Hybrid Deep Learning for Hydraulic Cylinder Fault Diagnosis under Complex Conditions via Multi-Source Signal Fusion

Abstract

Hydraulic presses are indispensable in automotive and aerospace manufacturing, with hydraulic cylinders serving as key components for operational safety and product quality. Internal leakage faults in hydraulic cylinders are difficult to diagnose due to the scarcity of labeled data, the complexity of fault mechanisms, and the limited representation capability of single-signal methods under variable operating conditions. To address these issues, a hybrid deep learning feature fusion model based on displacement error and pressure signal, including convolutional autoencoder, multi-head attention mechanism, residual network and bidirectional long short time series neural network (CAEMRAB), is proposed for the diagnosis and classification of leakage faults in hydraulic cylinders. A hydraulic cylinder test system simulates heavy load, variable speed, and nonlinear motion under actual operating conditions. Through the all-round deep feature decoupling of the proposed model, the multi-source signal representation ability in complex and multi-noise environments is enhanced, effectively extracting the local and global features of displacement error and pressure signal fault data and achieving efficient classification. Experimental results show that CAEMRAB improves diagnostic accuracy by at least 3.87% over baseline models, while maintaining strong robustness across single-signal diagnosis, varying sample sizes, and noisy conditions. These findings demonstrate the effectiveness, reliability, and stability of the proposed approach for hydraulic cylinder fault diagnosis.