Proceedings of the

The 33rd European Safety and Reliability Conference (ESREL 2023)
3 – 8 September 2023, Southampton, UK

An Integrated Approach for Failure Diagnosis and Analysis of Industrial Systems Based on Multi-Class Multi-Output Classification: A Complex Hydraulic Application

Bahman Askari1,a, Yves Langeron2,d, Graziana Cavone3, Raffaele Carli1,b, Antoine Grall2,e and Mariagrazia Dotoli1,c

1Dept. of Electrical and Information Engineering, Polytechnic University of Bari, Italy.

2Computer Science and Digital Society (LIST3N) Laboratory, Université of Technologie de Troyes, France.

3Dept. of Civil, Computer Science, and Aeronautical Technologies Engineering, University Roma Tre, Italy.


For complex systems, a fault of one or several components does not necessarily lead to a failure of the system, but if the failed components are not immediately replaced, they may conduct some other components to an idle state. In this work, a data-driven model with a two-step decision approach is proposed to provide a comprehensive analysis of the potential failures and their causes. In the first step, a Multi-Class Multi-Output (MCMO) classification technique is used to diagnose potential failures based on sensor signals, and, in the second step, Failure Analysis (FA) is applied to investigate the root causes of those failures. The proposed approach is applied to a multi-component Hydraulic System (HS) case study, showing the resulting effectiveness in improving system reliability, reducing downtime, and minimizing the impact of failures on system operations. The results show that MCMO classification is a promising approach for multi-component system failure diagnosis that offers several advantages over conventional methods.

Keywords: Reliability, Failure analysis, Failure diagnosis, Machine learning, Classification, Industrial systems, and Hydraulic systems.

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