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

arXiv: https://arxiv.org/abs/2403.12554 · HTML: https://mlsanity.com/paper/2403.12554/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2024.

## Built on by (most cited first, 1 of 1)

- [Sparse Hyperparametric Itakura-Saito Nonnegative Matrix Factorization via Bi-Level Optimization](https://mlsanity.com/paper/2502.17123/) (2025)

## Abstract (arXiv)

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.