Enhanced Rolling Bearing Fault Diagnosis Using Multimodal Deep Learning and Singular Spectrum Analysis
Bibliographic record
Abstract
A decision-level multimodal fusion deep learning strategy is proposed for the effective fault detection of rolling bearings based on long-term fault signals collected from multiple sensors. First, key features are extracted from the multimodal signal set using singular spectrum analysis (SSA), and these features are transformed into a composite dataset that combines short-time Fourier transform (STFT) images and time series data. Based on this, a recursive gated convolutional neural network (RGCNN) is designed to process the STFT image data, while a 1D convolutional neural network (1DCNN) is specifically optimized for training with time series data. Furthermore, decision-level multimodal feature fusion is achieved by applying a weighted average method to integrate the features from different deep learning models, aiming to obtain more comprehensive fault prediction results. The proposed method, multimodal fusion fault detection (MFFD), is validated on the Paderborn and Ottawa rolling bearing datasets, which include various typical faults. Experimental results demonstrate the effectiveness of the proposed approach. Compared to traditional single-modality deep learning models, the proposed method shows significant improvements in fault diagnosis accuracy and generalization capability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".