New approach for systems monitoring based on semi-supervised classification

In this paper, we consider the problem of fault diagnosis for systems with many possible functioning modes. A new methodology has been proposed combining both supervised and unsupervised learning methods. Since supervised learning requires necessarily a broad labelled base that may not always available in a sufficient cardinality, we aim at first an unsupervised grouping of a critical faults set (classes) though a Self-Adaptive Clustering Algorithm (SACA). Within this framework, the presented algorithm is based on the evaluation of a metric distance between cluster centroids and samples. An integrated process for optimization allows the tuning of confidence threshold for decision. Next, an additional supervised classification step using Artificial Neural Network (ANN) provides practical information for decision-making. The network is trained according to the classification multi-levels dedicated for multi-class problems. The developed approach is assessed on a hydraulic system consisting of three connected tanks.