An Integrated Approach to Condition-Based Maintenance Decision-Making of Planetary Gearboxes: Combining Temporal Convolutional Network Auto Encoders with Wiener Process

Abstract

With the increasing complexity of industrial automation, planetary gearboxes play a vital role in large-scale equipment transmission systems, directly impacting operational efficiency and safety. Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment, leading to excessive maintenance costs or potential failure risks. However, existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes. To address these challenges, this study proposes a novel condition-based maintenance framework for planetary gearboxes. A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals, which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features, enabling the collaborative extraction of long-period meshing frequencies and short-term impact features from the vibration signals. Kernel principal component analysis was employed to fuse and normalize these features, enhancing the characterization of degradation progression. A nonlinear Wiener process was used to model the degradation trajectory, with a threshold decay function introduced to dynamically adjust maintenance strategies, and model parameters optimized through maximum likelihood estimation. Meanwhile, the maintenance strategy was optimized to minimize costs per unit time, determining the optimal maintenance timing and preventive maintenance threshold. The comprehensive indicator of degradation trends extracted by this method reaches 0.756, which is 41.2% higher than that of traditional time-domain features; the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56, which is 8.9% better than that of the static threshold optimization. Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety. This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes, provides an interpretable solution for the predictive maintenance of complex mechanical systems, and promotes the development of condition-based maintenance strategies for planetary gearboxes.