Comparative Analysis of SVM and ANN for Machine Condition Monitoring and Fault Diagnosis in Gearboxes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In large-scale manufacturing, ensuring the efficient operation of rotating machines is crucial to avoid breakdowns and failures during production.This article introduces a method for detecting gearbox faults by analyzing vibration signals and employing artificial intelligence techniques, with a particular emphasis on comparing these methods.The diagnostic process consists of three stages: extracting features using Wavelet Packet Transform (WPT) and statistical analysis, selecting optimal properties through the gain ratio method, and using Support Vector Machine (SVM) and Artificial Neural Network (ANN) models to distinguish between faults and assess their performance.The diagnostic outcomes demonstrate that both SVM and ANN models accurately identify various fault patterns depending on the operating conditions.Remarkably, the study highlights the ANN model's superiority over the SVM model in classifying gearbox faults, indicating its suitability for gearbox fault diagnosis.This research yields valuable insights into machine condition monitoring, showcasing the ANN model as a robust tool for gearbox fault detection.The findings advocate for the implementation of ANN-based approaches in real-world applications to enhance the reliability of fault detection and prevention in rotating machines.Furthermore, future research directions may explore additional enhancements and optimizations for ANN models, leading to more advanced machine health monitoring systems in the manufacturing industry.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it