Evaluation of Time-Frequency Representations for Deep Learning-Based Rotating Machinery Fault Diagnosis
Bibliographic record
Abstract
This study evaluates and compares five time-frequency representation (TFR) methods for fault diagnosis in rotating machinery, aiming to ensure operational reliability and reduce unexpected downtime. The methods—short-time Fourier transform (STFT), continuous wavelet transform (CWT), modified S-transform (MS-transform), smoothed pseudo Wigner-Ville distribution (SPWVD), and Hilbert-Huang transform (HHT)—are investigated. Vibration signals from benchmark bearing and gearbox datasets are converted into two-dimensional TFR data and classified using a convolutional neural network (CNN). Results show that MS-transform achieves the highest accuracy (up to 99.87%) under ideal conditions. STFT and CWT demonstrate better robustness in noisy environments, maintaining over 99% accuracy at 15 dB signal-to-noise ratio (SNR). SPWVD is computationally intensive with moderate performance, while HHT performs poorly under noise. Renyi entropy, energy conservation, and training time are also used to assess TFR quality. These findings support selecting appropriate TFR methods for industrial fault diagnosis.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".