A two-stage estimation method based on Conceptors-aided unsupervised clustering and convolutional neural network classification for the estimation of the degradation level of industrial equipment

In practical applications, degradation level estimation is often facing the challenge of dealing with unlabeled time series characterized by long-term temporal dependencies, which are typically not properly represented using sliding time windows. Inspired by the idea of representing temporal patterns by a mechanism of neurodynamical pattern learning, called Conceptors, a two-stage method for the estimation of the equipment degradation level is developed. In the first stage, clusters of Conceptors representing similar patterns of degradation within complete run-to-failure trajectories are identified; in the second stage, the obtained clusters are used to supervise the training of a convolutional neural network classifier of the equipment degradation level. The proposed method is applied to a synthetic case study and to two literature case studies regarding bearings degradation level estimation. The obtained results show that the proposed method provides more accurate estimation of the equipment degradation level than other state-of-the-art methods.