Online Anomaly Explanation: A Case Study on Predictive Maintenance

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

Autores

Rita Ribeiro (Autor) FCUP; Mastelini, SM (Autor) Outra; Davari, N (Autor) Outra; Aminian, E (Autor) Outra; Veloso, B (Autor) FEP; 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: 383-399

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-T4N

DOI: 10.1007/978-3-031-23633-4_25

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 17