A Primer on Vibrational Ball Bearing Feature Generation for Prognostics and Diagnostics Algorithms

Abstract

This report is the result of a prognostic and diagnostic program involving roller bearings. The objective of the effort was to develop techniques that could be used to detect the initial fault and predict the remaining useful life of a roller bearing. There are many techniques from digital signal processing, statistical, and machine learning fields that can be for fault detection and prediction. In this report, a description of roller bearing faults and life are presented. From this starting point, the report leads into various techniques that can be applied to vibrational data in order to generate features that can be used for fault detection. Feature generation is an important step in the prognostic and diagnostic development. This overview of possible features is intended to provide sufficient information to pursue feature selection and algorithm development for roller bearings prognostic and diagnostic techniques.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2015
Accession Number
ADA614145

Entities

People

  • Kwok F. Tom

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Ball Bearings
  • Bearings
  • Data Acquisition
  • Data Mining
  • Data Science
  • Detection
  • Digital Signal Processing
  • Failure Mode And Effect Analysis
  • Information Science
  • Machine Learning
  • Pattern Recognition
  • Roller Bearings
  • Signal Processing
  • Statistical Analysis
  • Three Dimensional
  • Two Dimensional

Fields of Study

  • Engineering

Readers

  • Computer Science.
  • Fault Tolerant Diagnosis of Black and White Balloon Isolation Tests Using ¥.
  • Tribology (the study of the boundary interaction between sliding surfaces, lubrication, wear and friction).

Technology Areas

  • AI & ML