Method of vibration signal processing and load-type identification of a mill based on ACMD-SVD

doi:10.24425/gsm.2023.144626

Gospodarka Surowcami Mineralnymi - Mineral Resources Management

2023

DOI: 10.24425/gsm.2023.144626

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Method of vibration signal processing and load-type identification of a mill based on ACMD-SVD

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Advances in vibration analysis and modeling of large rotating mechanical equipment in mining arena: A review

Wang,

Xiong,

Duan

et al. 2023

AIP Advances

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Large-scale rotating mechanical equipment in the mining arena plays a pivotal role in mining production, where vibration issues directly influence production efficiency and safety. This Review aims to provide a comprehensive review of the latest advancements and methodologies related to the generation mechanisms, identification, and applications of vibrational characteristics in large-scale mining rotating mechanical equipment. Semi-autogenous mills, ball mills, and coal mills are selected as archetype equipment, and the Lagrangian motion equation is employed to unveil the generation mechanisms of vibrations and the embedded physical information in the signals of these machines. Initially, the research delves deeply into the acquisition, extraction, and identification of vibrational signal features, emphasizing that while mechanical vibration signals can reveal the internal operational state and fault information of machinery, there remains a need to enhance their capability to depict complex vibrational signals. Subsequently, this Review discusses in depth the studies focused on predicting the vibrational state of equipment by establishing accurate and reliable soft measurement models, pointing out that current models still have room for improvement in prediction accuracy and generalization capabilities. Conclusively, based on the elucidation of mechanical vibration mechanisms and the collation and outlook of the existing research study, the importance of on-site monitoring, deep learning, Internet of Things technology, and full lifecycle management is accentuated. To better support practical engineering applications, further exploration into the physical properties of vibrational signals and the mechanisms of mechanical vibrations is essential.

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Advances in vibration analysis and modeling of large rotating mechanical equipment in mining arena: A review

Wang,

Xiong,

Duan

et al. 2023

AIP Advances

View full text Add to dashboard Cite

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Technique for Defining the Optimal Parameters of Moving Window at Vibration Accelerometer Signal Processing

Fedoryshyn,

Lymych,

Zagraj

et al. 2024

Energy eng. control syst.

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This paper presents a technique for defining the optimal parameters of a moving window when processing the signal of a vibration accelerometer installed on a ball drum mill as part of the automation system. Time series signals of the vibration acceleration have been synthesized based on the experimental data of frequency spectrums with the application of the inverse Fourier transform. The lower and upper limits for the moving window size have been defined. The frequency spectrum for the time series signal within the moving window has been built by means of the fast Fourier transform method. An optimality criterion has been proposed. This criterion considers the quality of the derived frequency spectrum and the computational resources of the microprocessor system needed for processing the vibration accelerometer signal. The optimal duration of the moving window for the analyzed example is 100 ms. The impact of the time signal sampling rate on the frequency spectrum shape has been studied.

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Method of vibration signal processing and load-type identification of a mill based on ACMD-SVD

Cited by 2 publications

References 7 publications

Advances in vibration analysis and modeling of large rotating mechanical equipment in mining arena: A review

Advances in vibration analysis and modeling of large rotating mechanical equipment in mining arena: A review

Technique for Defining the Optimal Parameters of Moving Window at Vibration Accelerometer Signal Processing

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