Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Using Text Visualization to aid Analysis of Machine Maintenance Logs

Published

Author(s)

Michael P. Brundage, Senthil K. Chandrasegaran, Xiaoyu Zhang, Kwan-Liu Ma

Abstract

Maintenance and error logs for machines in manufacturing organizations are typically written as informal notes by operators or technicians working on the machines. These logs are written using a combination of common language and internally-used abbreviations and jargon. Due to inconsistencies in the terminology used during error logging and in identifying root causes of issues, the data needs to be cleaned before automated analyses can be effectively used. This can require a human to go through and clean/tag the data, disambiguate multiple terms, and sometimes assign additional tags to the data objects to aid automated classification. With some organizations storing over a million records of legacy maintenance report data, this is not entirely feasible. We introduce a visual analytic approach to help analysts sift through such heterogeneous datasets so that the inconsistent data can be tagged and categorized with minimum manual effort. Though such data typically includes metadata such as date, time, severity, machine IDs, etc., in this paper we focus on the manually-entered text descriptions. We use metrics such as word occurrence frequency and information-theoretic metrics to visually highlight common and uncommon issues and fixes that occur in the maintenance logs. We illustrate our approach with data from industry and discuss future research directions to address scalability, metadata, and other approaches for grouping similar logs.
Proceedings Title
Proceedings of the 11th Model-Based Enterprise Summit
Conference Dates
March 30-April 3, 2020
Conference Location
Gaithersburg, MD
Conference Title
Model-Based Enterprise Summit

Keywords

maintenance, manufacturing, Natural Language Processing, visulization

Citation

Brundage, M. , Chandrasegaran, S. , Zhang, X. and Ma, K. (2020), Using Text Visualization to aid Analysis of Machine Maintenance Logs, Proceedings of the 11th Model-Based Enterprise Summit, Gaithersburg, MD, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=930322 (Accessed June 9, 2023)
Created April 30, 2020, Updated May 15, 2020