A Noise Invariant Method for Bearing Fault Detection and Diagnosis Using Adapted Local Binary Pattern (ALBP) and Short-Time Fourier Transform (STFT)
Bibliographic record
Abstract
This study proposes a new method for bearing Fault Detection and Diagnosis (FDD) in Belt Starter Generators (BSGs) using vibration signals. The Adapted Local Binary Pattern (ALBP) method is introduced, and its performance is compared to the conventional Local Binary Pattern (LBP) technique and a Convolutional Neural Network with Support Vector Machine (CNN-SVM) model. Significantly, ALBP demonstrates superior accuracy without significantly increasing computational complexity, outperforming both LBP and the CNN-SVM model. The experimental setup involves a Specialty Motor Testing system, with vibration data collected using a single accelerometer under specific speed and torque conditions. The focus is on detecting and diagnosing bearing faults, such as lubrication and contamination, under various test conditions for Original Equipment Manufacturer (OEM) and After-Market (AM) bearings. ALBP achieves diagnostic accuracy ranging from 99% to 99.8%, representing a significant advancement in bearing FDD. Another novel aspect of this study is the training and testing of the model on separate days. This approach ensures the model’s robustness against data variability and domain shifts, unlike the traditional random data splitting method, which can yield misleadingly high accuracy on a portion of data but fails to generalize. Results show that ALBP achieves an average diagnosis accuracy of 99.4%, compared to 80% for the CNN-SVM model, further highlighting the superior performance of ALBP.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".