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Non-negative tensor factorization for vibration-based local damage detection
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DOI：10.1016/j.ymssp.2023.110430.png)
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摘要
En 中文
In this study, a novel non-negative tensor factorization (NTF)-based method for vibration -based local damage detection in rolling element bearings is proposed. As the diagnostic signal registered from a faulty machine is non-stationary, the time-frequency method is frequently used as a primary decomposition technique. It is proposed here to extract multi-linear NTF-based components from a 3D array of time-frequency representations of an observed signal partitioned into blocks. As a result, frequency and temporal informative components can be efficiently separated from non-informative ones. The experiments performed on synthetic and real signals demonstrate the high efficiency of the proposed method with respect to the already known non-negative matrix factorization approach.
Keyword:
Fault detection
Bearings
Vibration
Non-negative matrix factorization
Non-negative tensor factorization
期刊
IF:
8.9
论文数:
1.3W
被引数:
6.6W
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引用论文
Local damage detection based on vibration data analysis in the presence of Gaussian and heavy-tailed impulsive noise.png)
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MEASUREMENTIF5.6
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