Mechanism-Guided Multi-Sensor Diagnosis of Return Oil Filter Blockage in Excavator Hydraulic Systems Based on a WOA-MLP-GRU Network

The return oil filter in an excavator hydraulic system plays a critical role in maintaining oil cleanliness, and its blockage fault directly affects the thermal state and operational reliability of the machine. Owing to the progressive evolution and concealed characteristics of this fault, accurate identification remains challenging. To address this issue, this study proposes a return oil filter blockage fault identification model combining the whale optimization algorithm (WOA), multilayer perceptron (MLP), and gated recurrent unit (GRU). First, the hydraulic system configuration, operating process, and blockage mechanism of the return oil filter are analyzed. Then, multi-source operational variables are selected according to the fault propagation mechanism, while ACF and FFT analyses are employed to support signal denoising. In addition, variational mode decomposition (VMD) is introduced for representative pressure signals to provide supplementary evidence for fault evolution and the rationality of variable selection. On this basis, WOA is used to optimize the key hyperparameters of the hybrid model. Finally, the proposed scheme is systematically validated. Experimental results show that WOA-MLP-GRU achieves the best overall performance among the compared models, with an identification accuracy of 98.3%. Moreover, the proposed model exhibits lower validation loss, better feature separability, and a more concentrated error distribution, demonstrating superior training stability, robustness, and generalization capability.