based DTE modeling method which achieves not only high-accurate prediction results but also fast-computing speed and reasonable mathematical supports.
References
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Moth-flame optimization algorithm
TL;DR: The MFO algorithm is compared with other well-known nature-inspired algorithms on 29 benchmark and 7 real engineering problems and the statistical results show that this algorithm is able to provide very promising and competitive results.
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Long short-term memory neural network for traffic speed prediction using remote microwave sensor data
TL;DR: A comparison with different topologies of dynamic neural networks as well as other prevailing parametric and nonparametric algorithms suggests that LSTM NN can achieve the best prediction performance in terms of both accuracy and stability.
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Machinery health prognostics: A systematic review from data acquisition to RUL prediction
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The local mean decomposition and its application to EEG perception data.
TL;DR: The paper presents the results of applying LMD to a set of scalp electroencephalogram (EEG) visual perception data, and suggests that there is a statistically significant difference between the theta phase concentrations of the perception and no perception EEG data.
Journal ArticleDOI
Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks
TL;DR: Inspired by the success of deep learning methods that redefine representation learning from raw data, this work proposes local feature-based gated recurrent unit (LFGRU) networks, a hybrid approach that combines handcrafted feature design with automatic feature learning for machine health monitoring.