Intelligent Fault Diagnosis for Rotating Machinery Utilizing Gramian Angular Field-Parallel Convolutional Neural Network and Gated Recurrent Unit Networks

To address the limitations of traditional fault diagnosis methods for rotating machinery, which heavily rely on single-dimensional vibration data and fail to fully exploit the deep features of time-series data, this study proposes an innovative diagnostic model that integrates Gramian Angular Field-Parallel Convolutional Neural Network (GAF-PCNN) with Gated Recurrent Units (GRU). Specifically, one-dimensional vibration signals are first transformed into Gramian angular and difference fields as image representations using Gramian Angular Field (GAF). These two types of images are then input into parallel-configured PCNN modules for feature learning. The features extracted by the two CNN branches are weighted and fused to construct a combined feature sequence. This sequence is subsequently fed into the GRU network to capture temporal dependencies and perform deep feature extraction. In this process, an integrated self-attention mechanism is applied to dynamically select key features. The proposed method is validated using two publicly available datasets, including comparative and noise interference experiments. The results demonstrate that the proposed model excels in diagnostic accuracy, model generalization, and robustness against noise interference.