Fault Forecasting Using Data-Driven Modeling: A Case Study for Metro do Porto Data Set

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2023

Autores

Davari, N (Autor) Outra; Veloso, B (Autor) FEP; Rita Ribeiro (Autor) FCUP; João Gama (Autor) FEP

Ata de Conferência Internacional

Título: MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT II

Páginas: 400-409

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

Grenoble, FRANCE, SEP 19-23, 2022

Indexação

ISI Web of Knowledge - 2 Citações

Scopus - 3 Citações

Outras Informações

ID Authenticus: P-00X-T4P

DOI: 10.1007/978-3-031-23633-4_26

Abstract (EN): The demand for high-performance solutions for anomaly detection and forecasting fault events is increasing in the industrial area. The detection and forecasting faults from time-series data are one critical mission in the Internet of Things (IoT) data mining. The classical fault detection approaches based on physical modelling are limited to some measurable output variables. Accurate physical modelling of vehicle dynamics requires substantial prior information about the system. On the other hand, data-driven modelling techniques accurately represent the system's dynamic from data collection. Experimental results on large-scale data sets from Metro do Porto subsystems verify that our method performs high-quality fault detection and forecasting solutions. Also, health indicator obtained from the principal component analysis of the forecasting solution is applied to predict the remaining useful life.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 10