arXiv:2606.18882v1 Announce Type: cross Abstract: This work investigates the application of a domain-shift aware neural network for regression tasks aimed at estimating unbalance masses in rotating shafts under varying operating conditions. Experimental data were collected from a test rig in which a primary shaft, equipped with a flange carrying unbalanced masses, was driven at different rotational speeds, while a secondary shaft could be optionally activated to introduce domain discrepancy. The unbalance masses were positioned at a fixed radial distance, and the dynamic response of the system
The continuous advancements in AI and machine learning are enabling more sophisticated applications for predictive maintenance and industrial monitoring, making this a natural progression.
This development allows for improved predictive maintenance in complex industrial machinery, potentially leading to increased operational efficiency, reduced downtime, and enhanced safety across sectors dependent on rotating systems.
The ability to accurately characterize unbalance in rotating systems despite changing operating conditions through AI reduces reliance on traditional, less adaptive methods.
- · Industrial manufacturers
- · Predictive maintenance software providers
- · AI/ML industrial integrators
- · Traditional diagnostic equipment manufacturers
- · Maintenance service providers relying solely on reactive repairs
Companies using rotating machinery gain more precise and proactive maintenance capabilities.
Reduced operational costs and increased uptime across industries like aerospace, energy, and manufacturing drive further adoption of AI in industrial IoT.
The broader integration of AI for system monitoring could lead to more resilient and autonomous industrial operations, altering employment patterns in maintenance and operations.
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Read at arXiv cs.AI