Online Anomaly Explanation: A Case Study on Predictive Maintenance

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

Authors

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

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 - 2 Citations

Scopus - 3 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 17