Sensitivity analysis by differential importance measure for unsupervised fault diagnostics

Giovanni Floreale, Piero Baraldi, Xuefei Lu, Paolo Rossetti and Enrico Zio

Reliability Engineering and System Safety, 2024, vol. 243, issue C

Abstract: Fault diagnostic approaches based on supervised classifiers are difficult to apply to safety-critical or new design systems, because they require the availability of labelled data collected when the systems operate abnormally, which is a rare situation. To address this challenge, we develop a novel unsupervised method for fault diagnostics based on a fault detection module and on sensitivity analysis . Specifically, the Differential Importance Measure (DIM) is originally used to quantify how much a signal is, or a set of signals are, responsible for the variation of the system health state. The proposed method is tested on simulated data from a wind turbine and on real data from a gas turbine. The advantages of the proposed fault diagnostic method are: (1) it can be developed using only normal condition data (2) it allows identifying the component responsible for the abnormality by quantifying the contribution of groups of signals to the variation of the system health state; (3) it is capable of distinguishing the abnormalities caused by changes in external conditions from those caused by components malfunctions; (4) it can be used in combination with any fault detection technique.

Keywords: Condition monitoring; Fault diagnostics; Sensitivity analysis; Differential Importance Measure; Wind turbine (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:243:y:2024:i:c:s0951832023007603

DOI: 10.1016/j.ress.2023.109846

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