TF-Mamba: Intelligent fault diagnosis model for wind turbine bearings based on multimodal selective gating Mamba
Year: 2026
Type: article
Abstract: To address the non-stationarity of wind-turbine bearing vibration signals, the need for collaborative modeling of one-dimensional sequential features and two-dimensional visual features, and the computational cost of sequence modeling, this study proposes an intelligent fault diagnosis model based on multimodal selective-gating Mamba, termed TF-Mamba. A progressive multimodal fusion framework of “feature extraction–CoSSM–explicit selection–global decision-making” is constructed. Specifically, CoSSM enables cross-modal feature interaction through shared-parameter Mamba encoding and reciprocal gating. Bi-SG performs adaptive explicit selection of fused features along the channel and spatial dimensions, and EnSSM models global sequential dependencies for final decision-making. Experiments are conducted on CWRU and the State Grid Wind-turbine Dataset (SGWD), with BMTM-Net and WCamba included as recent baselines. The main comparison yields accuracies of 99.07% and 96.85%, respectively. Across ten repeated runs, TF-Mamba achieves mean accuracies of 99.02 ± 0.05 % on CWRU and 96.75 ± 0.12 % on SGWD. Paired permutation tests against BMTM-Net give Holm-adjusted p = 0.0156 for Accuracy and Macro-F1 on both datasets. In four leave-one-load-out CWRU evaluations, its mean accuracy across the held-out loads is 97.75%, with a mean Macro-F1 of 0.9757. A separate gate-substitution comparison gives Bi-SG an SGWD mean-accuracy advantage of 0.54 percentage points over CBAM. The network contains 2.81 million parameters and has a reported single-sample inference time of 4.68 ms on an RTX 4090. These results support the diagnostic value of progressive time–frequency collaboration and selective fusion under the evaluated dataset and load conditions.
Source: Energy Reports
Institutions: PowerChina (China), North China Electric Power University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,040
Funder: State Grid Corporation of China
OpenAlex ID: W7225407882