Fault Diagnostics by Conceptors-Aided Clustering

Fault diagnostics in practice faces the challenge of dealing with unlabelled time series that have long-term temporal dependencies. Inspired by the idea of representing temporal patterns by a mechanism of neurodynamical pattern learning, called Conceptors, we propose an unsupervised clustering method for identifying the degradation state of industrial equipment. Conceptors are used to represent the dynamic behaviour of the degradation trajectories and spectral clustering is used to group the Conceptors in homogenous classes of similar degradation states. The proposed method is applied to a case study of literature. The results show that the accuracy of the fault diagnosis is satisfactory.