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Publicações

Publicações por Rita Paula Ribeiro

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

2023

Study on Correlation Between Vehicle Emissions and Air Quality in Porto

Autores
Shaji, N; Andrade, T; Ribeiro, RP; Gama, J;

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

Abstract
Road transportation emissions have increased in the last few decades and have been the primary source of pollutants in urban areas with ever-growing populations. In this context, it is important to have effective measures to monitor road emissions in regions. Creating an emission inventory over a region that can map the road emission based on the vehicle trips can be helpful for this. In this work, we show that it is possible to use raw GPS data to measure levels of pollution in a region. By transforming the data using feature engineering and calculating the vehicle-specific power (VSP), we show the areas with higher emissions levels made by a fleet of taxis in Porto, Portugal. The Uber H3 grid system is used to decompose the city into hexagonal grids to sample nearby data points into a region. We validate our experiments on real-world sensor datasets deployed in several city regions, showing the correlation with VSP and true values for several pollutants attesting to the method's usefulness.

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