Research on Vibration Signal Classification Method of the Key Equipment of Reactor Based on Open-set Recognition

In the actual engineering scenario of health monitoring of key equipment in reactors, the equipment status continues to deteriorate over time, and the types of monitoring data gradually increase. Traditional data-driven algorithms may experience accuracy degradation or even failure. In order to overcome the above problems, this paper proposes an open set signal classification method. Firstly, a variational coding classifier network is used to classify known classes (KCs) and learn the distribution of feature space to extract hidden features; Then the hidden features are fitted to a Weibull distribution, and whether the sample belongs to unknown classes (UCs) is determined based on Extreme Value Theory (EVT); Finally, a simulated open set experiment is conducted using a multi-class labeled vibration signal dataset collected during the actual operation of the reactor. The experimental results show that by selecting appropriate discrimination thresholds, effective recognition of KCs and UCs can be achieved. The method proposed in this article provides a feasible solution for data classification scenarios where equipment gradually transitions from normal state to unknown faults in practical engineering scenarios.