Fault Classification of CWRU Rolling Bearing Dataset Using Convolutional Neural Networks (CNN)

Application of Convolutional Neural Networks in Mechanical Equipment Fault Diagnosis

Convolutional Neural Networks (CNN) have become an ideal choice for processing vibration signals and image data due to their powerful feature extraction capabilities. In the field of mechanical equipment health monitoring, the Case Western Reserve University (CWRU) rolling bearing dataset serves as a standard benchmark for evaluating fault diagnosis algorithms.

Data Characteristics and Preprocessing The CWRU dataset contains vibration signals from normal bearings and multiple fault types (such as inner race, outer race, and rolling element damage). The original data consists of one-dimensional time series, typically adapted for CNN input through the following methods: - Converting time-domain signals to time-frequency images (e.g., using Short-Time Fourier Transform or Wavelet Transform implementations like scipy.signal.stft or pywt.wavedec) - Directly reshaping 1D signals into 2D matrix formats using numpy.reshape operations - Standardization processing to eliminate dimensional influences via sklearn.preprocessing.StandardScaler

Network Architecture Design Key Points - Shallow convolutional kernels: Prioritize capturing high-frequency vibration features (such as fault impact components) using small kernel sizes (e.g., 3x3) - Pooling layers: Gradually compress feature dimensions while enhancing translation invariance through max-pooling or average-pooling operations - Fully connected layers: Integrate deep features for fault type discrimination using dense layers with activation functions - Dropout layers: Prevent overfitting in small sample data scenarios by implementing random neuron deactivation during training

Optimization Strategies in Practical Applications - Data augmentation: Expand training samples by adding Gaussian noise or applying time-shift transformations using libraries like librosa or numpy.roll - Transfer learning: Reuse feature extraction capabilities of ImageNet pre-trained models through frameworks like TensorFlow Hub or PyTorch Torchvision - Multi-sensor fusion: Improve accuracy by combining temperature, acoustic, and other multi-source data with sensor fusion algorithms

This technical solution has been validated in actual industrial scenarios, achieving over 95% accuracy in identifying early微弱 faults, significantly outperforming traditional spectrum analysis methods. Future developments could explore integrating attention mechanisms and other advanced deep learning innovations to further enhance performance.