Intelligent Faults Diagnosis of Variable Operating Condition Bearing Based on Order Analysis and Deep Residual Networks
Bibliographic record
Abstract
As an important component of rotating mechanical systems, rolling bearings greatly affect the safety and reliability of equipment operation. If rolling bearings fail during system operation, it will not only lead to a decline in equipment performance but also, in extreme cases, cause system failure or shutdown, resulting in more serious economic losses or even casualties. Therefore, fault diagnosis of rolling bearings is of great significance. However, due to the complex working environment, accurate fault diagnosis of rolling bearings faces significant challenges. For example, in reality, there are often variable operating conditions, and changes in equipment speed can lead to signal “spectrum damage,” making it difficult to obtain effective information for fault identification and extraction. This ultimately makes it difficult to identify bearing faults. This research proposes an order analysis and deep residual network based fault diagnosis technique for rolling bearings working under changeable conditions. Firstly, the original vibration signal and speed signal data of the bearing are preprocessed using order analysis method to generate the corresponding target data set. Then, the deep residual network model is trained and fine-tuned on the generated target data set. Finally, the fine-tuned deep residual network model is applied to fault diagnosis. The method is validated on a variable operating condition bearing data set from the University of Ottawa in Canada, and the results show that the method can achieve a 99.4% accuracy rate in fault diagnosis of variable operating condition bearings, demonstrating promising application prospects.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 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".