.png){kind=icon}
Return
Adaptive variable sampling T-SSD algorithm
.png){kind=icon}
PRE
AI
DOI：10.1177/14759217241270912.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Addressing the challenges of non-unique decomposition outcomes and prolonged decomposition durations in the fault feature adaptive extraction algorithm based on tensor decomposition, this paper presents a novel algorithm called the adaptive variable sampling tensor singular spectrum decomposition (T-SSD) algorithm. The proposed approach centers on decomposing multichannel time series with adaptive sampling frequency, leveraging tensor singular value decomposition. Initially, the embedding dimension and the number of resampling points were optimized by power spectral density analysis and adaptive sampling algorithm. Subsequently, a third-order tensor is constructed based on the principle of tensor-tensor-preserving order multiplication, combining trajectory tensor construction and embedding dimension. Finally, the signal decomposition and reconstruction of multichannel component signals are achieved through adaptive sampling, interpolation, and complementary back steps. Experimental signal analysis indicates that this algorithm can better extract fault characteristics from signals compared to common signal processing algorithms. In comparison with the traditional T-SSD algorithm, this method significantly improves decomposition efficiency, particularly for low-frequency components. It effectively tackles the efficiency challenge caused by data redundancy, enabling the organic fusion and adaptive decomposition of multichannel signals.
Keywords:
Tensor decomposition
multichannel signals
adaptive decomposition
feature extraction
third-order tensor
Journal
S
IF:
5.7
Papers:
2.4K
Citations:
1.1W
Organization
Cited Papers
Semi-supervised multi-sensor information fusion tailored graph embedded low-rank tensor learning machine under extremely low labeled rate.png)
INFORMATION FUSION
INFORMATION FUSIONIF15.5
PREAI
ShareSaveShareSaveShareSaveShareSave
View more