Industrial predictive maintenance for ultra-high-pressure waterjet equipment faces a critical challenge: traditional survival models relying on the constant proportional hazards assumption fail to capture the nonlinear, time-varying failure mechanisms inherent to complex multi-sensor industrial systems. This paper aims to address this limitation by proposing SurvWaterJet, a novel deep survival analysis framework tailored for the web-enabled waterjet predictive maintenance system, enabling real-time failure risk prediction and remote monitoring for Industrial Internet of Things (IIoT)-connected waterjet cutting equipment.
SurvWaterJet integrates three complementary components: (1) a nuclear norm imputation module that exploits the low-rank structure of device feature matrices to recover missing sensor values with theoretical convergence guarantees; (2) a Parallel-Encoder with multi-head self-attention that explicitly models dynamic coupling among nine heterogeneous physical sensor signals; and (3) a Kolmogorov–Arnold Network sub-network with learnable B-spline activations, combined with a Piecewise Constant Hazard loss function, to approximate highly nonlinear lifecycle degradation trajectories.
Evaluated on a real-world WaterJet Dataset collected from 15 factory-deployed devices across 30 independent runs, SurvWaterJet achieves a Concordance Index of 0.738 (+18.3% over CPH), an integrated Brier score of 0.108 and a peak time-dependent AUC of 0.765 at the high-load operating phase (t = 1200 h), outperforming representative baselines (e.g. DeepSurv, DSM, SurvTrace, etc.) across all six evaluation metrics. The deployable RESTful API enables real-time failure risk prediction with sub-100 ms latency for up to 50 concurrent IIoT-connected devices.
To the best of the authors’ knowledge, this study presents the first application of Kolmogorov–Arnold Networks within a deep survival analysis framework for industrial equipment maintenance, specifically optimized for a web-enabled waterjet predictive maintenance system. We demonstrate that learnable univariate function compositions provide superior approximation of nonlinear degradation phenomena compared to conventional fixed-activation deep models, while the end-to-end framework offers a principled, web-deployable solution for lifecycle-aware maintenance decision-making in IIoT environments. The proposed approach bridges advanced survival modeling with web-based operational intelligence, facilitating remote monitoring and proactive intervention for waterjet cutting equipment.
