Improving data quality using a deep learning network

Hwang, Chulhyun; Lee, Kyouhwan; Jung, Hoe-Kyung

doi:10.11591/ijeecs.v20.i1.pp306-312

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…This problem of backward coherence in RNN structure is presented as a solution by accompanying a memory cell RNN structure in LSTM structure. With this memory cell, information from the previous time can be taken and transferred to the next [32], [33]. These units in the LSTM network remember long or short time periods.…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

A hybrid of convolutional neural network and long short-term memory network approach to predictive maintenance

Nasser

Al-Khazraji

2022

IJECE

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<p>Predictive maintenance (PdM) is a successful strategy used to reduce cost by minimizing the breakdown stoppages and production loss. The massive amount of data that results from the integration between the physical and digital systems of the production process makes it possible for deep learning (DL) algorithms to be applied and utilized for fault prediction and diagnosis. This paper presents a hybrid convolutional neural network based and long short-term memory network (CNN-LSTM) approach to a predictive maintenance problem. The proposed CNN-LSTM approach enhances the predictive accuracy and also reduces the complexity of the model. To evaluate the proposed model, two comparisons with regular LSTM and gradient boosting decision tree (GBDT) methods using a freely available dataset have been made. The PdM model based on CNN-LSTM method demonstrates better prediction accuracy compared to the regular LSTM, where the average F-Score increases form 93.34% in the case of regular LSTM to 97.48% for the proposed CNN-LSTM. Compared to the related works the proposed hybrid CNN-LSTM PdM approach achieved better results in term of accuracy.</p>

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Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%