Non-negative tensor factorization for vibration-based local damage detection

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.

Automated summary

In this study, a novel non-negative tensor factorization (NTF)-based method is proposed for vibration-based local damage detection in rolling element bearings. The time-frequency method is used to decompose the non-stationary diagnostic signal from faulty machines. Multi-linear NTF-based components are extracted from a 3D array of time-frequency representations of the observed signal, allowing for efficient separation of informative and non-informative components. Experiments on synthetic and real signals demonstrate the high efficiency of this method compared to the existing non-negative matrix factorization approach.