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

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

Davari, N (Author) Other; Veloso, B (Author) FEP; Rita Ribeiro (Author) FCUP; João Gama (Author) FEP

Conference proceedings International

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

Pages: 400-409

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

Grenoble, FRANCE, SEP 19-23, 2022

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 2 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10