Variational Mode Feature Construction-Based Improved Kernel Extreme Learning Machine for Rotating Machinery Intelligent Diagnosis
Bibliographic record
Abstract
The complex operational environment brings challenges to vibration signal-based rotating mechanical equipment fault identification. On the one hand, the fault features under heavy background noise reveal weakness and nonstationary characteristics, which makes traditional spectral-based methods impuissant to extract effective features. On the other hand, the rotating equipment is served in normal condition for most of life, resulting in collected samples appearing small quantity and class imbalance. To deal with these issues, this article proposes a variational mode feature construction-based improved kernel extreme learning machine (VMF-IKELM) for bearing and gear fault identification. The methodology involves the following three steps. First, an adaptive variational mode decomposition (AVMD) is introduced to extract the nonstationary intrinsic features (IFs) from raw signals. Then, the typical indicators of IFs are calculated and reshaped to construct the variational mode samples to reflect the nonstationary characteristics from multiple aspects. Finally, the representative samples are input into VMF-IKELM optimized with particle swarm optimization (PSO) for rotating machinery intelligent diagnosis. Experimental study verified that this architecture can extract effective IFs and accomplish further high-precision intelligent fault diagnosis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".