Mean Shift Clustering-Based Analysis of Nonstationary Vibration Signals for Machinery Diagnostics
Bibliographic record
Abstract
Vibration analysis is a powerful tool for condition monitoring of rotating machinery. In the nonstationary case, this analysis often involves denoising and extraction of the time-varying harmonic components buried within the vibration signal. However, the complexity of many contemporary techniques—especially in relation to nonstationary signals—and their dependence on prior knowledge of the system kinematics in order to be effective is an inhibitor to autonomous fault detection and monitoring of nonstationary systems. In this article, a nonparametric, blind spectral preprocessing approach to simultaneously denoise and extract the harmonic content from nonstationary vibration signals is presented. The proposed approach utilizes mean shift clustering in conjunction with the short-time Fourier transform to separate time-varying harmonics from background noise within the frequency spectrum, without the need for a priori knowledge of the system. The technique is fully invertible, allowing the time signals corresponding to the separated time-varying harmonic and residual components to be reconstructed. The performance of the proposed technique is compared against existing preprocessing methods and validated using several industrial data sets: first, using vibration data obtained from a low-speed, nonstationary industrial automated people mover gearbox, next, using vibration data from an aircraft engine containing outer race faults, and finally, using nonstationary vibration data from a wind turbine containing frequent speed fluctuations.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".