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Detalhes

Detalhes

  • Nome

    Rita Paula Ribeiro
  • Cluster

    Informática
  • Cargo

    Investigador Sénior
  • Desde

    01 janeiro 2008
006
Publicações

2023

Online Anomaly Explanation: A Case Study on Predictive Maintenance

Autores
Ribeiro, RP; Mastelini, SM; Davari, N; Aminian, E; Veloso, B; Gama, J;

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

Abstract
Predictive Maintenance applications are increasingly complex, with interactions between many components. Black-box models are popular approaches due to their predictive accuracy and are based on deep-learning techniques. This paper presents an architecture that uses an online rule learning algorithm to explain when the black-box model predicts rare events. The system can present global explanations that model the black-box model and local explanations that describe why the black-box model predicts a failure. We evaluate the proposed system using four real-world public transport data sets, presenting illustrative examples of explanations.

2023

An Online Data-Driven Predictive Maintenance Approach for Railway Switches

Autores
Tome, ES; Ribeiro, RP; Veloso, B; Gama, J;

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

Abstract
An online data-driven predictive maintenance approach for railway switches using data logs obtained from the interlocking system of the railway infrastructure is proposed in this paper. The proposed approach is detailed described and consists of a two-phase process: anomaly detection and remaining useful life prediction. The approach is applied to and validated in a real case study, the Metro do Porto, from which seven months of data is available. The approach has been revealed to be satisfactory in detecting anomalies. The results open the possibilities for further studies and validation with a more extensive dataset on the remaining useful life prediction.

2023

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

Autores
Davari, N; Veloso, B; Ribeiro, RP; Gama, J;

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

Abstract
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.

2023

Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part I

Autores
Koprinska, I; Mignone, P; Guidotti, R; Jaroszewicz, S; Fröning, H; Gullo, F; Ferreira, PM; Roqueiro, D; Ceddia, G; Nowaczyk, S; Gama, J; Ribeiro, RP; Gavaldà, R; Masciari, E; Ras, ZW; Ritacco, E; Naretto, F; Theissler, A; Biecek, P; Verbeke, W; Schiele, G; Pernkopf, F; Blott, M; Bordino, I; Danesi, IL; Ponti, G; Severini, L; Appice, A; Andresini, G; Medeiros, I; Graça, G; Cooper, LAD; Ghazaleh, N; Richiardi, J; Miranda, DS; Sechidis, K; Canakoglu, A; Pidò, S; Pinoli, P; Bifet, A; Pashami, S;

Publicação
PKDD/ECML Workshops (1)

Abstract

2023

Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part II

Autores
Koprinska, I; Mignone, P; Guidotti, R; Jaroszewicz, S; Fröning, H; Gullo, F; Ferreira, PM; Roqueiro, D; Ceddia, G; Nowaczyk, S; Gama, J; Ribeiro, RP; Gavaldà, R; Masciari, E; Ras, ZW; Ritacco, E; Naretto, F; Theissler, A; Biecek, P; Verbeke, W; Schiele, G; Pernkopf, F; Blott, M; Bordino, I; Danesi, IL; Ponti, G; Severini, L; Appice, A; Andresini, G; Medeiros, I; Graça, G; Cooper, LAD; Ghazaleh, N; Richiardi, J; Miranda, DS; Sechidis, K; Canakoglu, A; Pidò, S; Pinoli, P; Bifet, A; Pashami, S;

Publicação
PKDD/ECML Workshops (2)

Abstract

Teses
supervisionadas

2022

Towards a national data cube of satellite earth observation data for ecological modeling and monitoring

Autor
Nuno Filipe Escaleira de Sousa

Instituição
UP-FCUP

2022

Boosting the Prediction of Extreme Values

Autor
Aníbal José Ferreira e Silva

Instituição
UP-FCUP

2022

Data-driven Predictive Maintenance for Industry 4.0

Autor
Emanuel Rodrigues de Sousa Tomé

Instituição
UP-FCUP

2022

Spatio-temporal clustering to study vehicle emissions and air quality correlation at Porto

Autor
Nirbhaya Shaji

Instituição
UP-FCUP

2022

Feature Selection in Imbalance Domain Learning Problems: A Case Study on Scrapping of Tires

Autor
Pedro Manuel Moreira Unas

Instituição
UP-FCUP