Application of recurrent neural network to mechanical fault diagnosis: a review

Zhu, Junjun; Jiang, Quan; Shen, Yehu; Qian, Chenhui; Xu, Fengyu; Zhu, Quanxin

doi:10.1007/s12206-022-0102-1

Cited by 78 publications

(35 citation statements)

References 108 publications

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“…With the development of the Internet, big data technology is becoming more and more mature, and the combination of e-commerce and big data has an impact on the traditional medical device industry, gradually transforming the marketing mode of wide distribution and high investment to an accurate and refined marketing mode that can improve the return on investment, actively tap the needs of consumers, and meet the personalized needs of consumers. At present, the key problems in the economic environment are that the competition in the industry is more complex, the previous marketing model is relatively weak, and the phenomenon of homogeneity is more serious [10]. The biggest problem with the marketing methods of electronic part market is the thinness of marketing forms, and most of them still adopt the traditional marketing system as the center [11].…”

Section: Introductionmentioning

confidence: 99%

Precision Marketing Optimization Model of e-Commerce Platform Based on Collaborative Filtering Algorithm

2022

Wireless Communications and Mobile Computing

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e-commerce mode shows great modern commercial value. In particular, online shopping has become a fashion and trend for people because of its convenience and rapidness. How to find the information users that need accurately and quickly in the increasing network information and recommend products is a big problem. Although precision marketing was mainly used in e-commerce activities in the past, due to factors such as the technical basis and data analysis ability at that time, there was not enough technical ability and theoretical basis to deeply mine and make use of the existing data. The collaborative filtering algorithm is one of the most widely used and successful recommendation techniques, but it has obvious defects. In this paper, the nearest neighbor collaborative filtering recommendation algorithm based on statistical eigenvalue classification is proposed in the collaborative filtering algorithm. By calculating the similarity between items, the user’s rating of unrated items is preliminarily predicted, the nearest neighbor of items is formed, and the classified cluster of items is formed. The matrix is filled by the similarity between related items. The cold treatment problem is solved under the optimization of the ant colony algorithm. In the experiment of the model, the optimization rate for the cold start problem is 87.3%.

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Section: Introductionmentioning

confidence: 99%

Precision Marketing Optimization Model of e-Commerce Platform Based on Collaborative Filtering Algorithm

2022

Wireless Communications and Mobile Computing

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“…Recurrent neural network (RNN) is a kind of neural network with the capacity for short-term memory [ 9 ]. Its neurons have the ability to receive data from themselves as well as from other neurons.…”

Section: Deepar Modelmentioning

confidence: 99%

Remaining Useful Life Prediction of Rolling Bearings Using GRU-DeepAR with Adaptive Failure Threshold

Wang

Liu

et al. 2023

Sensors

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Aiming at the problem that a single neural network model has difficulty in accurately predicting trends of the remaining useful life of a rolling bearing, a method of predicting the remaining useful life of rolling bearings using a gated recurrent unit-deep autoregressive model (GRU-DeepAR) with an adaptive failure threshold was proposed. First, time domain and frequency domain features were extracted from the rolling bearing vibration signal. Second, its operation process was divided into a smooth operation stage and degradation stage according to the trend of the accumulated root mean square of maximum. Then, the failure threshold for different bearings were determined adaptively by the maximum of the smooth operation data. The degradation dataset of a rolling bearing was subsequently obtained. In the meantime, a GRU-DeepAR model was built to obtain predictions of the failure time and failure probability. Appropriate model parameters were determined after a large number of tests to assure the effectiveness and prediction accuracy. Finally, the trend of time series and failure times were predicted by inputting the degradation dataset into the GRU-DeepAR model. Experiments showed that the proposed method can effectively improve the accuracy of the remaining useful life prediction of a rolling bearing with good stability.

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“…Recurrent Neural Networks (RNNs) are an artificial neural network category that is specifically engineered to manage sequential data by analyzing every element in a sequence while retaining an internal state or memory of previous elements [125][126][127]. RNNs are particularly useful for natural language processing, speech recognition, time series prediction, and other applications that involve sequential or temporal data.…”

Section: Rnn For Renewable Energy Forecastingmentioning

confidence: 99%

Forecasting Renewable Energy Generation with Machine learning and Deep Learning: Current Advances and Future Prospects

Benti¹,

Chaka²,

Semie³

2023

Preprint

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This article presents a review of current advances and future prospects in the field of forecasting renewable energy generation using machine learning (ML) and deep learning (DL) techniques. With the increasing penetration of renewable energy sources (RES) into the electricity grid, accurate forecasting of their generation becomes crucial for efficient grid operation and energy management. Traditional forecasting methods have limitations, and thus ML and DL algorithms have gained popularity due to their ability to learn complex relationships from data and provide accurate predictions. This paper reviews the different approaches and models that have been used for renewable energy forecasting and discusses their strengths and limitations. It also highlights the challenges and future research directions in the field, such as dealing with uncertainty and variability in renewable energy generation, data availability, and model interpretability. Finally, this paper emphasizes the importance of developing robust and accurate renewable energy forecasting models to enable the integration of RES into the electricity grid and facilitate the transition towards a sustainable energy future.

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Application of recurrent neural network to mechanical fault diagnosis: a review

Cited by 78 publications

References 108 publications

Precision Marketing Optimization Model of e-Commerce Platform Based on Collaborative Filtering Algorithm

Precision Marketing Optimization Model of e-Commerce Platform Based on Collaborative Filtering Algorithm

Remaining Useful Life Prediction of Rolling Bearings Using GRU-DeepAR with Adaptive Failure Threshold

Forecasting Renewable Energy Generation with Machine learning and Deep Learning: Current Advances and Future Prospects

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