Use Paderborn data and HIilbert theory to diagnosis the fault of rolling bear with MATLAB.

the loader for common open source mechanical fault diagnosis datasets (CWRU, MFPT, SouthEast Uni., Paderborn Uni.)

A comprehensive repository for motor fault diagnosis experiments using the Paderborn Bearing Dataset. This project explores deep learning-based feature extraction, ensemble modeling (CNNs, Transformers), and data augmentation techniques to enhance fault classification. Includes automated pipelines for preprocessing, training, and evaluation.

Machine learning–based bearing fault diagnosis using the Paderborn dataset.

Bearing fault diagnosis using motor current & vibration signals from the Paderborn dataset, multi-class classification with traditional ML and SHAP explainability.

Machine learning methods using time-domain features were tested for bearing fault detection with CWRU and Paderborn data. SVM gave the best accuracy, while 1D-CNNs on raw signals also performed strongly. Combining classical ML and deep learning improved overall fault diagnosis.

A deep learning pipeline for bearing fault diagnosis using the Paderborn dataset. Combines a CNN-based Autoencoder for anomaly detection and a CNN classifier for multi-class fault classification (Healthy, Outer Race, Inner Race and ball fault). Uses envelope FFT features from vibration signals and is designed for industrial maintenance

Vibration Signal Image Encoding and CNN for Bearing Fault Diagnosis using the Paderborn dataset. Converts 1‑D vibration signals into STFT spectrogram images and trains a lightweight 2D CNN for binary classification of healthy vs faulty rolling‑element bearings, implemented entirely in a single Jupyter/Colab notebook.