A framework for reconciliating data clusters from a fleet of nuclear power plants turbines for fault diagnosis

作者：Al Dahidi Sameer; Di Maio Francesco^*; Baraldi Piero; Zio Enrico; Seraoui Redouane

来源：Applied Soft Computing, 2018, 69: 213-231.

DOI：10.1016/j.asoc.2018.04.044

• Fault diagnosis
• Unsupervised ensemble clustering
• Incremental learning
• Cluster reconciliation
• Fleet of nuclear power plants (NPPs) turbines shut-down

When a fleet of similar Systems, Structures and Components (SSCs) is available, the use of all the available information collected on the different SSCs is expected to be beneficial for the diagnosis purpose. Although different SSCs experience different behaviours in different environmental and operational conditions, they maybe informative for the other (even if different) SSCs. In the present work, the objective is to build a fault diagnostic tool aimed at capitalizing the available data (vibration, environmental and operational conditions) and knowledge of a heterogeneous fleet of P Nuclear Power Plants (NPPs) turbines. To this aim, a framework for incrementally learning different clusterings independently obtained for the individual turbines is here proposed. The basic idea is to reconciliate the most similar clusters across the different plants. The data of shut-down transients acquired from the past operation of the P NPPs turbines are summarized into a final, reconciliated consensus clustering of the turbines behaviors under different environmental and operational conditions. Eventually, one can distinguish, among the groups, those of anomalous behavior and relate them to specific root causes. The proposed framework is applied on the shut-down transients of two different NPPs. Three alternative approaches for learning data are applied to the case study and their results are compared to those obtained by the proposed framework: results show that the proposed approach is superior to the other approaches with respect to the goodness of the final consensus clustering, computational demand, data requirements, and fault diagnosis effectiveness.

• 出版日期2018-8