Composite fault diagnosis of rolling bearings based on EMD-AADPCI vibration images

Introduction Bearing fault detection and prevention are crucial. However, traditional diagnostic methods generally suffer from insufficient accuracy when dealing with complex bearing faults. Therefore, developing new methods that can effectively characterize complex fault features and achieve high-precision diagnosis has significant theoretical and engineering value. Methods This study proposes a vibration image generation method based on Empirical Mode Decomposition-Adaptive Angle Distribution Polar Image (EMD-AADPCI) and constructs a hybrid diagnostic model combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). First, the vibration signal is processed by Empirical Mode Decomposition, and the designed adaptive angle distribution mechanism dynamically allocates polar coordinate angles according to the local features of the intrinsic mode functions, converting the signal into a two-dimensional vibration image containing rich fault information. Subsequently, a CNN-LSTM hybrid model is constructed. CNN extracts spatial and deep features from the image, and LSTM captures the temporal dependencies between features, ultimately achieving accurate classification of complex bearing faults. Results Experiments show that the proposed method significantly outperforms traditional methods. In terms of feature representation, the vibration images generated by EMD-AADPCI achieved a 20.25% improvement in fault classification accuracy compared to the comparative method MIC-SPCI (reaching 93.00%). The constructed CNN-LSTM model achieved a training accuracy of 94.88% with a loss rate as low as 1.43%. In the composite fault diagnosis task, the model achieved a classification accuracy of 98.00%. After 10 repeated experiments, the model achieved average accuracy, recall, and F1 score of 98.13%, 98.72%, and 98.33% for different composite fault diagnoses, respectively. Even in a low signal-to-noise ratio environment with strong noise interference (-4 dB), the model maintained a diagnostic accuracy of over 97%, demonstrating good robustness. Discussion The proposed EMD-AADPCI method can more effectively preserve and highlight fault-related information, while the CNN-LSTM hybrid model fully leverages the advantages of spatial feature extraction and time series modeling. Experimental results show that this method has extremely high accuracy and anti-interference ability in bearing composite fault diagnosis. This provides an effective and innovative solution for intelligent diagnosis and preventive maintenance of complex faults in bearings and other rotating machinery, and has good prospects for widespread application.