SSH-Net: A Deep Neural Network for Predicting Failure Time Distribution Functions under Competing Risks with Application to GPU Data

2026-06-18

AI Summary

This study addresses the challenge of modeling competing risks in complex engineering systems, where hierarchical structural data hinder accurate characterization of cause-specific failure time distributions using conventional methods. To overcome this limitation, the authors propose the Structured Stratified Hazard Network (SSH-Net), a deep neural network architecture that aligns its structure with the data hierarchy by employing independent subnetworks to differentially model distinct covariate groups. SSH-Net directly outputs cause-specific hazard functions within a competing risks framework and is trained using a penalized log-likelihood loss. Comprehensive evaluation via Brier score, AUC, and RMSE demonstrates that SSH-Net significantly improves the accuracy of failure time distribution prediction, as validated on both simulated data and real-world failure records from Titan GPUs.

Abstract

Competing risks are commonly observed in engineering fields and can bring challenges to time-to-event data modeling when the application scenarios are complicated. Recently, deep neural networks have received great attention for prediction with competing risks, due to their flexibility and high learning capability. However, the complexity of neural network structure brings extra difficulty in hyperparameter tuning based on different data inputs. Additionally, when an engineered system has complex physical structures with multiple hierarchical levels, treating all structural levels as a single group of inputs may fail to capture critical information. To address the issues, we propose a Structured Segmented Hazard Deep Neural Network (SSH-Net) for failure time prediction under cause-specific competing risks framework. Our approach associates neural network structure with data structures, and allows different covariate groups to impact the failure prediction through separate sub-networks. The neural network is constructed based on a cause-specific competing risks model. The SSH-Net outputs cause-specific hazard functions, and utilizes the penalized log-likelihood as the loss function. The prediction accuracy of SSH-Net is validated through simulation studies by evaluating the Brier score, the area under receiver operating characteristic curves (AUC), and the root mean square error (RMSE) of the predicted cause-specific cumulative incident function. We further demonstrate the model's ability to predict failure time distribution functions using the Titan GPU failure time data.

Problem

• competing risks
• failure time prediction
• hierarchical data structure
• cause-specific hazard
• time-to-event modeling

Innovation

• SSH-Net
• competing risks
• structured neural network
• cause-specific hazard
• failure time prediction