Rolling Element Bearing Fault Detection and Diagnosis with One-Dimensional Convolutional Neural Network

TL;DR

This paper presents a 1D CNN model for automatic fault detection in rolling element bearings using raw vibration data, achieving high accuracy across multiple datasets and load conditions, and demonstrating potential for real-time industrial monitoring.

Contribution

The study introduces a compact 1D CNN that eliminates manual feature extraction, achieving high accuracy in bearing fault diagnosis across diverse datasets and operational conditions.

Findings

Achieved over 95% accuracy on benchmark datasets.

Model generalizes well across different operational loads.

Hyperparameter tuning improves diagnostic performance.

Abstract

Rolling element bearings are critical components in rotating machinery, and their condition significantly influences system performance, reliability, and operational lifespan. Timely and accurate fault detection is essential to prevent unexpected failures and reduce maintenance costs. Traditional diagnostic methods often rely on manual feature extraction and shallow classifiers, which may be inadequate for capturing the complex patterns embedded in raw vibration signals. In this study, a compact one-dimensional convolutional neural network (1D CNN) is developed for automated bearing fault diagnosis using raw time-domain vibration data, eliminating the need for manual feature engineering. The model is trained and evaluated on two established benchmark datasets: the Case Western Reserve University (CWRU) dataset and the Paderborn University (PU) dataset. The CWRU data were segmented based on four distinct motor load conditions (0 HP to 3 HP), with each load scenario trained and tested independently to ensure strict separation and prevent data leakage. The CNN achieved high average test accuracies of 99.14%, 98.85%, 97.42%, and 95.14% for 0 HP, 1 HP, 2 HP, and 3 HP, respectively. On the PU dataset, known for its naturally induced faults and greater operational variability the model achieved a robust average testing accuracy of 95.63%. These results affirm the model ability to generalize across datasets and varying operating conditions. Further improvements were observed through hyperparameter tuning, particularly window length and training epochs, underscoring the importance of tailored configurations for specific datasets and load conditions. Overall, the proposed method demonstrates the effectiveness and scalability of 1D CNNs for real-time, data-driven bearing fault diagnosis, offering a reliable foundation for condition monitoring in industrial applications.