Archive for April 1st, 2005

Preventing Failures by Mining Maintenance Logs with Case-Based Reasoning

The project integrates work in natural language processing, machine learning, and the semantic web, bringing together these diverse disciplines in a novel way to address a real problem. The objective is to extract and categorize machine components and subsystems and their associated failures using a novel approach that combines text analysis, unsupervised text clustering, and domain models. Through industrial partnerships, this project will demonstrate effectiveness of the proposed approach with actual industry data.

Read the paper:

Preventing Failures by Mining Maintenance Logs with Case-Based Reasoning

by Mark Devaney, Ashwin Ram, Hai Qui, Jay Lee

59th Meeting of the Society for Machinery Failure Prevention Technology (MFPT-59), Virginia Beach, VA, April 2005