Tracking and visualizing signs of degradation for early failure prediction of rolling bearings

Research output: Contribution to journalArticlepeer-review

Abstract

Predictive maintenance, which means detection of failure ahead of time, is one of the pillars of Industry 4.0. An effective method for this technique is to track early signs of degradation before failure occurs. This paper presents an innovative failure predictive scheme for machines. The proposed scheme combines the use of the full spectrum of vibration data from the machines and a data visualization technology. This scheme requires no training data and can be started quickly after installation. First, we proposed to use the full spectrum (as high-dimensional data vectors) with no cropping and no complex feature extraction and to visualize the data behavior by mapping the high-dimensional vectors into a two-dimensional (2D) map. This ensures simplicity of the process and less possi-bility of overlooking important information as well as provide a human-friendly and human-understandable output. Second, we developed a real-time data tracker that can predict failure at an appropriate time with sufficient allowance for maintenance by plotting real-time frequency spectrum data of the target machine on a 2D map created from normal data. Finally, we verified our proposal using vibration data of bearings from real-world test-to-failure measurements obtained from the IMS dataset.

Original languageEnglish
Pages (from-to)629-642
Number of pages14
JournalJournal of Robotics and Mechatronics
Volume33
Issue number3
DOIs
Publication statusPublished - 2021

Keywords

  • Data visualization
  • Early signs of degra-dation
  • Full spectrum
  • Predictive maintenance
  • Real-time data tracker

ASJC Scopus subject areas

  • Computer Science(all)
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Tracking and visualizing signs of degradation for early failure prediction of rolling bearings'. Together they form a unique fingerprint.

Cite this