An Intelligent Fault Diagnosis Method Using GRU Neural Network towards Sequential Data in Dynamic Processes

Yuan, Jing; Tian, Ying

doi:10.3390/pr7030152

Cited by 42 publications

(18 citation statements)

References 27 publications

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“…The difference between GRU and LSTM is that one gate threshold is used to replace the inputting gate threshold and the forgetting gate threshold, that is, an “updating” gate threshold is used to control the state of the cell. The advantage of this method is that the calculation is simplified and the expression ability of the model is excellent ( Table 1 ) [ 86 , 87 ]. The parameters of the models above are shown in Table 1 .…”

Section: Methodsmentioning

confidence: 99%

A Labeling Method for Financial Time Series Prediction Based on Trends

Wang

et al. 2020

Entropy

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Time series prediction has been widely applied to the finance industry in applications such as stock market price and commodity price forecasting. Machine learning methods have been widely used in financial time series prediction in recent years. How to label financial time series data to determine the prediction accuracy of machine learning models and subsequently determine final investment returns is a hot topic. Existing labeling methods of financial time series mainly label data by comparing the current data with those of a short time period in the future. However, financial time series data are typically non-linear with obvious short-term randomness. Therefore, these labeling methods have not captured the continuous trend features of financial time series data, leading to a difference between their labeling results and real market trends. In this paper, a new labeling method called “continuous trend labeling” is proposed to address the above problem. In the feature preprocessing stage, this paper proposed a new method that can avoid the problem of look-ahead bias in traditional data standardization or normalization processes. Then, a detailed logical explanation given, a definition of continuous trend labeling was proposed and also an automatic labeling algorithm was given to extract the continuous trend features of financial time series data. Experiments on the Shanghai Composite Index and Shenzhen Component Index and some stocks of China showed that our labeling method is a much better state-of-the-art labeling method in terms of classification accuracy and some other classification evaluation metrics. The results of the paper also proved that deep learning models such as LSTM and GRU are more suitable for dealing with the prediction of financial time series data.

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Section: Methodsmentioning

confidence: 99%

A Labeling Method for Financial Time Series Prediction Based on Trends

Wang

et al. 2020

Entropy

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“…GRU solves the problem with its two gates named update and reset to manage the succession of data in the time of learning them for predicting through the information organized in a long chain [45]. Update gate receives the input and sort them out before hand over to memory [42].…”

Section: Figure 2 Graph Of Entropy Ft Probabilitymentioning

confidence: 99%

Predicting machine failure using recurrent neural network-gated recurrent unit (RNN-GRU) through time series data

Zainuddin

Hasan

2021

Bulletin EEI

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Time series data often involves big size environment that lead to high dimensionality problem. Many industries are generating time series data that continuously update each second. The arising of machine learning may help in managing the data. It can forecast future instance while handling large data issues. Forecasting is related to predicting task of an upcoming event to avoid any circumstances happen in current environment. It helps those sectors such as production to foresee the state of machine in line with saving the cost from sudden breakdown as unplanned machine failure can disrupt the operation and loss up to millions. Thus, this paper offers a deep learning algorithm named recurrent neural network-gated recurrent unit (RNN-GRU) to forecast the state of machines producing the time series data in an oil and gas sector. RNN-GRU is an affiliation of recurrent neural network (RNN) that can control consecutive data due to the existence of update and reset gates. The gates decided on the necessary information to be kept in the memory. RNN-GRU is a simpler structure of long short-term memory (RNN-LSTM) with 87% of accuracy on prediction.

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“…RNNs have been mainly used for fault prognosis and only a relatively small number of works focus on their application to fault diagnosis. Some examples are (Li et al, 2018a;Li et al, 2018bQiu et al, 2019 for bearings Zhao Q. et al, 2018 Yuan andTian, 2019), for chemical processes control [see Tenessee Eastman dataset (Chen, 2019)] and (Lei et al, 2019) for wind turbines.…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

“…These methods can be divided into two categories: "RNN + classifier" and end-to-end approaches. The works of Li et al (2018aLi et al ( , 2018b and Yuan and Tian (2019) belong to the first category. The first employs an LSTM-based architecture to extract informative features from the input data.…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

“…For example, Janssens et al (2018) ; Jia et al (2019) use CNNs to perform fault diagnosis of rotating machinery based on infrared thermal videos and images respectively. Yuan et al (2018) propose a method that fuses features extracted from different data structures, including infrared images, for CNN-based fault classification of a rotor system.…”

Section: Artificial Intelligence-based Prognostic and Health Managemementioning

confidence: 99%

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Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

Biggio

Kastanis

2020

Front. Artif. Intell.

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Prognostic and Health Management (PHM) systems are some of the main protagonists of the Industry 4.0 revolution. Efficiently detecting whether an industrial component has deviated from its normal operating condition or predicting when a fault will occur are the main challenges these systems aim at addressing. Efficient PHM methods promise to decrease the probability of extreme failure events, thus improving the safety level of industrial machines. Furthermore, they could potentially drastically reduce the often conspicuous costs associated with scheduled maintenance operations. The increasing availability of data and the stunning progress of Machine Learning (ML) and Deep Learning (DL) techniques over the last decade represent two strong motivating factors for the development of data-driven PHM systems. On the other hand, the black-box nature of DL models significantly hinders their level of interpretability, de facto limiting their application to real-world scenarios. In this work, we explore the intersection of Artificial Intelligence (AI) methods and PHM applications. We present a thorough review of existing works both in the contexts of fault diagnosis and fault prognosis, highlighting the benefits and the drawbacks introduced by the adoption of AI techniques. Our goal is to highlight potentially fruitful research directions along with characterizing the main challenges that need to be addressed in order to realize the promises of AI-based PHM systems.

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An Intelligent Fault Diagnosis Method Using GRU Neural Network towards Sequential Data in Dynamic Processes

Cited by 42 publications

References 27 publications

A Labeling Method for Financial Time Series Prediction Based on Trends

A Labeling Method for Financial Time Series Prediction Based on Trends

Predicting machine failure using recurrent neural network-gated recurrent unit (RNN-GRU) through time series data

Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

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