Online Anomaly Explanation: A Case Study on Predictive Maintenance

Author(s):Show less -

Ribeiro, Rita P. ; Mastelini, Saulo Martiello ; Davari, Narjes ; Aminian, Ehsan ; Veloso, Bruno ; Gama, Joao ; Koprinska, I ; Mignone, P ; Guidotti, R ; Jaroszewicz, S ; Froning, H ; Gullo, F ; Ferreira, PM ; Roqueiro, D ; Ceddia, G ; Nowaczyk, S ; Gama, J ; Ribeiro, R ; Gavalda, R ; Masciari, E ; Ras, Z ; 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 ; Graca, G ; Cooper, L ; Ghazaleh, N ; Richiardi, J ; Saldana, D ; Sechidis, K ; Canakoglu, A ; Pido, S ; Pinoli, P ; Bifet, A ; Pashami, S

• Document type: Journal article
• Source: MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT II; v. 1753, p. 17-pg., 2023-01-01.

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. (AU)

• FAPESP's process: 21/10488-7 - Online nearest neighbor search
• Grantee: Saulo Martiello Mastelini
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate