An Intelligent System for Bearing Fault Identification based on Gramian Multi-Resolution Dynamic Mode Decomposition
Bibliographic record
Abstract
This work introduces a single sensor intelligent end-to-end framework for fault detection based on Gramian-multi-resolution dynamic mode decomposition (GMrDMD). Vibration signals are transformed using gram matrix followed by spatial temporal decomposition based on multi-resolution dynamic mode decomposition (MrDMD). The gram matrix converts the 1D data into time evolving snapshot matrix which retains the relation of signal with time. This forms the input to the MrDMD framework which decomposes the system dynamics into hierarchically evolving fast and slow modes capable of isolating the transient fault characteristics. To handle sensor and environmental noise a robust total least square DMD algorithm is applied at each level of MrDMD. The resultant mode matrix is color coded and fed as image to a convolutional neural network (CNN) for classification. The performance of the designed method is verified on University of Ottawa dataset which contains five type of fault vibration signal under four different time varying rotational speed condition. The results demonstrate that of the proposed data driven method is effectively able to distinguish between different fault characteristics with an accuracy of 96.83%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".