Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks

Zhao, Rui; Wang, Dongzhe; Yan, Ruqiang; Mao, Kezhi; Shen, Fei; Wang, Jinjiang

doi:10.1109/tie.2017.2733438

Cited by 687 publications

(248 citation statements)

References 21 publications

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“…In addition to this, we summarize and group existing methods in Table 1. Local feature-based gated recurrent unit (LFGRU) networks have been proposed in [11] to predict machine condition by further processing of handcrafted features that aggregate time series information, using a gated recurrent unit network to learn richer feature representations. In order to predict defects and model degradation phenomena in renewable energy storages, the work of [25] introduces an error correction factor that enhances the grey model (GM) without increasing complexity.…”

Section: Predictive Algorithms In Industrial Processesmentioning

confidence: 99%

A Deep Learning framework for simulation and defect prediction applied in microelectronics

Dimitriou¹,

Leontaris²,

Vafeiadis³

et al. 2020

Simulation Modelling Practice and Theory

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The prediction of upcoming events in industrial processes has been a longstanding research goal since it enables optimization of manufacturing parameters, planning of equipment maintenance and more importantly prediction and eventually prevention of defects. While existing approaches have accomplished substantial progress, they are mostly limited to processing of one dimensional signals or require parameter tuning to model environmental parameters. In this paper, we propose an alternative approach based on deep neural networks that simulates changes in the 3D structure of a monitored object in a batch based on previous 3D measurements. In particular, we propose an architecture based on 3D Convolutional Neural Networks (3DCNN) in order to model the geometric variations in manufacturing parameters and predict upcoming events related to sub-optimal performance. We validate our framework on a microelectronics use-case using the recently published "PCB scans" dataset where we simulate changes on the shape and volume of glue deposited on an Liquid Crystal Polymer (LCP) substrate before the attachment of integrated circuits (IC). Experimental evaluation examines the impact of different choices in the cost function during training and shows that the proposed method can be efficiently used for defect prediction.

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Section: Predictive Algorithms In Industrial Processesmentioning

confidence: 99%

A Deep Learning framework for simulation and defect prediction applied in microelectronics

Dimitriou¹,

Leontaris²,

Vafeiadis³

et al. 2020

Simulation Modelling Practice and Theory

View full text Add to dashboard Cite

show abstract

“…For instance, recurrent neural networks (RNN) have been successful for the long-term prognosis of rolling bearing health status [30]. In [31], a local feature-based gated recurrent unit network is applied to tool wear prediction, gearbox fault diagnosis and bearing fault detection. The bidirectional recurrent structure proposed by the authors can access the sequential data in two directions-forward and backward-so that the model can fully explore the 'past and future' of each state.…”

Section: Related Workmentioning

confidence: 99%

Tool wear classification using time series imaging and deep learning

Martínez-Arellano

Terrazas

Ratchev

2019

Int J Adv Manuf Technol

164

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Tool condition monitoring (TCM) has become essential to achieve high-quality machining as well as cost-effective production. Identification of the cutting tool state during machining before it reaches its failure stage is critical. This paper presents a novel big data approach for tool wear classification based on signal imaging and deep learning. By combining these two techniques, the approach is able to work with the raw data directly, avoiding the use of statistical pre-processing or filter methods. This aspect is fundamental when dealing with large amounts of data that hold complex evolving features. The imaging process serves as an encoding procedure of the sensor data, meaning that the original time series can be re-created from the image without loss of information. By using an off-the-shelf deep learning implementation, the manual selection of features is avoided, thus making this novel approach more general and suitable when dealing with large datasets. The experimental results have revealed that deep learning is able to identify intrinsic features of sensory raw data, achieving in some cases a classification accuracy above 90%.

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“…GRU, which is a recently proposed architecture of RNN, is a simplified variant of LSTM [51]. There are mainly two changes in GRU.…”

Section: Grumentioning

confidence: 99%

Ensemble Recurrent Neural Network Based Probabilistic Wind Speed Forecasting Approach

et al. 2018

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Abstract:Wind energy is a commonly utilized renewable energy source, due to its merits of extensive distribution and rich reserves. However, as wind speed fluctuates violently and uncertainly at all times, wind power integration may affect the security and stability of power system. In this study, we propose an ensemble model for probabilistic wind speed forecasting. It consists of wavelet threshold denoising (WTD), recurrent neural network (RNN) and adaptive neuro fuzzy inference system (ANFIS). Firstly, WTD smooths the wind speed series in order to better capture its variation trend. Secondly, RNNs with different architectures are trained on the denoising datasets, operating as sub-models for point wind speed forecasting. Thirdly, ANFIS is innovatively established as the top layer of the entire ensemble model to compute the final point prediction result, in order to take full advantages of a limited number of deep-learning-based sub-models. Lastly, variances are obtained from sub-models and then prediction intervals of probabilistic forecasting can be calculated, where the variances inventively consist of modeling and forecasting uncertainties. The proposed ensemble model is established and verified on less than one-hour-ahead ultra-short-term wind speed forecasting. We compare it with other soft computing models. The results indicate the feasibility and superiority of the proposed model in both point and probabilistic wind speed forecasting.

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Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks

Cited by 687 publications

References 21 publications

A Deep Learning framework for simulation and defect prediction applied in microelectronics

A Deep Learning framework for simulation and defect prediction applied in microelectronics

Tool wear classification using time series imaging and deep learning

Ensemble Recurrent Neural Network Based Probabilistic Wind Speed Forecasting Approach

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