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

TL;DR

This paper introduces a multi-head attention fusion neural network that models degradation, operating states, and noise for improved prognostics of complex systems under changing conditions.

Contribution

It presents a novel architecture combining BiLSTM and attention mechanisms to explicitly model operational effects and sensor data interactions in prognostic modeling.

Findings

The method effectively captures complex temporal dependencies.

It improves prognostic accuracy on NASA dataset.

The fusion approach enhances modeling of operational states.

Abstract

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 more 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. The proposed framework explicitly models and integrates three signal components: (1) the monotonic degradation trend, which reflects the underlying deterioration of the system; (2) discrete operating states, identified through clustering and encoded into dense embeddings; and (3) residual random noise, which captures unexplained variation in sensor measurements. The core strength of the framework lies in its architecture, which combines BiLSTM networks with attention mechanisms to better capture complex temporal dependencies. The attention mechanism allows the model to adaptively weight different time steps and sensor signals, improving its ability to extract prognostically relevant information. In addition, a fusion module is designed to integrate the outputs from the degradation-trend branch and the operating-state embeddings, enabling the model to capture their interactions more effectively. The proposed method is validated using a dataset from the NASA repository, and the results demonstrate its effectiveness.