Data-Driven Fault Diagnosis in a Complex Hydraulic System based on Early Classification

In this paper, an early time-series classification (ETSC) algorithm is applied to support fault diagnosis in a complex hydraulic system (HS) with several interconnected components. The proposed technique aims at early classifying the state of the system while keeping the loss of classification inaccuracy at the minimum level. In contrast to baseline models that detect the eventual faults at the end of each working cycle, the ETSC model can diagnose any fault type of the HS components before observing the entire working cycle. Indeed, the early classification model successfully achieves a trade-off between the accuracy and the earliness criterion. Experimental results on a realistic HS dataset from the related literature show that the ETSC method can effectively identify different fault types with a higher accuracy and earlier compared to baseline methodologies. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)