SSH-Net: A Deep Neural Network for Predicting Failure Time Distribution Functions under Competing Risks with Application to GPU Data
arXiv:2606.20451v1 Announce Type: cross 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 l
The increasing complexity of engineered systems and the availability of large datasets necessitate more sophisticated predictive models, pushing the development of AI solutions for reliability engineering.
Improved predictive analytics for system failures, especially in complex environments with competing risks, can significantly enhance operational efficiency, reduce downtime, and inform better design decisions across various industries.
This research introduces a novel deep neural network architecture designed to better handle the complexities of reliability engineering prediction, offering more robust and adaptable tools for industrial applications.
- · Reliability engineering sector
- · GPU manufacturers (indirectly)
- · AI/ML researchers
- · Traditional statistical modeling approaches for competing risks (relatively)
- · Industries with high maintenance costs due to unpredictable failures (if not ado
More accurate and flexible prediction of component and system failure times will become available.
Enhanced predictive maintenance strategies could lead to significant cost savings and improved safety in high-stress engineering environments.
The widespread adoption of such AI models could drive demand for more specialized hardware and software for industrial AI applications, fostering further innovation.
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Read at arXiv cs.LG