An Industrial Robot Gearbox Fault Diagnosis Approach Using Multi-Scale Empirical Mode Decomposition and a One-Dimensional Convolutional Neural Network-Bidirectional Gated Recurrent Unit Method

To address the limitations of traditional methods in adapting to complex operating conditions, this paper proposes a fault diagnosis approach combining multi-scale empirical mode decomposition (MS-EMD) and a one-dimensional convolutional neural network (1D CNN) integrated with a bidirectional gated recurrent unit (BiGRU). The method incorporates multi-scale down-sampling to generate signals at different time scales, utilizes EMD to extract multi-frequency features, and selects key intrinsic mode functions (IMFs) based on frequency energy entropy, significantly enhancing the stability and representational capability of signal decomposition. The 1D CNN-BiGRU module ensures efficient integration of local feature extraction and sequence modeling. Initially, down-sampling is applied to produce signals at various time scales, followed by EMD to decompose these signals and obtain comprehensive IMFs. Key IMFs are then selected using frequency energy entropy, and signals are reconstructed to highlight critical features, effectively eliminating redundant components and noise. Next, the multi-scale reconstructed signals are fed into the 1D CNN, which automatically extracts local signal features to strengthen feature representation. A multi-channel design further improves the ability to capture multi-scale information. Finally, the extracted features are input into the BiGRU, which leverages its sequence modeling capabilities to learn and classify fault patterns. Experimental results show that this method achieves an average fault diagnosis accuracy of 99.58% for gearboxes under noisy conditions, demonstrating a significant improvement over traditional methods. This validates its robustness and efficiency in complex environments. By integrating multi-scale signal decomposition and fusion, adaptively selecting critical features, and utilizing deep learning for feature modeling, this method significantly enhances the fault diagnosis capability of vibration signals from industrial robot gearboxes, offering a new approach for achieving high-precision intelligent diagnostics.