Fault Diagnosis with Machine Learning: Bearing & Gearbox Vibration Analysis in MATLAB

Fault Diagnosis with Machine Learning: Bearing & Gearbox Vibration Analysis in MATLAB is most useful as a research topic when the simulation is treated as an experiment rather than a demonstration. The central objective is machine-learning fault diagnosis from vibration signals for bearings and gearboxes. A strong study fixes the plant and test conditions, defines a baseline, changes one research factor at a time and reports numerical evidence alongside plots.

For doctoral and postgraduate work, the model should make every assumption visible: rated values, data sources, solver settings, controller sampling, initial conditions, boundary conditions and disturbance definitions. This makes the results easier to defend in a thesis, reproduce later and convert into a publication-oriented comparison.

1. Import labelled vibration signals with known operating conditions.
2. Segment signals consistently and prevent train/test leakage.
3. Extract time, frequency and time-frequency features or learn them directly.
4. Train baseline and advanced classifiers with cross-validation.
5. Test robustness across load/speed variations.
6. Report confusion matrices and class-wise metrics.

Use numerical metrics that map directly to the research objective. Recommended outputs for this topic include:

• accuracy
• precision/recall/F1
• confusion matrix
• ROC/AUC where applicable
• noise/load robustness
• inference time

To turn this topic into a stronger research contribution, start with one baseline and one proposed method, then extend the validation using domain adaptation, transformer features, few-shot diagnosis. The final results section should explain why the proposed method changes the engineering behaviour, not only whether the output curve looks smoother. Include failure cases or operating limits when they reveal the boundary of the method.

• domain adaptation
• transformer features
• few-shot diagnosis
• explainable fault classification