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An Automated Machine Learning Approach for Real-Time Fault Detection and Diagnosis

Leite, Denis ; Martins, Aldonso ; Rativa, Diego ; De Oliveira, Joao F. L. ; Maciel, Alexandre M. A.

Sensors (Basel, Switzerland), 2022-08, Vol.22 (16), p.6138 [Periódico revisado por pares]

Basel: MDPI AG

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Título:
An Automated Machine Learning Approach for Real-Time Fault Detection and Diagnosis
Autor: Leite, Denis ; Martins, Aldonso ; Rativa, Diego ; De Oliveira, Joao F. L. ; Maciel, Alexandre M. A.
Assuntos: Analysis ; Automation ; Case studies ; discrete-event systems ; Fault detection ; Fault diagnosis ; intelligent manufacturing systems ; Machine learning ; Machinery ; Magneto-electric machines ; Manufacturing ; Mechanization ; Real time ; Sensors ; Simulation methods ; smart manufacturing ; Time series
É parte de: Sensors (Basel, Switzerland), 2022-08, Vol.22 (16), p.6138
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Descrição: This work presents a novel Automated Machine Learning (AutoML) approach for Real-Time Fault Detection and Diagnosis (RT-FDD). The approach’s particular characteristics are: it uses only data that are commonly available in industrial automation systems; it automates all ML processes without human intervention; a non-ML expert can deploy it; and it considers the behavior of cyclic sequential machines, combining discrete timed events and continuous variables as features. The capacity for fault detection is analyzed in two case studies, using data from a 3D machine simulation system with faulty and non-faulty conditions. The enhancement of the RT-FDD performance when the proposed approach is applied is proved with the Feature Importance, Confusion Matrix, and F1 Score analysis, reaching mean values of 85% and 100% in each case study. Finally, considering that faults are rare events, the sensitivity of the models to the number of faulty samples is analyzed.
Editor: Basel: MDPI AG
Idioma: Inglês

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An Automated Machine Learning Approach for Real-Time Fault Detection and Diagnosis

Leite, Denis ; Martins, Aldonso ; Rativa, Diego ; De Oliveira, Joao F. L. ; Maciel, Alexandre M. A.

Basel: MDPI AG

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