A Multi-head Attention Fusion Network for Industrial Prognostics under Discrete Operational Conditions

ArXi:2604.10248v1 Announce Type: new Complex systems such as aircraft engines, turbines, and industrial machinery often operate under dynamically changing conditions. These varying operating conditions can substantially influence degradation behavior and make prognostic modeling challenging, as accurate prediction requires explicit consideration of operational effects. To address this issue, this paper proposes a novel multi-head attention-based fusion neural network.