Real-Time Diagnostics of Metal-Cutting Machines by Means of Recurrent LSTM Neural Networks

Munasypov, Rustem; Idrisova, Yu. V.; Masalimov, K. A.; Kudoyarov, R. G.; Fetsak, S. I.

doi:10.3103/s1068798x20050160

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…Liang et al [ 18 ] conceived a new method for reliability forecast based on radial basis function (RBF) and genetic algorithm (GA), which realized good results by selecting appropriate parameters and network architecture. Munasypov et al [ 19 ] used infinite impulse respond local recurrence neural network (IIR-LRNN) to forecast failures and forecast the reliability of engineering components and systems. This dynamic modeling technique is used for reliability forecast tasks for the first time.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Forecast of the Employment Situation of College Graduates Based on the LSTM Neural Network

Yang

2021

Computational Intelligence and Neuroscience

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Scientific and reasonable forecast model of graduates’ employment data can efficaciously embody the complex characteristics of graduates’ employment data and embody the nonlinear dynamic interaction of influencing elements of graduates’ employment situation. It has a strong and steady characteristic learning capability, thus selecting the main influence data that influence the change of graduates’ employment data. In this paper, according to the situation embodied by students’ employment, a data mining analysis model is set up by using the statistical method based on the model of cluster analysis technology to forecast the employment situation of graduates. In this paper, a forecast technique of graduates’ employment situation based on the long short-term memory (LSTM) recurrent neural network is conceived, including network structure design, network training, and forecast process implementation algorithm. In addition, aiming at minimizing the forecasting error, an LSTM forecasting model parameter optimization algorithm based on multilayer grid search is conceived. It also verifies the applicability and correctness of the LSTM forecasting model and its parameter optimization algorithm in the analysis of graduates’ employment situation.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Forecast of the Employment Situation of College Graduates Based on the LSTM Neural Network

Yang

2021

Computational Intelligence and Neuroscience

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Diagnostics of the Technological System State for Phosphorus Production Based on Deep Neural Networks

Puchkov

Lobaneva

Василькова

2021

Lecture Notes in Networks and Systems

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Classification-Based Parameter Optimization Approach of the Turning Process

Yang,

Jiang,

Yang

et al. 2024

Machines

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The turning process is a widely used machining process, and its productivity has a significant impact on the cost and profit in industrial enterprises. Currently, it is difficult to effectively determine the optimum process parameters under complex conditions. To address this issue, a classification-based parameter optimization approach of the turning process is proposed in this paper, which aims to provide feasible optimization suggestions of process parameters and consists of a classification model and several optimization strategies. Specifically, the classification model is used to separate the whole complex process into different substages to reduce difficulties of the further optimization, and it achieves high accuracy and strong anti-interference in the identification of substages by integrating the advantages of an encoder-decoder framework, attention mechanism, and major voting. Additionally, during the optimization process of each substage, Dynamic Time Warping (DTW) and K-Nearest Neighbor (KNN) are utilized to eliminate the negative impact of cutting tool wear status on optimization results at first. Then, the envelope curve strategy and boxplot method succeed in the adaptive calculation of a parameter threshold and the detection of optimizable items. According to these optimization strategies, the proposed approach performs well in the provision of effective optimization suggestions. Ultimately, the proposed approach is verified by a bearing production line. Experimental results demonstrate that the proposed approach achieves a significant productivity improvement of 23.43% in the studied production line.

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Real-Time Diagnostics of Metal-Cutting Machines by Means of Recurrent LSTM Neural Networks

Cited by 3 publications

References 2 publications

[Retracted] Forecast of the Employment Situation of College Graduates Based on the LSTM Neural Network

[Retracted] Forecast of the Employment Situation of College Graduates Based on the LSTM Neural Network

Diagnostics of the Technological System State for Phosphorus Production Based on Deep Neural Networks

Classification-Based Parameter Optimization Approach of the Turning Process

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